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Volume 14 Issue 1
Jan.  2025
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Article Contents
Abstract
1. Introduction
2. Measurement Method for Sea Clutter with Shore-based Radar
3. Experiment Radar, Sites, and Data Acquisition
4. Typical Data Presentation and Analysis
5. Conclusions
Appendix
References
Article Navigation >  Journal of Radars  > 2025  >  14(1): 168-188
HUANG Shuaiming, ZHU Xiaohua, WANG Wubin, et al. U-Sodar: Noncontact vital sign detection technology based on ultrasonic radar[J]. Journal of Radars, 2025, 14(1): 168–188. doi: 10.12000/JR24114
Citation: HUANG Shuaiming, ZHU Xiaohua, WANG Wubin, et al. U-Sodar: Noncontact vital sign detection technology based on ultrasonic radar[J]. Journal of Radars, 2025, 14(1): 168–188. doi: 10.12000/JR24114
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U-Sodar: Noncontact Vital Sign Detection Technology Based on Ultrasonic Radar

DOI: 10.12000/JR24114 CSTR: 32380.14.JR24114
  • 1.

    Nanjing University of Science and Technology, Nanjing 210000, China

  • 2.

    North Institute of Automatic Control Technology, Taiyuan 030006, China

Funds:  The National Natural Science Foundation of China (62431013, 62301255, 62201259), Natural Science Foundation of Jiangsu Province (BK20220942, BK20220940), Fundamental Research Funds for the Central Universities (30923011026, 30923011006)
More Information
  • Corresponding author: ZHAO Heng, soniczhao@live.com; HONG Hong, hongnju@njust.edu.cn
  • Received Date: 2024-06-05
  • Rev Recd Date: 2024-09-22
    • Available Online: 2024-09-29
  • Publish Date: 2024-10-18
    Abstract
  • Amidst the global aging trend and a growing emphasis on healthy living, there is an increased demand for unobtrusive home health monitoring systems. However, the current mainstream detection methods in this regard suffer from low privacy trust, poor electromagnetic compatibility, and high manufacturing costs. To address these challenges, this paper introduces a noncontact vital signal collection device using Ultrasonic radar (U-Sodar), including a set of hardware based on a three-transmitter four-receiver Multiple Input Multiple Output (MIMO) architecture and a set of signal processing algorithms. The U-Sodar local oscillator uses frequency division technology with low phase noise and high detection accuracy; the receiver employs front-end direct sampling technology to simplify the involved structure and effectively reduce external noise, and the transmitter uses an adjustable PWM direct drive to emit various ultrasonic waveforms, possessing software-defined ultrasonic system characteristics. The signal processing algorithm of U-Sodar adopts the graph processing technique of signal chord length and realizes accurate recovery of signal phase under 5 dB Signal-to-Noise Ratio (SNR) using picture filtering and then reconstruction. Experimental tests on the U-Sodar system demonstrated its anti-interference and penetration capabilities, proving that ultrasonic penetration relies on material porosity rather than intermedium vibration conduction. The minimum measurable displacement for a given SNR with correct demodulation probability is also derived. The results of actual human vital sign signal measurement experiments indicate that U-Sodar can accurately measure respiration and heartbeat at 3.0 m and 1.5 m, respectively, and the heartbeat waveforms can be measured within 1.0 m. Overall, the experimental results demonstrate the feasibility and application potential of U-Sodar in noncontact vital sign detection.

     

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  • When detecting military and civil targets, such as ships, skimming aircraft, fairway buoys, fishing vessels, small-scale yachts, and floating ice in a complex marine environment, sea-detecting radars are inevitably influenced by surface scattering echo, i.e., sea clutter. Particularly under high-resolution radar and high sea conditions, spike phenomenon, which occurs frequently in sea clutter and has strong overall energy, is extremely similar to target echo in time domain. However, the radar shows a greater spectral width in frequency domain, which easily results in false alarm and seriously influences sea target detection. Thus, studying the characteristics of sea clutter based on actual application scenario and real data, developing sea clutter suppression and target detection, and improving sea-detecting radar capacity have become bottlenecks with strong exploration and difficulty and crucial issues in current research[1–3].

    The research methods for sea clutter characteristics can be classified into two types. One is research on the formation mechanism of sea clutter and scattering calculation based on geometrical model of sea surface and electromagnetic scattering theory. The other is acquiring real data on surface scattering echo and modifying existing theory or developing a new theory from an experimental perspective. The research method from an experimental view is widely used and closely resembles the real environment, thereby mutually supplementing and demonstrating the research method from the perspective of theoretical calculation. The characteristics of real experimental data are closely related to radar equipment. As a result, the research method based on real measurement of experiment can combine universal research results of theoretical calculation with specific application scenarios of special equipment to a certain extent. Besides, this research method implements optimization and improvement, advances the application of characteristic research results of sea clutter, and strongly supports research on target detection and clutter suppression such as MTI/MTD, transform domain disposal, tracking before detection, and constant false alarm rate[4,5]. Called “information fully recorded sea clutter measurement data,” real experimental data include not only sea clutter data and target echo data but also marine environment data (e.g., wave height, wave direction, wind speed, wind direction, temperature, and relative humidity), real-time location of target (e.g., size or RCS, longitude and latitude or range orientation, velocity, direction of real-time motion state, and motion track), radar operating parameters (e.g., radar location, height, frequency band, pulse repetition frequency, and distance/orientation resolution). Such information normalizes detailed records and correlates to radar data to repeat the experimental scenario corresponding to data and ensure high application values of such data.

    According to the literature, various sea clutter measurement experiments with radar have been made at China and abroad to support the development of sea-detecting radar equipment and improve the capacity of sea target detection[6]. Typical foreign experiments include four-frequency (P, L, C, X) sea clutter measurement with airborne radar made by the United States Navy Research Laboratory[7], “Peak Program” of the US Navy and Defense Advanced Research Projects Agency[8], sea clutter measurement experiment with X-band IPIX radar conducted by McMaster University in Canada[9,10], sea clutter measurement experiment with X-band Fynmeet radar conducted by the Council for Scientific and Industrial Research (CSIR)[11,12], sea clutter measurement experiment with Ka-band radar on the South Coast of Spain[13], and L-band multichannel sea clutter experiment conducted by the Defense Science and Technology Organization[14-16]. In China, several radar-related scientific research institutions have also conducted sea clutter measurement experiments under different conditions[6,17–30], acquired massive data under various radar platforms, and developed many studies on characteristic analysis and modeling and multi-domain feature extraction[31–41]. However, because of military and technical confidentiality, most sea clutter datasets measured with radar have not been disclosed, and only the datasets acquired from the sea clutter measurement experiment with X-band IPIX radar conducted by McMaster University and with X-band Fynmeet radar of South Africa’s CSIR are available.

    The IPIX radar dataset is well known in the research field of sea target detection, being typical radar data under small grazing angles of shore-based platform. Under the leadership of Prof. Simon Haykin of McMaster University, a research team[9,10] conducted sea clutter measurement and small-sea floating target detection experiments in Dartmouth, south of Nova Scotia, and in Grimsby, Ontario in 1993 and 1998, respectively. The Fynmeet radar dataset was acquired by CSIR to support a small sea-target monitoring system. CSIR conducted sea clutter and echo data measurement experiments twice for target vessels over 19 days on the southwest coastline of South Africa in 2006 and 2007, respectively. Such a database is complete in a variety of parameters and records[11,12]. These two publicly available datasets focus on specific targets and sea areas for experiments, thereby having certain limitations. These datasets can be used to evaluate the performance of clutter suppression and target detection methods, but may be inaccurate in performance evaluation for the Chinese sea area and specific target detection. Furthermore, because of indefinite reasons, CSIR datasets can no longer be downloaded from the official website.

    Considering research demands for sea clutter characteristics and sea target detection technology, and learning from successful experiences in data acquisition and records of McMaster University for the IPIX radar datasets and of South Africa’s CSIR for the Fynmeet radar dataset, the research group working on sea target detection at Naval Aeronautical University have proposed a “sea-detecting radar data-sharing program.” This program aims to develop a sea-detection experiment with X-band full solid-phase coherent radar in stages and batches, acquire real radar data and experimental supplementary data under different conditions, as well as build and form datasets used to support recognition for sea clutter characteristics, sea clutter suppression, sea target detection, tracking, and classification. These datasets are disclosed and shared in batches to contribute toward advancing the quality sea-detecting radar equipment and improving detection performance.

    Using shore-based X-band full solid-phase coherent radar, this experiment acquires surface electromagnetic scattering echo and measure surface scattering coefficient[42–45], which are used to study rules of sea clutter change with resolution, incidence direction and scattering direction, and environmental characteristics of sea surface. This experiment also investigates statistical features and spectral characteristics of sea clutter. Note that the radar’s observation objects include not only sea surface (sea clutter) but also sea-surface targets such as channel buoys and ships. In other words, acquired real radar data include pure sea clutter data and sea clutter as well as target echo data.

    By measuring and recording echo power on the sea surface, we can build a corresponding relationship between the radar scattering coefficient of the measured sea surface and the voltage value of the radar video based on calibration. Such a relationship can be used in real measurements to determine the radar scattering coefficient of the measured sea surface from video voltage value. As a result, the data of this experiment can be compared with other experimental data. The external calibration method is mainly used in this experiment, i.e., to provide a calibration level with a calibration object under a given radar cross section. The calibration method is expressed as follows[42]:

    σ0=σA=PrPr0⋅(RrR0)4⋅σ0A=VrVr0⋅(RrR0)4⋅σ0A (1)

    where σ0 refers to the radar cross section of the measured sea surface (unit: dBm2/m2); σ refers to the radar cross section shown when the radar antenna beam irradiates the sea surface (unit: m2); A refers to the irradiation area on the sea surface made from the radar antenna beam (unit: m2); Pr refers to the echo power of measured sea surface (unit: W); Pr0 refers to echo power of calibration object (unit: W); Rr refers to the distance from the measured sea surface to antenna (unit: m); R0 refers to the distance between the calibration object and antenna (unit: m); σ0 is the radar cross section of the calibration object (unit: m2); Vr refers to the echo voltage of measured sea surface (unit: V); and Vr0 refers to the echo voltage of the calibration object (unit: V).

    The calibration accuracy mainly depends on the radar cross section of the calibration object and relative size of the surface scattering cross section. The calibration error can be expressed as follows:

    σm−σ0σm=σbσ0±2√σbσ0 (2)

    where σm refers to the measured radar cross section of the calibration object (unit: m2); σ0 refers to the real radar cross section of the calibration object (unit: m2); and σb refers to the radar cross section of the measured sea surface (unit: m2). When error is limited within ±20% (equivalent to ±1 dB), σb/σ0≤10−2 (equivalent to –20 dB) is controlled.

    In the calibration process, a rectangle panel, a corner reflector, a Luneburg lens reflector, and a metal ball are typically used as calibration objects. The scattering cross sections of these standard objects can be obtained through theoretical calculation (Ref. [42] provides details). This experiment proposes to use a metal ball as a calibration object (see Fig. 1). The calibration object is placed at sea surface and higher than sea level to a certain extent as a whole. Besides, the object is not submerged. The position of the calibration object is accurately calculated based on erection location of the radar and orientation of the wave beam. Stable calibration results can be obtained by averaging measurements acquired multiple times.

    Figure  1.  Stainless steel ball calibration body
    DownLoad: Full-Size Img PowerPoint

    In fact, for research demands of real sea clutter, such as characteristics of statistical distribution, correlation characteristics, Doppler spectrum characteristics, clutter suppression, and target detection, we pay close attention to the relative amplitude of radar echo. If differences exist in surface scattering coefficient and different bands and radars, then the radar will be calibrated.

    3.1 Experiment radar

    The X-band solid power amplifier surveillance/navigation radar used in this experiment is mainly applicable to scenarios such as shipping navigation and shore surveillance. Characterized by high-range resolution, high reliability, and small distance from dead zone of detection, the radar clearly distinguishes various targets under different ranges (see Fig. 2). Such a radar utilizes a solid power-amplifier combined impulse transmission mechanism (see Fig. 3) to improve range resolution, decline dead zones of range, and increase radiation power. The launch time is 40 ns to 100 µs. The target distance is calculated based on the difference of time between receiving signals and launching signals. Furthermore, the radar scans completely through 360° on a horizontal plane (see Tab. 1 for technical parameters of the radar).

    Table  1.  X-band experimental radar parameters
    Parameters Technical indexes
    Operating frequency X
    Scope of operating frequency 9.3-9.5 GHz
    Range 0.0625-96 nm
    Scanning bandwidth 25 MHz
    Range resolution 6 m
    Pulse repetition frequency 1.6 K, 3 K, 5 K & 10 K
    Emission peak power 50 W
    Revolving speed of antenna 2rpm, 12 rpm, 24 rpm, 48 rpm
    Length of antenna 1.8 m
    Operating mode of antenna Gazing & circular scanning
    Polarization mode of antenna HH
    Width of horizontal beam of antenna 1.2°
    Width of vertical beam of antenna 22°
     | Show Table
    DownLoad: CSV
    Figure  2.  X-band solid-state power amplifier surveillance/ navigation radar
    DownLoad: Full-Size Img PowerPoint
    Figure  3.  Three modes of combined pulse emission
    DownLoad: Full-Size Img PowerPoint
    1.  Summary table of sea-detecting radar data (the 1st phase)
    No. Grade of sea conditions Data size Working mode of radar antenna Transmission pulse mode Regional AIS data Meteorological and
    hydrological data     		Grazing angle (°)
    1 Grade 1 and below >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
    2 Grade 2 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
    3 Grade 3 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
    4 Grade 4 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
    5 Grade 5 >5 Groups Staring & scanning Mode 2 No Yes 0.3–15
    Notes: ① Daily AIS data will be generated into a table format, which provides MMSI of ships, time, longitude and latitude, and others. ② Daily meteorological and hydrological data will be generated into an NC-format file, which indicates wind speed, wind direction, wave height, and cycle. ③ The number of pulses contained in a group of data under staring mode will not be less than 105, while a group of data under scanning mode contains a complete scanning cycle. ④ Under transmission pulse mode 2, radars of each corresponding repetition cycle will transmit a single-carrier frequency pulse and an LFM pulse.
     | Show Table
    DownLoad: CSV

    3.2 Experiment sites

    The “sea-detecting radar data-sharing program” proposes a series of sea-detecting experiments, including shore-based and airborne platform experiments, which are carried out within one to two years.

    The sea target detection site located in the coastal zone of Yantai, Shandong Province is selected as the shore-based experiment site. Three experiment sites with different altitudes are covered because of geographical conditions for observing the same sea area, various features, and mutual complementation.

    Experiment site 1: Located on Yangma Island in Yantai, the experiment site is approximately 50 m far from the seaside, at an altitude approximately 30 m, and approximately 180° sea-detecting range of radar. According to measurements, the scope of grazing angles is approximately 0.3° to 15°. Various sea targets exist, which are mainly small and medium-sized ships (see Fig. 4 for details).

    Figure  4.  Aerial view of experiment site 1 for sea detection
    DownLoad: Full-Size Img PowerPoint

    Experiment site 2: Located on the seaward side of a small mountain in Muping district, Yantai, the experiment side is approximately 500 m far from the seaside, has an altitude of 60–120 m, and has approximately 180° sea-detecting range of radar. According to measurements, the scope of grazing angles shown in significant sea clutter data is approximately 0.3°–6°. Offshore small fishing boats are mainly sea targets and a few other sea targets exist at a remote distance. Thus, this site is feasible for conducting the target experiment (see Fig. 5 for details).

    Figure  5.  Aerial view of experiment site 2 for sea detection
    DownLoad: Full-Size Img PowerPoint

    Experiment site 3: Located on Daiwang mountain in the Zhibu district of Yantai, this experiment site is approximately 2200 m far from the seaside and at an altitude approximately 400 m and sea-detecting range of radar larger than 180°. According to measurements, the scope of grazing angles shown in the significant sea clutter data is approximately 1.2° to 7°. Various sea targets are mainly large, small, and medium-sized ships, which show a wide horizon (see Fig. 6 for details).

    Figure  6.  Aerial view of experiment site 3 for sea detection
    DownLoad: Full-Size Img PowerPoint

    The four-fin Bell 407GXi helicopter (see Fig. 7), which carries the experiment radar and is allowed to fly over the Yantai airspace, is mainly used in airborne platform experiment. For the sea target detection experiment, the helicopter is designed with a flight height from 500 m to 4000 m, loading capacity of up to 300 kg, single flight time of less than 2 h, maximum range of 675 km, and maximum cruising speed of 250 km/h.

    Figure  7.  Helicopter experimental platform
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    Phase 1 experiment was conducted at seaside experiment site 1 from September to October 2019 to collect radar echo data, including sea clutter data and target echo data of ships, under different conditions. Phases 2 and 3 experiments will consider experiment sites 2 and 3 and an airborne experiment platform with high altitude. As the shore-based/shipborne radar used in the phase 1 experiment cannot be installed in an airborne platform, the airborne experiment has not been carried out in phase 1, but will be conducted later.

    3.3 Radar data acquisition

    Model HD-LD-CJ-10 portable radar acquisition equipment developed by the research group has been used for data acquisition (see Fig. 8). The equipment is constituted by a 2U portable reinforced IPC, acquisition board card, and upper computer software. Designed with 105 MSPS peak sampling capacity and 80 MB/S continuous storage capacity, and designed with 3-way TTL level signal, 2-receiving and 2-sending RS232 signals, and 4-receiving RS422 signal ports, the acquisition board will perform 3-way quantification and can be used to access data of auxiliary equipment. The upper computer software can realize user-defined port gate sampling, automatically separate binary data files based on the size of the preset file, and record automatic identification system (AIS), radar output point/flight path, and other data through serial and network interfaces.

    Figure  8.  HD-LD-CJ-10 portable acquisition equipment
    DownLoad: Full-Size Img PowerPoint

    When radar signals are collected, port gate sampling will be conducted based on triggering signals. The designation format for the data acquisition file is 20191008085830_staring/scanning (y/m/d/h/min/s/antenna working mode) and the working mode of the antenna includes staring (at a certain direction) and scanning (in a circular manner). Fig. 9 presents the basic format of collected data. Each data file is constituted by multiple pulse combined echo data ranked in order. Different working modes of radar correspond to different pulse combinations, and each pulse combination includes one to three transmission pulses. The echo data of each pulse combination is constituted by “data head plus echo data.” The data head includes zone bit, length of information head, data version, pulse repetition period, frequency and counting, sampling frequency, data source and triggering mode, orientation code, sign of data loss, start time and corresponding sampling depth of port gate, UTC time, radar position, true north angle of radar, working mode of radar and data check bit, and others. The length of echo data can be calculated based on the size of the port gate and sampling depth. Besides, the sampling number of each pulse echo in one pulse combination is defined (see Appendix for details of the real data).

    Figure  9.  Format of radar acquisition data
    DownLoad: Full-Size Img PowerPoint

    Regarding the process of sea-detecting radar experiment and data acquisition, the radar is erected at a predetermined experiment side. Before each experiment, the preliminary level of sea state and staring angle at the antenna are determined based on the wind speed, wind direction, wave height, wave direction, weather forecast, and real-time information. The radar starts up and works under antenna parking mode and circular scanning mode, respectively. Furthermore, the data acquisition unit will be enabled to record data and synchronize data of AIS equipment. The data acquisition lasts over 2 min under parking mode and over 5 min under circular scanning mode. When the requirements are satisfied, the data acquisition is stopped, and the list of records for data acquisition and record information are prepared, as indicated in the experiment process. This process is repeated over five times to ensure the acquisition of 10 groups of real data from each experiment. These data are validated by being replayed and analyzed onsite by acquisition equipment.

    MAT data format in Matlab is used for data disclosed in the phase 1 experiment of the “sea-detecting radar data-sharing program.” The data head information and echo data can be loaded directly for future reference. In subsequent experiments, as the data type and size increase, international general data format (e.g., NetCDF, HDF5, and others) will be used to improve compatibility with various types of radar data. Furthermore, special radar data management and analysis software will be provided to ensure standard management for radar data.

    3.4 Auxiliary data of experiment

    One of the important elements of the sea clutter experiment with the radar is synchronous record of marine environmental data. On the one hand, generating the “information fully recorded sea clutter measurement data” will include basic information on the marine environment during the time that the radar data are replayed. On the other hand, this process will advance the refinement of technical research on characteristics of sea clutter, sea clutter suppression, and target detection. The range of spatial scale is relatively small because of the limited scope of the sea area for shore-based observation with the radar. Meanwhile, through marine meteorological station and satellite remote sensing, the marine environmental data (e.g., wave height and direction) will be predicted or modified in real time within a large spatial scale. For the definite sea area observed with the radar, corresponding marine environmental data will be provided generally through modeling and reanalysis. As a result, certain errors may occur. Of course, marine environmental data can also be measured through information buoy and such measurements will be more accurate. However, the buoy will only measure a certain point in the sea area observed with the radar, which cannot reflect the conditions of other zones of the entire sea area. Therefore, this experiment combines both of them (i.e. the reanalysis data and the measured data), while marine environmental data in the field of view of the radar will be provided mainly through reanalyzed marine environmental data. Furthermore, marine information buoys will be placed in certain points and time within the radar observation area to modify the marine environmental data. A combination of both will provide more accurate results to support the sea-detecting radar experiment.

    The marine environment information on wind and wave will be mainly recorded in the sea-detecting radar experiment. The wind elements mainly include wind scale, wind speed, and wind direction, while the wave elements mainly include wave height, wave direction, wave velocity, wave period, and temperature. In addition, the weather phenomena, temperature, and relative humidity during the experiment will also be recorded.

    With the background field of Climate Forecast System Reanalysis (CFSR), the data source of wind elements utilizes the scale mode in weather research and forecasting, and 3DVAR (3D VARiational) data assimilation technology to assimilate various conventional and non-conventional meteorological observation data for land, ocean, and upper air as well as ensure dynamic integration between various observation and reanalysis data. On this basis, data on the historical optimal path of typhoons sourced from the National Meteorological Center and weak-constraint variational method have been used to adjust dynamically integrated reanalysis wind field and further improve data accuracy. The reanalysis dataset of the sea surface wind field can cover the northwest Pacific Ocean (10°S–50°N, 95°E–150°E). The data of the sea area observed with the radar will be randomly selected based on the experiment position and longitude and latitude in each experiment to support the experiment data analysis. For the data of wind elements, the spatial resolution is 0.1° (longitude and latitude), the time resolution is not less than 1 h, and the wind speed error at grid point of longitude and latitude is within ±1.5 m/s.

    With the driving field of CFSR, the data source of wave elements, based on the third-generation numerical mode SWAN of the offshore wave optimized by the National Marine Environmental Forecasting Center and the optimal interpolation assimilation technology, has assimilated effective wave height data of satellite altimeter along sea waves. The data source also used the nesting method to build a wave reanalysis system and form a high-resolution wave reanalysis dataset covering the northwest Pacific Ocean (0°–50°N, 100°E–160°E). Similar to the data acquisition method for wind elements, the data of the radar observed sea area will be randomly selected based on the longitude and latitude of the experiment site in each experiment to support the experiment data analysis. For the data of wave elements, the special resolution is 0.1° (longitude and latitude), time resolution is not less than 1 h, and wave height error at the grid point of longitude and latitude is within 0.3 m (see Fig. 10(a)—Fig. 10(d) for samples). In Fig. 10(a) and Fig. 10(c), “BUOY WS/BUOY SWH” refers to one-site experiment data of wind speed/effective wave height acquired through sea-surface buoy, and “MODEL WS/MODEL SWH” refers to the reanalysis data of wind speed/effective wave height acquired through data at the grid point of longitude and latitude and data modeling.

    Figure  10.  Meteorological and hydrological data of experimental sea area
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    Based on the above data acquisition, the buoy for marine meteorology and hydrological information will also be used to modify the reanalysis data of wind and wave at specific positions during the experiment. The real-time observation for onsite wave and tide can be realized at a fixed position through such a buoy because of its high accuracy in measurement and simple erection and maintenance (see Fig. 11). The measurement parameters include wave height, wave period, wave direction, wind speed, wind direction, air temperature, air pressure, and position coordinates of the buoy.

    Figure  11.  Marine meteorological and hydrological information buoy
    DownLoad: Full-Size Img PowerPoint

    In addition to obtaining the aforementioned marine environmental information, the sea-detecting radar experiment will also synchronize real motion tracks of the sea targets. For large and medium-sized ships, the position, motion state, and dimensional information of targets can be acquired through message data of AIS. For small-sized ships, coordination experiments (additionally installing AIS equipment or GPS/Beidou positioning system) will be conducted to acquire real motion tracks. Furthermore, non-coordinated targets (e.g., small-sized fishing ships and yachts) at sea surface will be roughly marked (for position and time) through observations of radar display terminal and manual records. These targets, however, cannot provide accurate position information as real values.

    To describe data validity, in this section, a group of typical sea-surface measurement data will be selected to provide time domain and frequency domain processing results. The data file name is 20191008085830_staring.mat and data acquisition was conducted from 8:58:30 a.m. on October 8, 2019. When the data acquisition began, the radar antenna stared at a certain direction of the sea surface. The pulse transmission mode is Mode 2 shown in Fig. 3, i.e., each trigger of radar successively transmits two different pulses. The first pulse is a single-carrier frequency signal with pulse width of 40 ns, while the second pulse is an LFM signal with bandwidth 25 MHz, pulse repetition frequency of 3 kHz, and sampling rate at a distance direction of 60 MHz. This group of data will be used to measure sea clutter. No coordinated target occurs at sea surface and the effective wave height of the sea surface is 1.8–2.0 m, which is Grade 4 sea condition according to the Table of Grade of Sea Conditions[1,46]. Fig. 12(a) and Fig. 12(b) show 2D plane graphs of the time domain for single-carrier frequency pulse echo and LFM pulse echo. This group of data contains a total of 4,096 pulses and the data duration is approximately 1.36 s. The echo sampling number at distance direction is 1,436 for the first pulse and 6,678 for the second pulse. Fig. 12(a) and Fig. 12(b) only shows the data of the area with strong clutter instead of the data in all distance units. Fig. 12(c) and Fig. 12(d) present oscillograms of the echo sequence of three distance units. We can observe that clutter energy gradually declines as the distance increases. Fig. 12(e) and Fig. 12(f) show a 2D planar graph of distance and frequency for the single-carrier frequency pulse echo and LFM pulse echo, respectively. Fig. 12(g) and Fig. 12(h) respectively show the Doppler spectra of multiple distance units. We can observe positive values at the center of the Doppler spectrum, indicating the movement of sea waves toward the gazing direction of the radar. According to a 2D diagram of distance frequency for the single-carrier frequency pulse echo, the amplitude of the Doppler spectrum for clutter at certain sampling points is weak, which is related to the fluctuation of sea waves and also the single-carrier frequency pulse echo without distance dimension matched filtering after digital demodulation. If distance dimension matched filtering is conducted, then the Doppler spectrum inevitably widens at the distance dimension to cover all distance units. As a result, results become similar to a 2D diagram of the distance frequency for the LFM pulse echo.

    Figure  12.  Results of typical measurements
    DownLoad: Full-Size Img PowerPoint

    According to an analysis of multiple groups of data, at the radar erection height of approximately 80 m and under levels 3–4 marine conditions, the range of the significant sea clutter is measured at not less than 3 km when the radar transmits a single-carrier frequency pulse and not less than 8 km when the radar transmits an LFM pulse. Such a range declines under low sea conditions but increase under high sea conditions.

    Real radar data are required to support the development of sea target detection technology with radar. Owing to military and technical confidentiality, most sea clutter datasets measured with radar have not been disclosed. The earlier disclosed sea-detecting radar data, however, is difficult to acquire. Thus, this paper has proposed a “sea-detecting radar data-sharing program” that aims to conduct sea-detection experiments with X-band solid full-coherent radar and other types of radars; acquire targets and sea clutter data under different grades of sea conditions, resolutions, and grazing angles; synchronously acquire real marine meteorological and hydrological data, target positions, and tracks; and achieve standard management for real radar data. As a result, dataset sharing will be advanced to provide data support for tackling critical problems in the research field of sea-detecting radars.

    The real sea-detecting X-band radar data will be disclosed and shared depending on the official website of the Journal of Radars. Experiment data will be uploaded to the “Data/Sea-Detecting Radar Data” page (see Appendix Fig. 1). Please visit http://radars.ie.ac.cn/web/data/getData?dataType=DatasetofRadarDetectingSea for details. Furthermore, data will be regularly updated after the sea-detection experiment.

    1.  Release address of sea-detecting radar data
    DownLoad: Full-Size Img PowerPoint

    The phase 1 experiment primarily focuses on collecting sea clutter data and then detecting the data of sea-surface targets (non-coordinated targets). Owing to large data size, each data file is not described in this paper. An overview of each data type will be provided based on grade of sea conditions for future reference and selection by researchers.

    Each pulse echo in every group of data includes data head, which includes zone bit, length of information head, data version, pulse repetition period, frequency and counting, sampling frequency, data source and triggering mode, orientation code, sign of data loss, start time and corresponding sampling depth of port gate, UTC time, radar position, true north angle of radar, working mode of radar and data check bit, and others. When the data are loaded, each data point contained in the data head and corresponding meaning will be presented for future reference.

    • References(44)
  • [1]
    United Nations. Ageing[EB/OL]. https://www.un.org/en/global-issues/ageing, 2024.
    [2]
    民政部, 全国老龄办. 2022年度国家老龄事业发展公报[EB/OL]. https://www.gov.cn/lianbo/bumen/202312/P020231214405906944856.pdf, 2023.

    Ministry of Civil Affairs of China, National Office for Aging of China. 2022 national report on the development of aging services[EB/OL] https://www.gov.cn/lianbo/bumen/202312/P020231214405906944856.pdf, 2023.
    [3]
    PIRZADA P, WILDE A, and HARRIS-BIRTILL D. Remote photoplethysmography for heart rate and blood oxygenation measurement: A review[J]. IEEE Sensors Journal, 2024, 24(15): 23436–23453. doi: 10.1109/JSEN.2024.3405414.
    [4]
    WANG Wenjin, DEN BRINKER A C, STUIJK S, et al. Algorithmic principles of remote PPG[J]. IEEE Transactions on Biomedical Engineering, 2017, 64(7): 1479–1491. doi: 10.1109/TBME.2016.2609282.
    [5]
    KOBAYASHI L, CHUCK C C, KIM C K, et al. Comparison of video photoplethysmography, video motion analysis, and passive infrared thermography against traditional contact methods for acquiring vital signs in emergency department populations[C]. 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, USA, 2023: 134–141. doi: 10.1109/UEMCON59035.2023.10316088.
    [6]
    PEREIRA C B, YU Xinchi, BLAZEK V, et al. Robust remote monitoring of breathing function by using infrared thermography[C]. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 2015: 4250–4253. doi: 10.1109/EMBC.2015.7319333.
    [7]
    方震, 简璞, 张浩, 等. 基于FMCW雷达的非接触式医疗健康监测技术综述[J]. 雷达学报, 2022, 11(3): 499–516. doi: 10.12000/JR22019.

    FANG Zhen, JIAN Pu, ZHANG Hao, et al. Review of noncontact medical and health monitoring technologies based on FMCW radar[J]. Journal of Radars, 2022, 11(3): 499–516. doi: 10.12000/JR22019.
    [8]
    XIONG Junjun, HONG Hong, XIAO Lei, et al. Vital signs detection with difference beamforming and orthogonal projection filter based on SIMO-FMCW radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2023, 71(1): 83–92. doi: 10.1109/TMTT.2022.3181129.
    [9]
    MA Yuanren, ZHAO Heng, ZHUANG Zhongxu, et al. Sleep-disordered breathing detection based on radar-pulse oximeter sensor fusion[C]. 2023 Asia-Pacific Microwave Conference, Taipei, China, 2023: 260–262. doi: 10.1109/APMC57107.2023.10439668.
    [10]
    SUN Li, BAI Ge, LUO Changhao, et al. A large-scale movement path fitting based phase compensation algorithm for FMCW radar vital sign detection[C]. 2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks, San Antonio, USA, 2024: 37–40. doi: 10.1109/WiSNeT59910.2024.10438585.
    [11]
    工业和信息化部. 中华人民共和国无线电频率划分规定[EB/OL]. https://www.gov.cn/zhengce/zhengceku/2018-12/31/content_5439640.htm, 2023.

    Ministry of Industry and Information Technology of China. Provisions on the allocation of radio frequencies of the People’s Republic of China[EB/OL]. https://www.gov.cn/zhengce/zhengceku/2018-12/31/content_5439640.htm, 2023.
    [12]
    国际电信联盟. ITU-R SM.2093-4[EB/OL]. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-SM.2093-4-2021-PDF-C.pdf, 2021.

    ITU. ITU-R SM.2093-4[EB/OL]. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-SM.2093-4-2021-PDF-C.pdf, 2021.
    [13]
    国家药品监督管理局. YY 9706.102-2021 医用电气设备 第1-2部分: 基本安全和基本性能的通用要求 并列标准: 电磁兼容 要求和试验[S]. 北京: 中国标准出版社, 2021.

    National Medical Products Administration. YY 9706.102-2021 Medical electrical equipment—Part 1-2: General requirements for basic safety and essential performance—collateral standard: Electromagnetic compatibility—requirements and tests[S]. Beijing: Standards Press of China, 2021.
    [14]
    Micradar. Healthy care radar[EB/OL]. https://en.micradar.cn/, 2024.
    [15]
    NAM Y, REYES B A, and CHON K H. Estimation of respiratory rates using the built-in microphone of a smartphone or headset[J]. IEEE Journal of Biomedical and Health Informatics, 2016, 20(6): 1493–1501. doi: 10.1109/JBHI.2015.2480838.
    [16]
    FUJITA Y, WATANABE K, KOBAYASHI K, et al. Development of system for unrestrained measurement of vital signs in the bathroom[C]. SICE Annual Conference 2011, Tokyo, Japan, 2011: 2322–2325.
    [17]
    DAFNA E, HALEVI M, BEN OR D, et al. Estimation of macro sleep stages from whole night audio analysis[C]. Annu Int Conf IEEE Eng Med Biol Soc 2016, Florida, USA, 2016: 2847–2850. doi: 10.1109/EMBC.2016.7591323.
    [18]
    WANG Zhi, ZHANG Fusang, LI Siheng, et al. Exploiting passive beamforming of smart speakers to monitor human heartbeat in real time[C]. 2021 IEEE Global Communications Conference, Madrid, Spain, 2021: 1–6. doi: 10.1109/GLOBECOM46510.2021.9685922.
    [19]
    王军强. 噪声敏感建筑隔声与声舒适性要求[J]. 声学技术, 2023, 42(1): 57–61. doi: 10.16300/j.cnki.1000-3630.2023.01.010.

    WANG Junqiang. Requirements for sound insulation and sound comfort of noise-sensitive buildings[J]. Technical Acoustics, 2023, 42(1): 57–61. doi: 10.16300/j.cnki.1000-3630.2023.01.010.
    [20]
    国家环境保护总部. GB 22337-2008 社会生活环境噪声排放标准[S]. 北京: 中国环境科学出版社, 2008.

    National Environmental Protection Headquarters. GB 22337-2008 Emission standard for community noise[S]. Beijing: China Environmental Press, 2008.
    [21]
    AL-NAJI A, AL-ASKERY A J, GHARGHAN S K, et al. A system for monitoring breathing activity using an ultrasonic radar detection with low power consumption[J]. Journal of Sensor and Actuator Networks, 2019, 8(2): 32. doi: 10.3390/jsan8020032.
    [22]
    AMBROSANIO M, FRANCESCHINI S, GRASSINI G, et al. A multi-channel ultrasound system for non-contact heart rate monitoring[J]. IEEE Sensors Journal, 2020, 20(4): 2064–2074. doi: 10.1109/JSEN.2019.2949435.
    [23]
    WANG Yili. Vital signs estimation scheme based on autocorrelation and variational mode decomposition using ultra-wide band radar[C]. 2023 8th International Conference on Intelligent Computing and Signal Processing, Xi’an, China, 2023: 350–354. doi: 10.1109/ICSP58490.2023.10248772.
    [24]
    MA Shaopeng, XUE Wei, CHEN Kehui, et al. Radar vital signs detection method based on variational mode decomposition and wavelet transform[C]. 2021 China Automation Congress, Beijing, China, 2021: 7469–7474. doi: 10.1109/CAC53003.2021.9728129.
    [25]
    XIA Ziliang, WANG Xinhuai, WEI Hongbo, et al. Detection of vital signs based on variational mode decomposition using FMCW radar[C]. 2021 International Conference on Microwave and Millimeter Wave Technology, Nanjing, China, 2021: 1–3. doi: 10.1109/ICMMT52847.2021.9617967.
    [26]
    YANG Xuan, WANG Ziying, ZHANG Li, et al. A novel arc-chord approximation demodulation based on green’s theorem[C]. 2023 IEEE MTT-S International Wireless Symposium, Qingdao, China, 2023: 1–3. doi: 10.1109/IWS58240.2023.10222839.
    [27]
    LV Qinyi, CAO Congqi, and ZHOU Deyun. Wireless vital-sign detection based on improved arc-chord approximation demodulation[C]. 2022 IEEE MTT-S International Microwave Biomedical Conference, Suzhou, China, 2022: 57–59. doi: 10.1109/IMBioC52515.2022.9790242.
    [28]
    LV Qinyi, MIN Lingtong, CAO Congqi, et al. Time-domain Doppler biomotion detections immune to unavoidable DC offsets[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 8505010. doi: 10.1109/TIM.2021.3125096.
    [29]
    ZHANG Li, FU Changhong, ZHUANG Zhongxu, et al. Kalman filter and cross-multiply algorithm with adaptive DC offset removal[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 4002710. doi: 10.1109/TIM.2022.3147317.
    [30]
    CAO Zhiyuan, ZHANG Chengcheng, JIN Zirui, et al. A DC offset cancellation circuit using digital assistance technique and self-calibrating comparator for RF transceiver[C]. 2023 IEEE 15th International Conference on ASIC, Nanjing, China, 2023: 1–4. doi: 10.1109/ASICON58565.2023.10396145.
    [31]
    HAZRA S, FUSCO A, KIPRIT G N, et al. Robust radar-based vital sensing with adaptive sinc filtering and random body motion rejections[J]. IEEE Sensors Letters, 2023, 7(5): 7001604. doi: 10.1109/LSENS.2023.3266237.
    [32]
    TARIQ A and GHAFOURI-SHIRAZ H. Vital signs detection using Doppler radar and continuous wavelet transform[C]. 5th European Conference on Antennas and Propagation, Rome, Italy, 2011: 285–288.
    [33]
    ZHANGI T, VALERIO G, SARRAZIN J, et al. Wavelet-based analysis of 60 GHz Doppler radar for non-stationary vital sign monitoring[C]. 2017 11th European Conference on Antennas and Propagation, Paris, France, 2017: 1876–1877. doi: 10.23919/EuCAP.2017.7928689.
    [34]
    FU Yangye, SUN Lijuan, GUO Jian, et al. EEMD-MICA based heart rate extraction algorithm for radar signals[C]. 2023 35th Chinese Control and Decision Conference, Yichang, China, 2023: 1649–1655. doi: 10.1109/CCDC58219.2023.10327001.
    [35]
    INDU S, GUPTA A, KAW A, et al. Life detection system using continuous wave Doppler radar and blind source separation[C]. 2017 Devices for Integrated Circuit, Kalyani, India, 2017: 711–715. doi: 10.1109/DEVIC.2017.8074043.
    [36]
    LIU Heng, WANG Yong, ZHOU Mu, et al. Millimeter-wave radar vital signs detection based on modified independent component analysis[C]. 2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation, Guangzhou, China, 2023: 1–2. doi: 10.1109/APCAP59480.2023.10470254.
    [37]
    CHOWDHURY J H, SHIHAB M, PRAMANIK S K, et al. Separation of heartbeat waveforms of simultaneous two-subjects using independent component analysis and empirical mode decomposition[J]. IEEE Microwave and Wireless Technology Letters, 2024, 34(8): 1059–1062. doi: 10.1109/LMWT.2024.3420253.
    [38]
    HUANG C Y, FANG Guanwei, CHUANG H R, et al. Clutter-resistant vital sign detection using amplitude-based demodulation by EEMD-PCA-correlation algorithm for FMCW radar systems[C]. 2019 49th European Microwave Conference, Paris, France, 2019: 928–931. doi: 10.23919/EuMC.2019.8910730.
    [39]
    YU Xiaogang, LI Changzhi, and LIN J. Noise analysis for noncontact vital sign detectors[C]. 2010 IEEE 11th Annual Wireless and Microwave Technology Conference, Melbourne, USA, 2010: 1–4. doi: 10.1109/WAMICON.2010.5461889.
    [40]
    MARZO A, CORKETT T, and DRINKWATER B W. Ultraino: An open phased-array system for narrowband airborne ultrasound transmission[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2018, 65(1): 102–111. doi: 10.1109/TUFFC.2017.2769399.
    [41]
    MÄKINEN Y, AZZARI L, and FOI A. Collaborative filtering of correlated noise: Exact transform-domain variance for improved shrinkage and patch matching[J]. IEEE Transactions on Image Processing, 2020, 29: 8339–8354. doi: 10.1109/TIP.2020.3014721.
    [42]
    WANG Jingyu, WANG Xiang, ZHU Zhongbo, et al. 1-D microwave imaging of human cardiac motion: An ab-initio investigation[J]. IEEE Transactions on Microwave Theory and Techniques, 2013, 61(5): 2101–2107. doi: 10.1109/TMTT.2013.2252186.
    [43]
    YANG Xiufang, JIAO Zhen, and YANG Yankang. Researchon linear demodulation method of Doppler radar vital signal[C]. 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms, Changchun, China, 2022: 1381–1385. doi: 10.1109/EEBDA53927.2022.9744892.
    [44]
    WANG Jingyu, WANG Xiang, CHEN Lei, et al. Noncontact distance and amplitude-independent vibration measurement based on an extended DACM algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(1): 145–153. doi: 10.1109/TIM.2013.2277530.
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    • Table  1.  X-band experimental radar parameters
      Parameters Technical indexes
      Operating frequency X
      Scope of operating frequency 9.3-9.5 GHz
      Range 0.0625-96 nm
      Scanning bandwidth 25 MHz
      Range resolution 6 m
      Pulse repetition frequency 1.6 K, 3 K, 5 K & 10 K
      Emission peak power 50 W
      Revolving speed of antenna 2rpm, 12 rpm, 24 rpm, 48 rpm
      Length of antenna 1.8 m
      Operating mode of antenna Gazing & circular scanning
      Polarization mode of antenna HH
      Width of horizontal beam of antenna 1.2°
      Width of vertical beam of antenna 22°
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    • 1.  Summary table of sea-detecting radar data (the 1st phase)
      No. Grade of sea conditions Data size Working mode of radar antenna Transmission pulse mode Regional AIS data Meteorological and
      hydrological data       		Grazing angle (°)
      1 Grade 1 and below >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
      2 Grade 2 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
      3 Grade 3 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
      4 Grade 4 >10 Groups Staring & scanning Mode 2 Yes Yes 0.3–15
      5 Grade 5 >5 Groups Staring & scanning Mode 2 No Yes 0.3–15
      Notes: ① Daily AIS data will be generated into a table format, which provides MMSI of ships, time, longitude and latitude, and others. ② Daily meteorological and hydrological data will be generated into an NC-format file, which indicates wind speed, wind direction, wave height, and cycle. ③ The number of pulses contained in a group of data under staring mode will not be less than 105, while a group of data under scanning mode contains a complete scanning cycle. ④ Under transmission pulse mode 2, radars of each corresponding repetition cycle will transmit a single-carrier frequency pulse and an LFM pulse.
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