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Small-footprint full-waveform Light Detection And Ranging (LiDAR) exhibits significant application potential owing to its high penetration capability and ability to capture complete echo data. However, the efficient and accurate processing of massive echo signals remains a crucial challenge for practical use, particularly in advancing waveform decomposition technology. In small-footprint full-waveform LiDAR systems, most echoes are single-target, while only multi-target echoes require detailed decomposition. Current solutions often sacrifice precision by employing simplified rapid waveform decomposition algorithms or process all echoes indiscriminately, resulting in low efficiency and the inability to balance accuracy and speed effectively. This study proposes a spatiotemporal coupling model-driven lightweight algorithm for detecting multi-target echoes in small-footprint full-waveform LiDAR. For the first time, it achieves efficient and accurate detection of multi-target echoes from waveform data with unknown echo counts. The proposed method eliminates redundant computations caused by indiscriminate processing of single-target echoes, significantly reducing waveform decomposition iterations. The technical contributions include constructing a spatiotemporal coupling echo signal model that captures the spatiotemporal characteristics of echo transmission, implementing model-driven lightweight waveform parameter estimation through double Gaussian function superposition fitting, and introducing an adaptive correlation discrimination method based on a signal-to-noise ratio approach. By leveraging the consistency of system-emitted pulses, the proposed method enables lightweight yet accurate multi-target echo detection. Experimental results on terrestrial and airborne waveform datasets demonstrate that our algorithm achieves 98.4% detection accuracy with a 93.1% recall rate. When integrated with four waveform decomposition methods, it improves processing efficiency by 2–3 times. The efficiency gain becomes even more pronounced as the proportion of single-target echoes increases.
Electromagnetic (EM) metasurfaces are a novel type of artificial EM material exhibiting great advantages for wireless communication and signal processing. By introducing external excitation (mechanical, thermal, electrical, optical, and magnetic excitations), the EM metasurface realizes a more flexible dynamic control of the EM response. On the basis of the dynamic control method, the EM metasurface can accurately control the phase, amplitude, polarization mode, propagation mode, and other characteristics of EM waves to realize wavefront control in different application scenarios. In this paper, we first summarize the research progress of dynamic control technology for EM metasurfaces. Then, the research status of EM metasurfaces in the fields of holographic imaging, polarization conversion, metalensing, beam steering, and intelligent systems based on the application scenarios is discussed. Finally, the development modes of EM metasurfaces and the development trends of intelligent control in the future are summarized and explored.
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Crop and soil parameters serve as fundamental indicators for characterizing crop growth status and monitoring vegetation dynamics. Radar remote sensing presents unique advantages, such as all-weather and day-and-night observation capabilities, as well as insensitivity to meteorological conditions. Furthermore, the penetration ability of microwaves enhances the sensitivity to soil parameter variations beneath crop canopies, demonstrating significant potential for retrieving crop and soil parameters. This article presents a comprehensive review and analysis of inversion models used for crop and soil parameters based on the microwave scattering theory. First, it discusses the evolution of microwave scattering models from theoretical frameworks to semiempirical approaches, demonstrating key trends in theoretical advancements and methodological refinements. Subsequently, it systematically examines inversion methods for crop parameters, soil parameters, and crop–soil interactions, revealing their underlying microwave scattering mechanisms. Finally, the article discusses current model limitations and proposes future research directions aligned with emerging technological developments to provide novel insights for subsequent investigations.
With the widespread application of Wi-Fi sensing technology in intelligent health monitoring, constructing high-quality perception datasets has become a key challenge. Particularly in monitoring abnormal behaviors, such as falls, traditional methods rely on repeated human experiments, which not only poses safety risks but also raises ethical concerns. To address these issues, this paper proposes a time-domain digital coding metasurface-assisted data acquisition method. By simulating the Doppler effect and micro-Doppler characteristics of the human body, the time-domain digital coding metasurface can effectively replace human experiments and assist in constructing Wi-Fi sensing datasets. To verify the feasibility of this method, we develop a time-domain digital coding metasurface with 0°–360° full-phase modulation capability. Experimental results show that the signals generated by the metasurface retain the motion characteristics of the human body, complement real samples, reduce the complexity of data collection, and finally improve the monitoring accuracy of the classification model significantly. This method provides an innovative and feasible solution for data acquisition for Wi-Fi sensing technology.
This study explores the use of one-bit Digital-to-Analog Converters (DAC) to mitigate the challenges of high hardware costs and excessive power consumption in large-scale Multiple-Input Multiple-Output (MIMO) communication and radar systems. The present study focuses on the design of one-bit transmit waveforms for dual-functional radar and communication systems. Under preset communication Quality of Service (QoS) constraints, the objective was to minimize the integral sidelobe-to-mainlobe ratio of the radar transmit beampattern. This should help enhance the power concentration of the transmitted beampattern and improve the performance of the beampattern synthesis. To address the limited Degrees of Freedom (DoF) caused by one-bit quantization, this study employs symbol-level precoding technology and then fully utilizes the DoFs in spatial and temporal domains to assist waveform design based on the principle of Constructive Interference (CI). To address the nonconvex fractional quadratic objective function and the multiple nonconvex discrete constraints inherent in the proposed waveform design problem, this study introduces an algorithm that combines the Dinkelbach transform with the Alternating Direction Method Of Multipliers (ADMM). This approach effectively tackles the NP-hard problem. The numerical results demonstrate that the designed waveform significantly reduces the required DAC resolution and achieves excellent radar beampattern performance while satisfying the QoS requirements of downlink multiuser communications.
Because the Terahertz (THz) band is capable of achieving terabit-per-second communication rates and high-precision sensing, THz Integrated Sensing And Communication (ISAC) has become a key technology for future wireless systems. We propose a THz ISAC framework based on a delay–Doppler waveform, i.e., the Orthogonal Delay–Doppler Division Multiplexing (ODDM) modulation. A more general off-grid ODDM modulation input/output relationship is derived to eliminate the assumption that channel path delays and Doppler frequency shifts are integer multiples of their resolutions. For ODDM symbol detection, a time-domain channel equalizer based on the conjugate gradient method is proposed to optimize the computational complexity. Compared with orthogonal frequency division multiplexing, ODDM demonstrates higher Doppler robustness against the Doppler effect. A sensing estimation algorithm is designed to achieve high-precision estimates with low complexity. The results show that the multi-target estimation accuracy approaches Cramér–Rao lower bounds.
Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.
The airport docking guidance system is essential for enhancing airport safety and operational efficiency. This study introduces a deep learning-based point cloud completion network designed for accurate aircraft localization using LiDAR technology. Initially, the aircraft parking process is simulated in a realistic virtual environment to generate complete point cloud data. Subsequently, partial point clouds caused by occlusions or sensor limitations are processed through the proposed network to reconstruct their complete geometric structures. Then the restored point cloud is aligned with a predefined aircraft model, enabling precise calculation of the aircraft's center coordinates in the simulated coordinate system through spatial transformation. Experimental results demonstrate that the network effectively recovers structural details from incomplete point clouds, enabling accurate computation of aircraft centroid coordinates. This approach achieves high-precision position detection for aircraft during docking, showing significant potential for practical airport applications. The codes are available at: https://www.scidb.cn/anonymous/UXZFZkFm.
Synthetic aperture radar (SAR) ocean remote sensing simulation is an important analytical tool for designing SAR systems for ocean applications. It can also provide training samples for detecting and recognizing SAR images of complex ocean phenomena. Therefore, it plays an important role in the design and application of SAR ocean remote sensing systems. The motion, time-varying, and decoherence characteristics of the sea surface caused the simulation difficulty and calculation amount of SAR ocean remote sensing to be much larger than those of fixed land targets. Therefore, improving the simulation efficiency while ensuring the simulation accuracy is key to achieving high-precision and high-efficiency simulation of SAR ocean imaging. This study introduces the main methods, development status, and main problems of dynamic ocean SAR imaging simulation and provides methods for realizing key problems in high-precision simulation of dynamic ocean SAR imaging. The method can complete the simulation of a 4-m resolution at a 400-km2 scene within 10 min while ensuring high fidelity. Under typical working conditions, the spectral peak error of a simulated SAR image is 3%, and the spectral width error is 4%. The typical applications of dynamic ocean surface SAR imaging simulation in wave spectrum inversion, wave texture suppression based on depth cancellation networks, and ship wake detection based on the Wake2Wake network are introduced. On the one hand, these applications verify that the fidelity of the high-precision simulation of dynamic sea SAR imaging presented in this study can satisfy the requirements of intelligent simulation training. On the other hand, the high-precision simulation offers a good prospect for intelligent application of SAR ocean images and can be an important method for providing samples for intelligent application of SAR ocean remote sensing.
Beamforming enhances the received signal power by transmitting signals in specific directions. However, in high-speed and dynamic vehicular network scenarios, frequent channel state updates and beam adjustments impose substantial system overhead. Furthermore, real-time alignment between the beam and user location becomes challenging, leading to potential misalignment that undermines communication stability. Obstructions and channel fading in complex road environments further constrain the effectiveness of beamforming. To address these challenges, this study proposes a multimodal feature fusion beamforming method based on a convolutional neural network and an attention mechanism model to achieve sensor-assisted high-reliability communication. Data heterogeneity is solved by customizing data conversion and standardization strategies for radar and lidar data collected by sensors. Three-dimensional convolutional residual blocks are employed to extract multimodal features, while the cross-attention mechanism integrates integrate these features for beamforming. Experimental results show that the proposed method achieves an average Top-3 accuracy of nearly 90% in high-speed environments, which is substantially improved compared with the single-modal beamforming scheme.
In recent years, target recognition systems based on radar sensor networks have been widely studied in the field of automatic target recognition. These systems observe the target from multiple angles to achieve robust recognition, which also brings the problem of using the correlation and difference information of multiradar sensor echo data. Furthermore, most existing studies used large-scale labeled data to obtain prior knowledge of the target. Considering that a large amount of unlabeled data is not effectively used in target recognition tasks, this paper proposes an HRRP unsupervised target feature extraction method based on Multiple Contrastive Loss (MCL) in radar sensor networks. The proposed method combines instance level loss, Fisher loss, and semantic consistency loss constraints to identify consistent and discriminative feature vectors among the echoes of multiple radar sensors and then use them in subsequent target recognition tasks. Specifically, the original echo data are mapped to the contrast loss space and the semantic label space. In the contrast loss space, the contrastive loss is used to constrain the similarity and aggregation of samples so that the relative and absolute distances between different echoes of the same target obtained by different sensors are reduced while the relative and absolute distances between different target echoes are increased. In the semantic loss space, the extracted discriminant features are used to constrain the semantic labels so that the semantic information and discriminant features are consistent. Experiments on an actual civil aircraft dataset revealed that the target recognition accuracy of the MCL-based method is improved by 0.4% and 1.4%, respectively, compared with the most advanced unsupervised algorithm CC and supervised target recognition algorithm PNN. Further, MCL can effectively improve the target recognition performance of radar sensors when applied in conjunction with the sensors.
Covert unmanned aerial vehicle (UAV) communication has garnered considerable attention for realizing a sustainable low-altitude economy (LAE). Based on the integrated sensing and communication (ISAC) framework, this paper studies the system strategies and resource allocation for a cooperative multi-UAV covert communication network, where multiple UAVs are employed to simultaneously conduct cooperative sensing and covert downlink transmissions to multiple ground users (GUs) in the presence of a mobile warden (Willie). To improve communication covertness, UAVs adaptively switch between jamming unmanned aerial vehicle (JUAV) mode and information unmanned aerial vehicle (IUAV) mode. To cope with the mobility of Willie, an unscented Kalman filtering (UKF)-based method is employed to track and predict Willie's location using delay and Doppler measurements extracted from ISAC echoes. By jointly optimizing the JUAV selection strategy, IUAV-GU scheduling, and communication/jamming power allocation, a real-time fairness transmission maximization problem is formulated. The alternating optimization (AO) approach is adopted to decompose the original problem into a series of sub-problems, resulting in an efficient sub-optimal solution. Simulation results demonstrate that the proposed scheme can accurately track Willie and effectively ensure covert downlink transmission.
Hyperspectral LiDAR (HSL) can obtain high precision and resolution spatial data along with the spectral information of the target, which can provide effective and multidimensional data for various research and application fields. However, differences in transmitting signal intensities of HSL at various wavelengths lead to variations in corresponding echo intensities, making it challenging to directly reconstruct accurate optical characteristics (reflectance spectral profile) of the target with echo intensities. To obtain the target reflectance spectral profile, a common solution is to correct the echo intensity (standard reference correction method) using standard diffuse reflectance whiteboards. However, in complex detection environments, whiteboards are susceptible to contamination, and the transmitting intensity of the laser may fluctuate due to changes in the environment and equipment conditions, which may potentially impact the calculation accuracy. The direct transmission of information from the full-waveform signals to the reconstruction of the reflectance spectral profiles is a more efficient approach. Therefore, we propose an echo intensity correction method based on HSL full-waveform data for the rapid generation of reflectance spectral profiles of targets. The initial step is to conduct a theoretical analysis that illustrates the similarity between the echo signals and the transmitting signals in terms of their waveforms. A skew-normal Gaussian function is then employed to fit the transmitting and echo signals of the HSL full waveform. Thereafter, the transmit-to-echo signal peak ratios (normalization factors) of the standard diffuse reflectance whiteboard at different wavelengths are calculated under ideal conditions. Finally, the reflectance spectral profile of the target is constructed by combining the normalization factor of the standard diffuse reflectance whiteboard with that of the target. To verify the effectiveness of the proposed method, we conducted experiments to compare the reflectance spectral profiles calculated using the standard reference correction method. Moreover, we performed wood-leaf separation and target classification experiments to assess its reliability and usability. The experimental results reveal the following: (1) the reconstructed reflectance spectral profiles of the target can be obtained by correcting the echo intensity with the transmitting signals, which is similar to that obtained by the standard reference correction method. Moreover, it demonstrates excellent stability under various temperatures and lighting conditions. Compared with the standard reference correction method, this approach effectively overcomes the influence of laser emission energy fluctuations, thereby considerably improving the measurement accuracy and consistency of reflectance spectral curves, especially under prolonged HSL operation conditions. (2) The wood-leaf separation and the multiple target classification can be conducted using the reconstructed target reflectance spectral profiles, with a classification accuracy of over 90%. Overall, the proposed method simplifies the correction of echo intensity for full-waveform HSL, which is suitable for the rapid reconstruction of target hyperspectral information during data acquisition.
Integrated Sensing And Communications (ISAC) based on reusing random communication signals within the existing network architecture may drastically reduce implementation costs, thereby accelerating the integration of sensing functionalities into current communication networks. However, the randomness of communication data introduces fluctuations in sensing performance across different signal realizations, leading to unstable sensing accuracy. To address this issue, we delve into random ISAC signal processing methods and propose a joint transceiver precoding optimization design for Multiple-Input Multiple-Output ISAC (MIMO-ISAC) systems. Specifically, considering target impulse response matrix estimation, we first define the Ergodic Cramér-Rao Bound (ECRB) as an average sensing performance metric under random signaling. By deriving the closed-form expression of the ECRB based on the distribution of complex inverse Wishart matrices, we theoretically reveal the performance loss arising when using random signals for sensing compared to the conventional deterministic orthogonal signals. Furthermore, we formulate the sensing-optimal subproblem by minimizing the ECRB and the communication-optimal subproblem of multiantenna multiuser signal estimation and derive the corresponding sensing-optimal and communication-optimal precoding designs. Subsequently, we extend the proposed transceiver precoding optimization framework to ISAC scenarios by explicitly constraining the communication requirements. Finally, through numerous simulations, we validate the effectiveness of the proposed method. The results demonstrate that the joint transceiver precoding design may allow high-accuracy target response matrix estimation while enabling flexible trade-offs between communication signal estimation and target response matrix estimation errors.
Compared to ground-based external radiation source radar, satellite signal-based external radiation source radar (i.e., satellite signal external radiation source radar) offers advantages such as global, all-time, and all-weather coverage, which can compensate for the limitations of ground-based external radiation source radar in terms of maritime coverage. In contrast to medium and high-altitude satellite signals, Low-Earth Orbit (LEO) communication satellite signals have advantages such as strong reception power and a large number of satellites, which can provide substantial detection range and accuracy for passive detection of maritime targets. In response to future development needs, this paper provides a detailed discussion of the research status and application prospects of satellite signal external radiation source radar, and presents a feasibility analysis for constructing a low-earth orbit communication satellite signal external radiation source radar system using Iridium and Starlink, two types of LEO communication satellite systems, which integrates high and low frequencies with both wide and narrow bandwidths. Based on this, the paper summarizes the technical challenges and potential solutions in the development of low-earth orbit communication satellite signal external radiation source radar systems. The aforementioned research can serve as an important reference for wide-area external radiation source radar detection.
Dual Function Radar and Communication (DFRC)-integrated electronic equipment platform, which combines detection and communication functions, effectively addresses issues such as platform limitations, resource constraints, and electromagnetic compatibility by sharing hardware platforms and transmitting waveforms. Therefore, it has become a research hotspot in recent years. The DFRC technology, centered on detection functionality and incorporating limited communication capabilities, has remarkable application prospects in typical detection scenarios, such as early warning and surveillance and tracking guidance under future combat conditions. This paper focuses on using the signal design method to optimize radar detection performance by effectively adjusting the trade-off between detection and communication in multi-domain resource utilization by guaranteeing a minimum communication performance. First, the performance measurement criteria of DFRC systems were summarized. Then, the paper provides a comprehensive introduction to the DFRC signal design methods under typical detection scenarios and a thorough analysis of the problems and current solutions of each signal design method. Finally, a summary and future research directions are outlined.
Bistatic Synthetic Aperture Radar (SAR), with the separated transmitter and receiver working in coordination, cannot only achieves high-resolution imaging in the forward-looking mode, but also possesses outstanding concealment and anti-interference capabilities. Therefore, bistatic SAR thrives in both civilian and military applications, such as ocean monitoring or reconnaissance imaging. However, ship targets are typically influenced by sea waves, generating unknown and complex three-dimensional oscillations. These random oscillations and radar motions vary with slow time, making the imaging view of bistatic SAR ship targets strongly time-dependent, so that it is extremely difficult to extract effective target features from final imaging results. Moreover, target oscillations are also coupled with the motion of bistatic platforms, which causes severe nonlinear spatial Doppler shifts in target echoes, and thus bistatic SAR images are usually defocused. To address these problems, this paper proposes an imaging method for bistatic SAR ship target by imaging time optimization, which generates well-focused bistatic SAR ship target images with the optimal views. Firstly, short-time Fourier transform is utilized to extract the time-frequency information of the ship. Secondly, based on this time-frequency information from multiple strong scatterers, the optimal three-dimensional rotation parameters are estimated, revealing the time-varying characteristics of the imaging projection plane. Then, the optimal imaging time centers are selected based on the optimal imaging projection planes, while the corresponding optimal imaging time intervals are chosen based on the optimal imaging resolutions. Finally, with the selected optimal imaging times, the desired images of the bistatic SAR ship target are produced. Simulation experiments verify the accuracy of target rotation parameter estimation under different bistatic configurations and noise conditions, as well as the effectiveness of imaging projection plane selection. In general, this method tackles with the issues of the time-varying imaging views of bistatic SAR ship targets and nonlinear spatial Doppler shifts, obtaining well-focused and optimally viewed target images, which significantly enhances the accuracy of subsequent target feature extraction.
With the emergence of the low-altitude economy, the communication and detection issues of Unmanned Aerial Vehicles (UAVs) have gained considerable attention. This paper investigates sensing reference signal design for Integrated Sensing And Communication (ISAC) in Orthogonal Frequency Division Multiplexing (OFDM) systems aimed at detecting long-range, high-speed UAVs. To address the ambiguity problem in long-range and high-speed UAV detection, traditional reference signal designs require densely arranged reference signals, leading to significant resource overhead. In addition, long-range detection based on OFDM waveforms faces challenges from Inter-Symbol Interference (ISI). To address these issues, this paper first proposes a reference signal pattern that supports long-range detection and resists ISI, achieving the maximum unambiguous detection range of the system with reduced resource overhead. Then, to address the challenge of high-speed detection, the paper incorporates range-rate into the Chinese Remainder Theorem-based method. Through the proper configuration of sensing reference signals and the cancellation of ghost targets, this approach significantly increases the unambiguous detection velocity while minimizing resource usage and avoiding the generation of ghost targets. The effectiveness of the proposed methods is validated through simulations. Simulation results show that compared with the traditional sensing reference signal design, our proposed scheme can reduce 72% overhead of reference signals for long-range and high-speed UAV detections.
This paper proposes an intelligent framework based on a cell-free network architecture, called HRT-Net. HRT-Net is designed to enhance multi-station collaborative sensing problems for joint radar and communication systems, offering accurate and resource-efficient target location estimation. First, the sensing area is divided into sub-regions and a lightweight region selection network employing depthwise separable convolution; this approach coarsely identifies the target’s sub-region, reducing computational demands and enabling extensive area coverage. To tackle interstation data disparity, we propose a channel-wise unidimensional attention mechanism. This mechanism aggregates multi-station sensing data effectively, enhancing feature extraction and representation by generating attention weight maps that refine the original features. Finally, we design a target localization network featuring multi-scale and multi-residual connections. This network extracts comprehensive, deep features and achieves multi-level feature fusion, allowing for reliable mapping of data to the target coordinates. Extensive simulations and real-world experiments validate the effectiveness and robustness of our scheme. The results show that compared with the existing methods, HRT-Net achieves centimeter-level target localization with low computational complexity and minimal storage overhead.
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Light Detection And Ranging (LiDAR) systems lack texture and color information, while cameras lack depth information. Thus, the information obtained from LiDAR and cameras is highly complementary. Therefore, combining these two types of sensors can obtain rich observation data and improve the accuracy and stability of environmental perception. The accurate joint calibration of the external parameters of these two types of sensors is the premise of data fusion. At present, most joint calibration methods need to be processed through target calibration and manual point selection. This makes it impossible to use them in dynamic application scenarios. This paper presents a ResCalib deep neural network model, which can be used to solve the problem of the online joint calibration of LiDAR and a camera. The method uses LiDAR point clouds, monocular images, and in-camera parameter matrices as the input to achieve the external parameters solving of LiDAR and cameras; however, the method has low dependence on external features or targets. ResCalib is a geometrically supervised deep neural network that automatically estimates the six-degree-of-freedom external parameter relationship between LiDAR and cameras by implementing supervised learning to maximize the geometric and photometric consistencies of input images and point clouds. Experiments show that the proposed method can correct errors in calibrating rotation by ±10° and translation by ±0.2 m. The average absolute errors of the rotation and translation components of the calibration solution are 0.35° and 0.032 m, respectively, and the time required for single-group calibration is 0.018 s, which provides technical support for realizing automatic joint calibration in a dynamic environment.
This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
The miniature multistatic Synthetic Aperture Radar (SAR) system uses a flexible configuration of transceiver division compared with the miniature monostatic SAR system, thereby affording the advantages of multi-angle imaging. As the transceiver-separated SAR system uses mutually independent oscillator sources, phase synchronization is necessary for high-precision imaging of the miniature multistatic SAR. Although current research on phase synchronization schemes for bistatic SAR is relatively mature, these schemes are primarily based on the pulse SAR system. However, a paucity of research exists on phase synchronization for the miniature multistatic Frequency Modulated Continuous Wave (FMCW) SAR. In comparison with the pulse SAR, the FMCW SAR system lacks a temporal interval between the transmitted pulses. Consequently, some phase synchronization schemes developed for the pulse SAR system cannot be directly applied to the FMCW SAR system. To this end, this study proposes a novel phase synchronization method for the miniature multistatic FMCW SAR, effectively resolving the problem of the FMCW SAR. This method uses the generalized Short-Time Shift-Orthogonal (STSO) waveform as the phase synchronization signal of disparate radar platforms. The phase error between the radar platforms can be effectively extracted through pulse compression to realize phase synchronization. Compared with the conventional linear frequency-modulated waveform, after the generalized STSO waveform is pulsed by the same pulse compression function, the interference signal energy is concentrated away from the peak of the matching signal and the phase synchronization accuracy is enhanced. Furthermore, the proposed method is adapted to the characteristics of dechirp reception in FMCW miniature multistatic SAR systems, and ground and numerical simulation experiments verify that the proposed method has high synchronization accuracy.
The ionosphere can distort received signals, degrade imaging quality, and decrease interferometric and polarimetric accuracies of spaceborne Synthetic Aperture Radars (SAR). The low-frequency systems operating at L-band and P-band are very susceptible to such problems. From another viewpoint, low-frequency spaceborne SARs can capture ionospheric structures with different spatial scales over the observed scope, and their echo and image data have sufficient ionospheric information, offering great probability for high-precision and high-resolution ionospheric probing. The research progress of ionospheric probing based on spaceborne SARs is reviewed in this paper. The technological system of this field is summarized from three aspects: Mapping of background ionospheric total electron content, tomography of ionospheric electron density, and probing of ionospheric irregularities. The potential of the low-frequency spaceborne SARs in mapping ionospheric local refined structures and global tendency is emphasized, and the future development direction is prospected.
Bistatic Synthetic Aperture Radar (BiSAR) needs to suppress ground background clutter when detecting and imaging ground moving targets. However, due to the spatial configuration of BiSAR, the clutter poses a serious space-time nonstationary problem, which deteriorates the clutter suppression performance. Although Space-Time Adaptive Processing based on Sparse Recovery (SR-STAP) can reduce the nonstationary problem by reducing the number of samples, the off-grid dictionary problem will occur during processing, resulting in a decrease in the space-time spectrum estimation effect. Although most of the typical SR-STAP methods have clear mathematical relations and interpretability, they also have some problems, such as improper parameter setting and complicated operation in complex and changeable scenes. To solve the aforementioned problems, a complex neural network based on the Alternating Direction Multiplier Method (ADMM), is proposed for BiSAR space-time adaptive clutter suppression. First, a sparse recovery model of the continuous clutter space-time domain of BiSAR is constructed based on the Atomic Norm Minimization (ANM) to overcome the off-grid problem associated with the traditional discrete dictionary model. Second, ADMM is used to rapidly and iteratively solve the BiSAR clutter spectral sparse recovery model. Third according to the iterative and data flow diagrams, the artificial hyperparameter iterative process is transformed into ANM-ADMM-Net. Then, the normalized root-mean-square-error network loss function is set up and the network model is trained with the obtained data set. Finally, the trained ANM-ADMM-Net architecture is used to quickly process BiSAR echo data, and the space-time spectrum of BiSAR clutter is accurately estimated and efficiently restrained. The effectiveness of this approach is validated through simulations and airborne BiSAR clutter suppression experiments.
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