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Editor Work2018 Vol. 7, No. 3
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2018, 7(3): 275-284.
Abstract
1036KB
Ultra-WideBand (UWB) radar can reconstruct the layout of a building, providing rich information for detecting and locating humans in buildings. Traditional imaging methods suffer from serious sidelobes and location displacement of behind-the-wall target because of the influence of walls. Sparse recovery is introduced into the field of through-the-wall imaging to improve the imaging quality. However, the reconstruction probability of weak scattering targets is low in traditional methods. In this study, the combination of sparse recovery method and Coherence Factor (CF) weighting is proposed to improve the reconstruction probability of weak scattering targets inside a room. The quasi-establishment of the support set can be improved during sparse imaging by reducing the effect of the sidelobes of strong scattering targets with CF, ultimately enhancing the robustness of the sparse imaging of the building layout. A location correction model for multiple walls after sparse imaging is established, based on which the locating error of walls can be reduced with a low amount of calculation. The results of the measured data reveal that compared with the traditional generalized orthogonal matching pursuit method, the proposed methods can improve the reconstruction probability of weak scattering targets and reduce the locating error of the inner layouts of buildings to less than 10 cm.
Ultra-WideBand (UWB) radar can reconstruct the layout of a building, providing rich information for detecting and locating humans in buildings. Traditional imaging methods suffer from serious sidelobes and location displacement of behind-the-wall target because of the influence of walls. Sparse recovery is introduced into the field of through-the-wall imaging to improve the imaging quality. However, the reconstruction probability of weak scattering targets is low in traditional methods. In this study, the combination of sparse recovery method and Coherence Factor (CF) weighting is proposed to improve the reconstruction probability of weak scattering targets inside a room. The quasi-establishment of the support set can be improved during sparse imaging by reducing the effect of the sidelobes of strong scattering targets with CF, ultimately enhancing the robustness of the sparse imaging of the building layout. A location correction model for multiple walls after sparse imaging is established, based on which the locating error of walls can be reduced with a low amount of calculation. The results of the measured data reveal that compared with the traditional generalized orthogonal matching pursuit method, the proposed methods can improve the reconstruction probability of weak scattering targets and reduce the locating error of the inner layouts of buildings to less than 10 cm.
2018, 7(3): 294-302.
When using Ground Penetrating Radar (GPR) on the occasion of complex underground medium detection, radar echo can be easily affected by various noise. In order to improve GPR detection resolution and data interpretation quality, this paper proposed a new GPR denoising algorithm based on automatic reversed-phase correction and kurtosis value comparison. GPR echo signal and random noise with the same length were fitted and two signals can be obtained. By using Independent Component Analysis (ICA) algorithm, these two signals can be decomposed into two other signals, one with high kurtosis named S1 and one with low kurtosis named S2. S1 signal’s phase was determined and automatic phase correction was carried out. By using Complete Ensemble Empirical Mode Decomposition (CEEMD) algorithm, S1 after automatic phase correction was decomposed, several Intrinsic Mode Function (IMF) can be obtained and kurtosis value of each IMF can be calculated. S2 signal’s kurtosis value was set as a threshold. The IMFs whose kurtosis values are lower than this threshold are classified as noise components, while the other IMFs whose kurtosis values are higher than this threshold are classified as signal components. By summing the IMFs of signal components, GPR echo signal can be reconstructed and denoising. This new GPR denoising algorithm solves the problems of phase uncertainty in ICA and manual IMF components classification in CEEMD and thus improves GPR denoising effects with higher computation efficiency. The effectiveness of the proposed algorithm is verified by simulation and real data processing experiments.
When using Ground Penetrating Radar (GPR) on the occasion of complex underground medium detection, radar echo can be easily affected by various noise. In order to improve GPR detection resolution and data interpretation quality, this paper proposed a new GPR denoising algorithm based on automatic reversed-phase correction and kurtosis value comparison. GPR echo signal and random noise with the same length were fitted and two signals can be obtained. By using Independent Component Analysis (ICA) algorithm, these two signals can be decomposed into two other signals, one with high kurtosis named S1 and one with low kurtosis named S2. S1 signal’s phase was determined and automatic phase correction was carried out. By using Complete Ensemble Empirical Mode Decomposition (CEEMD) algorithm, S1 after automatic phase correction was decomposed, several Intrinsic Mode Function (IMF) can be obtained and kurtosis value of each IMF can be calculated. S2 signal’s kurtosis value was set as a threshold. The IMFs whose kurtosis values are lower than this threshold are classified as noise components, while the other IMFs whose kurtosis values are higher than this threshold are classified as signal components. By summing the IMFs of signal components, GPR echo signal can be reconstructed and denoising. This new GPR denoising algorithm solves the problems of phase uncertainty in ICA and manual IMF components classification in CEEMD and thus improves GPR denoising effects with higher computation efficiency. The effectiveness of the proposed algorithm is verified by simulation and real data processing experiments.
2018, 7(3): 303-312.
A task scheduling method based on dynamic time window is proposed that is aimed at resolving the task scheduling problem of phased array radars. Based on constraints between the residuals of target tracking filtering and radar target tracking gate and search frame cycle, the method calculates the time window of tracking task and the time window of the search task, respectively. Finally, we undertake a simulation of our method by comparing with the traditional time window design method. The simulation results prove the effectiveness and superiority of our method.
A task scheduling method based on dynamic time window is proposed that is aimed at resolving the task scheduling problem of phased array radars. Based on constraints between the residuals of target tracking filtering and radar target tracking gate and search frame cycle, the method calculates the time window of tracking task and the time window of the search task, respectively. Finally, we undertake a simulation of our method by comparing with the traditional time window design method. The simulation results prove the effectiveness and superiority of our method.
2018, 7(3): 313-319.
The passive radar is a new radar system based on third-party non-cooperative radiation sources, which has unique advantages in micro-Doppler target classification and recognition. Its characteristics determine that the micro-Doppler effect parameter estimation method must have a good anti-noise performance and a small amount of calculation. In view of these considerations, this study presents a new idea of helicopter rotor parameter estimation using an echo flicker in the time-frequency domain based on the micro-motion signal model for the passive radar. The echo flicker parameters are extracted by accumulating the amplitudes of the positive and negative frequency axis data in the time-frequency diagram. The dictionary matrix is constructed based on the inherent characteristics of the micro-motion signals. The blade length, blade number, rotor speed, and other parameters are estimated using the orthogonal matching pursuit algorithm. Compared with the method based on the conventional Hough transform, the proposed method is more accurate and more rapid. The simulation and experimental results prove the feasibility of this method.
The passive radar is a new radar system based on third-party non-cooperative radiation sources, which has unique advantages in micro-Doppler target classification and recognition. Its characteristics determine that the micro-Doppler effect parameter estimation method must have a good anti-noise performance and a small amount of calculation. In view of these considerations, this study presents a new idea of helicopter rotor parameter estimation using an echo flicker in the time-frequency domain based on the micro-motion signal model for the passive radar. The echo flicker parameters are extracted by accumulating the amplitudes of the positive and negative frequency axis data in the time-frequency diagram. The dictionary matrix is constructed based on the inherent characteristics of the micro-motion signals. The blade length, blade number, rotor speed, and other parameters are estimated using the orthogonal matching pursuit algorithm. Compared with the method based on the conventional Hough transform, the proposed method is more accurate and more rapid. The simulation and experimental results prove the feasibility of this method.
2018, 7(3): 320-334.
Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) imaging system based on the orthogonal double baseline can achieve the 3-D geometric reconstruction of a target effectively, which is extremely helpful in target classification and identification. However, only sparse aperture measurements are available in the actual imaging process, which might pose some challenges to the traditional InISAR imaging algorithms. In this study, a new method of 3-D InISAR imaging of a ship with sparse aperture is presented. Minimum entropy algorithms are adopted to realize motion compensation and image coregistration of the sparse echoes. A gradient-based technique is used to achieve highly accurate signal reconstruction for the sparse aperture. A two-Dimensional (2-D) ISAR image was achieved with azimuth compression via the parameters-estimation method, and the 3-D reconstruction of a ship was achieved via the interference approach. The obtained simulation results validate the feasibility of the presented approach.
Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) imaging system based on the orthogonal double baseline can achieve the 3-D geometric reconstruction of a target effectively, which is extremely helpful in target classification and identification. However, only sparse aperture measurements are available in the actual imaging process, which might pose some challenges to the traditional InISAR imaging algorithms. In this study, a new method of 3-D InISAR imaging of a ship with sparse aperture is presented. Minimum entropy algorithms are adopted to realize motion compensation and image coregistration of the sparse echoes. A gradient-based technique is used to achieve highly accurate signal reconstruction for the sparse aperture. A two-Dimensional (2-D) ISAR image was achieved with azimuth compression via the parameters-estimation method, and the 3-D reconstruction of a ship was achieved via the interference approach. The obtained simulation results validate the feasibility of the presented approach.
2018, 7(3): 335-345.
The array InSAR system obtains a three-dimensional image of an observed scene using a combination of pulse compression and synthetic and real aperture techniques. However, Antenna Phase Center (APC) errors can occur within a practical array InSAR system, which thus degrades the imaging quality in a height direction. The aim of this paper is to improve calibration problems occurring with APC errors. The effect of APC errors is analyzed, and a calibration method based on the orthogonal subspace principle is proposed that utilizes SAR Single Look Complex (SLC) to obtain the noise subspace through eigenvalue decomposition. The subspace orthogonal principle is then used to solve the APC positions of multiple channels simultaneously. In addition, a calibration scheme for the APC position is presented for application with an array InSAR system. The effectiveness of the proposed calibration method is verified using simulations and experimental results.
The array InSAR system obtains a three-dimensional image of an observed scene using a combination of pulse compression and synthetic and real aperture techniques. However, Antenna Phase Center (APC) errors can occur within a practical array InSAR system, which thus degrades the imaging quality in a height direction. The aim of this paper is to improve calibration problems occurring with APC errors. The effect of APC errors is analyzed, and a calibration method based on the orthogonal subspace principle is proposed that utilizes SAR Single Look Complex (SLC) to obtain the noise subspace through eigenvalue decomposition. The subspace orthogonal principle is then used to solve the APC positions of multiple channels simultaneously. In addition, a calibration scheme for the APC position is presented for application with an array InSAR system. The effectiveness of the proposed calibration method is verified using simulations and experimental results.
2018, 7(3): 346-354.
As one of the most important means to achieve a High-Resolution and Wide-Swath (HRWS) imaging of the earth, multichannel in azimuth Synthetic Aperture Radar (SAR) have attracted considerable attention in recent years. However, prior to the unambiguous reconstruction of the multichannel SAR signal, each channel needs to be well calibrated, otherwise the performance of the reconstruction processor may degrade or even lose its effectiveness. Accurate baseband Doppler centroid estimation are critical for channel mismatch calibration and high-resolution imaging in the multichannel SAR systems. However, in the multichannel HRWS SAR system, the signal acquired by each channel is under-sampled that renders the traditional Doppler centroid estimation methods obsolete. In this paper, an eigen-structure method has been used to achieve a robust estimation of the baseband Doppler centroid and the phase mismatch in the multichannel SAR system. Processing with simulated and experimental C-band, four-channel airborne SAR data validates the effectiveness of this method.
As one of the most important means to achieve a High-Resolution and Wide-Swath (HRWS) imaging of the earth, multichannel in azimuth Synthetic Aperture Radar (SAR) have attracted considerable attention in recent years. However, prior to the unambiguous reconstruction of the multichannel SAR signal, each channel needs to be well calibrated, otherwise the performance of the reconstruction processor may degrade or even lose its effectiveness. Accurate baseband Doppler centroid estimation are critical for channel mismatch calibration and high-resolution imaging in the multichannel SAR systems. However, in the multichannel HRWS SAR system, the signal acquired by each channel is under-sampled that renders the traditional Doppler centroid estimation methods obsolete. In this paper, an eigen-structure method has been used to achieve a robust estimation of the baseband Doppler centroid and the phase mismatch in the multichannel SAR system. Processing with simulated and experimental C-band, four-channel airborne SAR data validates the effectiveness of this method.
2018, 7(3): 355-363.
By arranging multiple antennas in the intersection direction and combining the synthetic aperture of azimuth direction and large bandwidth signal with oblique distance, array-interferometric Synthetic Aperture Radar (SAR) can generate a three-dimensional resolution and ensure the elevation spacial sampling due to its multiple array element, which could avoid the layover problem in surveying and mapping in the Interference SAR (InSAR) and realize the three-dimensional imaging of the observation scene. However, considering the existence of too much noise in the three-dimensional point cloud distribution in the scene area and the large elevation error, the traditional Light Detection And Ranging (LiDAR) point cloud filtering method is not suitable for the filtering processing of the array-interferometric SAR point cloud. In order to solve this problem, an array-interferometric SAR point cloud filtering algorithm based on spatial clustering seed growth algorithm is proposed, in which the density-elevation image is generated by the double threshold of density and elevation, the small clutter is removed by image processing, and the vegetation is removed from the point cloud data by using the spatial clustering seed growth algorithm, thus the point cloud filtering process is completed. Using the first airborne array-interferometric SAR experimental data, the validity of the proposed algorithm is verified compared to the traditional LiDAR filtering method, which provides the guarantee for the subsequent building extraction and meticulous treatment.
By arranging multiple antennas in the intersection direction and combining the synthetic aperture of azimuth direction and large bandwidth signal with oblique distance, array-interferometric Synthetic Aperture Radar (SAR) can generate a three-dimensional resolution and ensure the elevation spacial sampling due to its multiple array element, which could avoid the layover problem in surveying and mapping in the Interference SAR (InSAR) and realize the three-dimensional imaging of the observation scene. However, considering the existence of too much noise in the three-dimensional point cloud distribution in the scene area and the large elevation error, the traditional Light Detection And Ranging (LiDAR) point cloud filtering method is not suitable for the filtering processing of the array-interferometric SAR point cloud. In order to solve this problem, an array-interferometric SAR point cloud filtering algorithm based on spatial clustering seed growth algorithm is proposed, in which the density-elevation image is generated by the double threshold of density and elevation, the small clutter is removed by image processing, and the vegetation is removed from the point cloud data by using the spatial clustering seed growth algorithm, thus the point cloud filtering process is completed. Using the first airborne array-interferometric SAR experimental data, the validity of the proposed algorithm is verified compared to the traditional LiDAR filtering method, which provides the guarantee for the subsequent building extraction and meticulous treatment.
2018, 7(3): 285-293.
This study proposes a high-resolution radar imaging method combined with the sparse low-rank matrix recovery technique and the deconvolution algorithm based on the matched filtering result. We establish a two-Dimensional (2D) convolution model for the radar signal after the Matched Filter (MF) to maximize the Signal-to-Noise Ratio (SNR) and use the 2D deconvolution algorithm of the Wiener filter to obtain a high resolution. However, representative deconvolution algorithms are confronted with an ill-posed problem, which magnifies the noise while influencing the imaging resolution. Prior to this study, the echo matrix after the MF was proven to be sparse and low rank under the constraint of a sparsely distributed target. The target distribution is smoothed by the influence of the point spread function. Hence, inspired by these points, we further enhance the SNR of the echo matrix based on the sparse and low-rank characteristics to alleviate the ill-posed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data.
This study proposes a high-resolution radar imaging method combined with the sparse low-rank matrix recovery technique and the deconvolution algorithm based on the matched filtering result. We establish a two-Dimensional (2D) convolution model for the radar signal after the Matched Filter (MF) to maximize the Signal-to-Noise Ratio (SNR) and use the 2D deconvolution algorithm of the Wiener filter to obtain a high resolution. However, representative deconvolution algorithms are confronted with an ill-posed problem, which magnifies the noise while influencing the imaging resolution. Prior to this study, the echo matrix after the MF was proven to be sparse and low rank under the constraint of a sparsely distributed target. The target distribution is smoothed by the influence of the point spread function. Hence, inspired by these points, we further enhance the SNR of the echo matrix based on the sparse and low-rank characteristics to alleviate the ill-posed problem of deconvolution and the poor resolution of the Wiener filter. The performance of the proposed method is demonstrated by the real experimental data.
2018, 7(3): 364-375.
In contrast to remote sensing radar, automotive radar focuses on the detection of short-range targets in the 0–1000 m range. Conventional automotive pulsed radar usually uses a monostatic antenna and it requires high peak power for the transmission of the short duration pulses to reliably detect targets at close range with a high resolution. Unfortunately, it is difficult and expensive to generate high-powered pulses on the nanosecond scale. Meanwhile, the existing automotive radars suffer from bottlenecks, i.e., spatial resolution, sidelobe levels, and Inter-Sensor Interference (ISI). To overcome the above challenges, a bistatic antenna to transmit and receive large time-bandwidth product waveforms is firstly proposed in this paper. Secondly, high spatial resolution is implemented using a Digital Beam Forming (DBF) transmitter and the high range resolution is achieved by using the pulse compression technique. Additionally, the radial velocity of the target is calculated by applying pulse Doppler processing. Finally, to deal with the sidelobe effect of impulse response function of point target and the interference arising from neighboring radars, novel Orthogonal Random Phase-Coded (ORPC) radar signals are presented. Using these ORPC signals, the impulse response function of the radar can achieve a peak sidelobe ratio of –45 dB without any loss in the signal-to-noise ratio. Most importantly, interference can be significantly reduced by using the proposed signals. Extensive simulations demonstrate the effectiveness and advantages of the proposed radar.
In contrast to remote sensing radar, automotive radar focuses on the detection of short-range targets in the 0–1000 m range. Conventional automotive pulsed radar usually uses a monostatic antenna and it requires high peak power for the transmission of the short duration pulses to reliably detect targets at close range with a high resolution. Unfortunately, it is difficult and expensive to generate high-powered pulses on the nanosecond scale. Meanwhile, the existing automotive radars suffer from bottlenecks, i.e., spatial resolution, sidelobe levels, and Inter-Sensor Interference (ISI). To overcome the above challenges, a bistatic antenna to transmit and receive large time-bandwidth product waveforms is firstly proposed in this paper. Secondly, high spatial resolution is implemented using a Digital Beam Forming (DBF) transmitter and the high range resolution is achieved by using the pulse compression technique. Additionally, the radial velocity of the target is calculated by applying pulse Doppler processing. Finally, to deal with the sidelobe effect of impulse response function of point target and the interference arising from neighboring radars, novel Orthogonal Random Phase-Coded (ORPC) radar signals are presented. Using these ORPC signals, the impulse response function of the radar can achieve a peak sidelobe ratio of –45 dB without any loss in the signal-to-noise ratio. Most importantly, interference can be significantly reduced by using the proposed signals. Extensive simulations demonstrate the effectiveness and advantages of the proposed radar.
2018, 7(3): 376-386.
This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data. First, based on the cylindrical scanning imaging model, the Barker code-based randomly sparse sampling method is employed to reduce the scanning time. Then, a three-dimensional imaging algorithm based on interferometry and compressed sensing in the frequency domain is proposed, with sparse representation of the image in the frequency domain after interferometry and Compressed Sensing measurement model, to recover the image frequency spectrum, thereby implementing human security image reconstruction via sparse sampling. Real data processing results indicated that the proposed method could obtain image resolution and performance similar to those of complete samples and that the image correlation coefficients before and after sparse sampling were better than 0.9, with 50% time/data reduction. Furthermore, based on the Barker codes and multistatic work mode, a sparse array architecture for rapid imaging was designed with a sparse rate of 94.6% and the guarantee of imaging quality. The proposed method was found to considerably increase the passage rate and reduce the amount of radiation unit and system complexity, marking its application significance and market prospect in security clearance.
This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data. First, based on the cylindrical scanning imaging model, the Barker code-based randomly sparse sampling method is employed to reduce the scanning time. Then, a three-dimensional imaging algorithm based on interferometry and compressed sensing in the frequency domain is proposed, with sparse representation of the image in the frequency domain after interferometry and Compressed Sensing measurement model, to recover the image frequency spectrum, thereby implementing human security image reconstruction via sparse sampling. Real data processing results indicated that the proposed method could obtain image resolution and performance similar to those of complete samples and that the image correlation coefficients before and after sparse sampling were better than 0.9, with 50% time/data reduction. Furthermore, based on the Barker codes and multistatic work mode, a sparse array architecture for rapid imaging was designed with a sparse rate of 94.6% and the guarantee of imaging quality. The proposed method was found to considerably increase the passage rate and reduce the amount of radiation unit and system complexity, marking its application significance and market prospect in security clearance.
2018, 7(3): 387-394.
A new Three-Dimensional (3D) scene reconstruction method is proposed to meet the practical application requirement of the active cylindrical scanning millimeter-wave 3D imaging security apparatus. Specifically, the ω-K algorithm is firstly used to realize the decoupling and focusing between the antenna array direction and the range direction, then the synthetic aperture processing between the distance and angle direction is performed to achieve the focusing with the Back Projection (BP) algorithm, realizing the fully 3D scene reconstruction. The echo data processing results of the simulated and measured 3D human model show that this method is theoretical feasible with good practical applicability. Besides, the proposed method could realize the fast and accurate 3D human imaging by CUDA platform and be applicable to the nonideal cylindrical scanning 3D imaging scenarios.
A new Three-Dimensional (3D) scene reconstruction method is proposed to meet the practical application requirement of the active cylindrical scanning millimeter-wave 3D imaging security apparatus. Specifically, the ω-K algorithm is firstly used to realize the decoupling and focusing between the antenna array direction and the range direction, then the synthetic aperture processing between the distance and angle direction is performed to achieve the focusing with the Back Projection (BP) algorithm, realizing the fully 3D scene reconstruction. The echo data processing results of the simulated and measured 3D human model show that this method is theoretical feasible with good practical applicability. Besides, the proposed method could realize the fast and accurate 3D human imaging by CUDA platform and be applicable to the nonideal cylindrical scanning 3D imaging scenarios.
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