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For Synthetic Aperture Radar (SAR) images, traditional super-resolution methods heavily rely on the artificial design of visual features, and super-reconstruction algorithms based on general Convolutional Neural Network (CNN) have poor fidelity to the target edge contour and weak reconstruction ability to small targets. Aiming at the above problems, in this paper, a Dilated-Resnet CNN (DR-CNN) super-resolution model based on feature reuse, i.e., Feature Reuse Dilated-Resnet CNN (FRDR-CNN), is proposed and perceptual loss is introduced, which accurately realizes four times the semantic super-resolution of SAR images. To increase the receptive field, a DR-CNN structure is used to limit the serious loss of the feature map’s resolution in the model, improving the sensitivity to tiny details. To maximize the utilization of features at different levels, the FRDR-CNN cascades the feature maps of different levels, which greatly improves the efficiency of the feature extraction module and further improves the super-resolution accuracy. With the introduction of the perceptual loss, this method has a superior performance in recovering image texture and edge information. Experimental results of the study show that the FRDR-CNN algorithm is more capable of providing small objects’ super-resolution and more accurate in the visual reconstruction of contour details, compared with traditional algorithms and several popular CNN super-resolution algorithms. Objectively, the Peak Signal to Noise Ratio(PSNR) is 33.5023 and Structural SIMilarity index(SSIM) is 0.5127, and the Edge Preservation Degreebased on the Ratio Of Average(EPD-ROA) is 0.4243 and 0.4373 in the horizontal and vertical directions, respectively. For Synthetic Aperture Radar (SAR) images, traditional super-resolution methods heavily rely on the artificial design of visual features, and super-reconstruction algorithms based on general Convolutional Neural Network (CNN) have poor fidelity to the target edge contour and weak reconstruction ability to small targets. Aiming at the above problems, in this paper, a Dilated-Resnet CNN (DR-CNN) super-resolution model based on feature reuse, i.e., Feature Reuse Dilated-Resnet CNN (FRDR-CNN), is proposed and perceptual loss is introduced, which accurately realizes four times the semantic super-resolution of SAR images. To increase the receptive field, a DR-CNN structure is used to limit the serious loss of the feature map’s resolution in the model, improving the sensitivity to tiny details. To maximize the utilization of features at different levels, the FRDR-CNN cascades the feature maps of different levels, which greatly improves the efficiency of the feature extraction module and further improves the super-resolution accuracy. With the introduction of the perceptual loss, this method has a superior performance in recovering image texture and edge information. Experimental results of the study show that the FRDR-CNN algorithm is more capable of providing small objects’ super-resolution and more accurate in the visual reconstruction of contour details, compared with traditional algorithms and several popular CNN super-resolution algorithms. Objectively, the Peak Signal to Noise Ratio(PSNR) is 33.5023 and Structural SIMilarity index(SSIM) is 0.5127, and the Edge Preservation Degreebased on the Ratio Of Average(EPD-ROA) is 0.4243 and 0.4373 in the horizontal and vertical directions, respectively.
Ground-based radar is a microwave remote sensing imaging technology which has been gradually developed and matured in the past 20 years. At present, it has been widely used in the monitoring of geological disasters such as landslide and collapse. Ground-based radars can detect micro-variation in target areas through the principle of interferometry. However, due to human factors, geological factors, meteorological factors, etc., the radar image of the monitoring area is seriously incoherence, which brings great difficulty to long-term quantitative monitoring. Therefore, it is urgent to further develop the application of change detection on the basis of quantitative monitoring, so as to provide effective information for a long-term and comprehensive understanding of the dynamic changes in the monitoring area. To solve the above problems, an unsupervised change detection of ground-based radar images based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the NonSubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, Principal Component Analysis (PCA) is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. Ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitoring area affected by precipitation and other factors. This method is used to detect the change of radar image before and after the landslide. The results show that the method in this paper is easier to cluster and segment, and the change detection results can significantly reduce the noise points while retaining the change area. Ground-based radar is a microwave remote sensing imaging technology which has been gradually developed and matured in the past 20 years. At present, it has been widely used in the monitoring of geological disasters such as landslide and collapse. Ground-based radars can detect micro-variation in target areas through the principle of interferometry. However, due to human factors, geological factors, meteorological factors, etc., the radar image of the monitoring area is seriously incoherence, which brings great difficulty to long-term quantitative monitoring. Therefore, it is urgent to further develop the application of change detection on the basis of quantitative monitoring, so as to provide effective information for a long-term and comprehensive understanding of the dynamic changes in the monitoring area. To solve the above problems, an unsupervised change detection of ground-based radar images based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the NonSubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, Principal Component Analysis (PCA) is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. Ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitoring area affected by precipitation and other factors. This method is used to detect the change of radar image before and after the landslide. The results show that the method in this paper is easier to cluster and segment, and the change detection results can significantly reduce the noise points while retaining the change area.
The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments. The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments.
A low pulse repletion frequency is required and an ambiguous Doppler spectrum should be considered to obtain a high-resolution and wide-swath Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) system in the azimuth direction. In this study, we have proposed a novel clutter suppression approach, where the Doppler spectrum of the single channel echo is ambiguous, with respect to the space-borne multiple channels in an azimuth high-resolution and wide-swath SAR-GMTI system. Initially, the azimuth deramping operation is utilized to compress the ambiguous Doppler spectrum, where the signal of clutter or the moving target is focused toward only some azimuth Doppler-frequency bins. Further, a covariance matrix corresponding to the clutter is estimated in the azimuth deramping and range compression domain. Subsequently, the matrix eigenvalue decomposition technique is employed to obtain an eigenvector corresponding to the minimum eigenvalue. Herein, we intend to achieve redundant channel freedom to ensure the suppression of clutter. The obtained eigenvector can be considered to be the orthogonality vector of the clutter space, which denotes the orthogonality with respect to the signal space vector. Hence, we adopt the obtained eigenvector to appropriately suppress the clutter. Simultaneously, the signal of the moving target can be appropriately preserved. Finally, some experiments are conducted to validate the proposed clutter suppression approach. A low pulse repletion frequency is required and an ambiguous Doppler spectrum should be considered to obtain a high-resolution and wide-swath Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) system in the azimuth direction. In this study, we have proposed a novel clutter suppression approach, where the Doppler spectrum of the single channel echo is ambiguous, with respect to the space-borne multiple channels in an azimuth high-resolution and wide-swath SAR-GMTI system. Initially, the azimuth deramping operation is utilized to compress the ambiguous Doppler spectrum, where the signal of clutter or the moving target is focused toward only some azimuth Doppler-frequency bins. Further, a covariance matrix corresponding to the clutter is estimated in the azimuth deramping and range compression domain. Subsequently, the matrix eigenvalue decomposition technique is employed to obtain an eigenvector corresponding to the minimum eigenvalue. Herein, we intend to achieve redundant channel freedom to ensure the suppression of clutter. The obtained eigenvector can be considered to be the orthogonality vector of the clutter space, which denotes the orthogonality with respect to the signal space vector. Hence, we adopt the obtained eigenvector to appropriately suppress the clutter. Simultaneously, the signal of the moving target can be appropriately preserved. Finally, some experiments are conducted to validate the proposed clutter suppression approach.
As a frequency-domain algorithm for Synthetic Aperture Radar (SAR) imaging, the Range Migration Algorithm (RMA) can theoretically achieve optimal performance. However, because its Stolt mapping is performed using pixel-by-pixel convolution, the computational efficiency of RMA is inadequate for massive SAR data processing requirements. In this paper, we propose a modified RMA based on the Principle of Chirp Scaling (PCS). First, SAR echo data is divided along the range direction, and the subswath signal is compensated by the second-order range-azimuth coupling term and high-order terms at the reference distance. Then, the nonlinear Stolt mapping is modified to become linear. Finally, Stolt interpolation is realized using PCS to efficiently resample the processed data. Demonstrating both well-focused performance and high computational efficiency, the proposed PCS-RMA employs only fast Fourier transforms and complex vector multiplication operations to achieve modified Stolt mapping. The processing results of several simulation data and X-band-measured airborne SAR data with a pulse bandwidth of 1.2 GHz verify the effectiveness of the proposed algorithm. The proposed algorithm can also be employed for the rapid processing of missile-borne, spaceborne, and drone-borne SAR data. As a frequency-domain algorithm for Synthetic Aperture Radar (SAR) imaging, the Range Migration Algorithm (RMA) can theoretically achieve optimal performance. However, because its Stolt mapping is performed using pixel-by-pixel convolution, the computational efficiency of RMA is inadequate for massive SAR data processing requirements. In this paper, we propose a modified RMA based on the Principle of Chirp Scaling (PCS). First, SAR echo data is divided along the range direction, and the subswath signal is compensated by the second-order range-azimuth coupling term and high-order terms at the reference distance. Then, the nonlinear Stolt mapping is modified to become linear. Finally, Stolt interpolation is realized using PCS to efficiently resample the processed data. Demonstrating both well-focused performance and high computational efficiency, the proposed PCS-RMA employs only fast Fourier transforms and complex vector multiplication operations to achieve modified Stolt mapping. The processing results of several simulation data and X-band-measured airborne SAR data with a pulse bandwidth of 1.2 GHz verify the effectiveness of the proposed algorithm. The proposed algorithm can also be employed for the rapid processing of missile-borne, spaceborne, and drone-borne SAR data.
Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results. Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results.
The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm. The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm.
With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed. With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on convolutional neural networks. By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed.
In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance. In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.
The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter. The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter.
To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed. To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed.
Compact Polarimetric (CP) mode is a new dual-pol mode introduced in the last decade. The main current CP mode transmits circular polarized waves. Data in the form of Stokes parameters obtained by this mode has rotational invariance. In real engineering applications, transmit distortions in all dual-pol modes, including the CP mode, cannot be directly compensated with external calibration methods. Therefore, it is necessary to analysis the influences caused by transmit distortions. Until now, the Maximum Normalized Error (MNE) parameter has already been proposed by existing researches to analyze polarimetric quality of the Polarimetric SAR (PolSAR) system. This paper has proposed an analysis method to analysis the influence of transmit distortions in polarimetric modes with circular polarimetric wave in transmission, based on the Axial Ratio (AR) parameter of real transmitted wave. Firstly, this paper has analyzed the influence of different transmit distortion sources to AR parameter with simulations. Meanwhile, this part has also demonstrated the influence of same distortion sources to the MNE parameter. Through comparison of this two results, this paper has concluded three advantages of the AR parameter over the MNE parameter. At last, the effectiveness of the proposed evaluation method has been verified using real measured GF-3 distortion data and test data obtained by experimental system, which transmit circular polarized waves. Compact Polarimetric (CP) mode is a new dual-pol mode introduced in the last decade. The main current CP mode transmits circular polarized waves. Data in the form of Stokes parameters obtained by this mode has rotational invariance. In real engineering applications, transmit distortions in all dual-pol modes, including the CP mode, cannot be directly compensated with external calibration methods. Therefore, it is necessary to analysis the influences caused by transmit distortions. Until now, the Maximum Normalized Error (MNE) parameter has already been proposed by existing researches to analyze polarimetric quality of the Polarimetric SAR (PolSAR) system. This paper has proposed an analysis method to analysis the influence of transmit distortions in polarimetric modes with circular polarimetric wave in transmission, based on the Axial Ratio (AR) parameter of real transmitted wave. Firstly, this paper has analyzed the influence of different transmit distortion sources to AR parameter with simulations. Meanwhile, this part has also demonstrated the influence of same distortion sources to the MNE parameter. Through comparison of this two results, this paper has concluded three advantages of the AR parameter over the MNE parameter. At last, the effectiveness of the proposed evaluation method has been verified using real measured GF-3 distortion data and test data obtained by experimental system, which transmit circular polarized waves.
The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment. The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment.
This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar. This article presents experimental results of target detection using a miniaturized multichannel passive radar system that exploits Long Term Evolution (LTE) signals. First, the advantages of LTE signals are discussed with respect to their ambiguity function. Second, both system design and field experiments are introduced. Finally, agreements between different targets and their truth obtained in the results prove the technical feasibility of using LTE signals for detecting ground and low-altitude targets via field experiments, thus forming the basis for further development of LTE-based passive radar.