Ground Moving Target Detection Research Articles

Ground Moving Target Detection with Adaptive Data Reconstruction and Improved Pseudo-Skeleton Decomposition

Ground Moving Target Detection with Adaptive Data Reconstruction and Improved Pseudo-Skeleton Decomposition

DPCA-Based Doppler Radar Measurements from Space: Effect of System Errors on Velocity Estimation Performance

Abstract The displaced phased center antenna (DPCA) method of clutter cancellation for ground moving target detection from airborne platforms has been in use for a number of decades. Application of the DPCA method for spaceborne Doppler weather radar velocity estimation was suggested in 2007. The initial description and analysis of the technique was followed several years ago by demonstration using a multiantenna airborne radar. Recent reviews of methods and technology for spaceborne cloud and precipitation radar have also mentioned possible use of DPCA. However, to date, analyses of the application of DPCA to spaceborne Doppler weather radar have assumed that the two channels and antennas are identical, including perfect alignment, and that the DPCA condition is well-satisfied. This study uses simulation to examine the effects of relaxing these assumptions. The simulation method and its validation are discussed, with companion analytical calculations in the appendix. Next, simulations are used to show the effects on the Doppler estimates from errors in pointing and positioning relative to the ideal DPCA. The DPCA technique is relatively robust to possible errors, indicating that a practical DPCA radar system can provide precise Doppler measurements from space. Significance Statement Analytical and simulation results show that the displaced phase center antenna approach can enable spaceborne atmospheric Doppler radar measurements with good accuracy, even in the presence of antenna mispointing and other system errors.

Ground Moving Target Detection With Nonuniform Subpulse Coding in SAR System

For the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, the increasing imaging width results in a serious range ambiguity problem, which affects the performance of ground moving target indication (GMTI). In this article, a novel nonuniform subpulse coding (NSPC) scheme is proposed. It is characterized by resorting to range-frequency band resources and detailed coding design for each subpulse, enabling the beam auto-scanning in elevation. Also, the bandpass filtering and digital beamforming (DBF) technology with improved data reconstruction are utilized to realize the separation of subpulses and suppress range ambiguity. The NSPC technique exchanges the signal bandwidth for increasing swath without range ambiguity, and the coded subpulses can be directed to the prescribed regions, while skipping the invalid areas where the echoes are blocked. After that, through the robust principal component analysis (RPCA) method, the moving target detection is performed for each separated region without residual range-ambiguous interference. The proposed approach has been theoretically deduced in detail and the simulation experiments demonstrate its effectiveness.

A New Method of Video SAR Ground Moving Target Detection and Tracking Based on the Interframe Amplitude Temporal Curve

A New Method of Video SAR Ground Moving Target Detection and Tracking Based on the Interframe Amplitude Temporal Curve

A Multichannel SAR Ground Moving Target Detection Algorithm Based on Subdomain Adaptive Residual Network

A Multichannel SAR Ground Moving Target Detection Algorithm Based on Subdomain Adaptive Residual Network

A Ground Moving Target Detection Method for Seismic and Sound Sensor Based on Evolutionary Neural Networks

The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption of unattended ground sensors needs to be reduced to achieve low-power consumption, this paper proposes a ground moving target detection method based on evolutionary neural networks. The technique achieves the selection of feature extraction methods and the design of evolving neural network structures. The experimental results show that the improved model can achieve high recognition accuracy with a smaller feature vector and lower network complexity.

Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.

Ground Moving Target Detection and Estimation for Airborne Multichannel Radar Based on Coherent Difference Processing

Ground moving targets with slow velocity and low radar cross-section (RCS) are usually embedded in the clutter Doppler spectrum. To achieve the detection and estimation of such targets, a novel method operating in the range-Doppler domain is developed for airborne multichannel radar systems. The interferometric phases that are sensitive to moving targets are obtained by coherent difference processing (CDP) for target detection. Moreover, the amplitude is utilized as complementary information to improve the detection performance. Then, a matched filter bank is designed and applied to the CDP processed data to complete the parameter estimation. The proposed method provides the benefits of high efficiency and robustness, since it does not involve matrix inversion, and it does not require homogeneous clutter assumption unlike adaptive algorithms. Experiments on real data acquired by an airborne X-band four-channel radar system demonstrate its effectiveness.

Robust ground moving target detection for airborne radar using a novel feature-based machine learning approach

A novel ground-moving target detection method is introduced using a distinguishing target, and clutter feature for airborne radar. The clutter proximity feature is extracted based on the Euclidean distance between a signal pixel and the expected clutter ridge in the angle-Doppler domain. Subsequently, target and clutter pixels are classified based on the extracted features for target detection without actually removing clutters or clutter estimation. The proposed technique is especially suitable for effective airborne radar target detection in the unknown ground clutter. The experimental results have validated the effectiveness of the new approach, which enables ground moving target detection in inhomogeneous clutter.

Single-Channel Circular SAR Ground Moving Target Detection Based on LRSD and Adaptive Threshold Detector

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

Single Channel SAR Ground Moving Target Detection Algorithm Based on Subband Interferometry

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

Ground Moving Target Detection With Seismic Fractal Features

Due to the strong nonstationary characteristics of seismic signals, energy criteria-based methods are not robust for detecting moving targets, especially in data with low SNRs. To address this problem, we propose a new method for detecting ground moving target based on fractal dimension (FD) theory named FD-based support vector machine (FD-SVM). In this method, seismic signals are first measured by fractals, which can effectively extract seismic nonlinear features. These fractal features are then fed into an SVM to distinguish moving targets from noise. Two data sets are used to evaluate the proposed method. One is a set of seismic signals induced by wheeled and tracked vehicles. The other is a set of seismic signals generated by human footsteps. Experimental results demonstrate that the proposed FD-SVM algorithm achieves promising results on both data sets. Compared with the benchmark methods, the FD-SVM algorithm achieves a better precision rate, recall rate, and F1 score.

A joint clutter suppression method based on robust principal component analysis and total variation denoising

In the scene of ground moving target detection, the target echo information is often corrupted by strong clutter. In this letter, a joint clutter suppression algorithm is proposed based on robust principal component analysis and total variation denoising for pulse Doppler radar. The novel method named total variation- robust principal component analysis replaces the l1-norm in robust principal component analysis algorithm with a total variation seminorm. Total variation robust principal component analysis retains the clutter suppression ability of robust principal component analysis, and effectively separates the target from the noise in range-Doppler domain.

Real-Time Performance Comparison of Vision-Based Autonomous Landing of Quadcopter on a Ground Moving Target

The tremendous applications of unmanned aerial vehicles (UAVs), such as inspection in complex environments, search, and rescue missions, have established this area of research. The domain of UAVs autonomous navigation and landing has received considerable amount of interest of robotics researchers in the past decades. Nevertheless, the limited capability of UAVs for autonomous landing severely hampers the use of aerial vehicles specially in GPS-denied areas. Fortunately, with the rapid development of computer vision, vision-based techniques have become an important and cheap tool to be used in UAVs for moving target detection and landing. In this paper, four major vision-based techniques namely PID controller, fuzzy logic, sliding mode control and model predictive control have been investigated and their landing performance is compared to each other. These landing techniques are implemented on a quadcopter for the purpose of ground moving target detection and then landing on it. The Raspberry Pi board with two Pi cameras for 3D scene construction and depth estimation along with ultrasonic sensor have been used in the quadcopter. A wheeled mobile robot with landing platform is taken as the target. The landing performance of different techniques has been observed and compared in terms of landing displacement error, time taken and the distance travelled to finish the operation in an open and obstacle-free environment. The simulation results are further verified by the hardware experiments.

Moving Target Shadow Analysis and Detection for ViSAR Imagery

The video synthetic aperture radar (ViSAR) is a new application in radar techniques. ViSAR provides high- or moderate-resolution SAR images with a faster frame rate, which permits the detection of the dynamic changes in the interested area. A moving target with moderate velocity can be detected by shadow detection in ViSAR. This paper analyses the frame rate and the shadow feature, discusses the velocity limitation of ViSAR moving target shadow detection and quantitatively gives the expression of velocity limitation. Furthermore, a fast factorized back projection (FFBP) based SAR video formation method and a shadow-based ground moving target detection method are proposed to generate SAR videos and detect the moving target shadow. The experimental results with simulated data prove the validity and feasibility of the proposed quantitative analysis and the proposed methods.

The Technology of Moving Target Detection and Tracking Based on Sequential SAR Image

Although the multi-channel SAR-GMTI technology can be used in ground moving target detection, but only can get trace point of the ground moving target, the ground moving target tracking and track reconstruction can’t be achieved. This article presents a new method of ground moving target detection and tracking which is based on sequence SAR images. Firstly, the moving target detection and velocity estimation can be realized using the method of multi-channel processing. Secondly, the location of moving target can be estimated accurately using a high accuracy target location method based on knowledge-aid. The method uses the matching and target tracking technique based on sequential SAR images to realize the tracking and track reconstruction of moving target. Next, the image reference locking technology is used to achieve accurate image registration for a series of sequential SAR images obtained. Finally, the moving target can be tracked. The results of measured data shows the validity of the presented method.

Range-Ambiguous Clutter Suppression for the SAR-GMTI System Based on Extended Azimuth Phase Coding

A range-ambiguous clutter suppression approach based on extended azimuth phase coding (EAPC) is proposed to handle the range ambiguity of the multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system for ground moving target indication (GMTI) application. The echoes from different ambiguous range regions can be well separated in the transmit spatial frequency domain by properly designing the EAPC shift factor. In the sequel, a set of transmit filters are employed to extract the echoes of each ambiguous region independently. Then the azimuth deramp operation is applied to the extracted data to focus the target energy of the desired region while the residual target energy of other regions is still smeared due to the mismatched reference function. After this, the adaptive matched filtering algorithm is adopted to suppress the clutter and detect the moving target. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for range-ambiguous clutter suppression and ground moving target detection.

Erratum to “Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing” [Mar 20 2143-2154

We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.

Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing

We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.

SAR ground moving target's along‐track velocity estimation in the complex image domain via SoWVD

A ground moving target is always defocused in the synthetic aperture radar (SAR) image domain due to the along-track velocity. In the image domain, a moving target's complex response along the azimuth direction can be modelled as a linear frequency-modulated signal, therefore, its Doppler rate, as well as the along-track velocity, can be estimated based on the recently proposed second-order Wigner–Ville (SoWVD) distribution transform. The proposed method has a low computational complexity without the search procedure and can be well combined with the SAR ground moving target detection algorithms in image domain, where the signal to clutter-plus-noise ratio is higher than other domains. Finally, the effectiveness of this method is verified with some numerical experiments.