scholarly journals Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3177 ◽  
Author(s):  
Shitao Zhu ◽  
Xiaoming Chen ◽  
Xuehan Pan ◽  
Xiaoli Dong ◽  
Hongyu Shi ◽  
...  
Keyword(s):  
Power Factor ◽  
High Probability ◽  
Matched Filter ◽  
Urban Environments ◽  
Moving Target ◽  
Moving Targets ◽  
Coherent Accumulation ◽  
Interference Noise ◽  
Multiple Measurements ◽  
Directional Difference

In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation. The amplitude coherent accumulation technique that extracts the coherent amplitude information of echoes after being processed by the SMF, is used to improve the SINR based on multiple measurements. Finally, the bi-directional difference technique is proposed to distinguish the target echoes and the interference/noise. Simulations and experiments are conducted to validate and demonstrate that small moving targets can be detected with high probability using the proposed method in urban environments, even with just one measurement.

scholarly journals A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

2020 ◽  
Vol 12 (24) ◽  
pp. 4017
Author(s):  
Chong Song ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Zhongbin Wang ◽  
Weidi Xu ◽  
...  
Keyword(s):  
Matched Filter ◽  
Training Data ◽  
Moving Target ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Practical Applications ◽  
Signal Environment ◽  
Auto Regressive ◽  
Space Time Adaptive Processing ◽  
Adaptive Matched Filter

The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.

scholarly journals High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

2021 ◽  
Vol 10 (4) ◽  
pp. 234
Author(s):  
Jing Ding ◽  
Zhigang Yan ◽  
Xuchen We
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Target Localization ◽  
Parallel Structure ◽  
Moving Target ◽  
Target Area ◽  
Moving Targets ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.

scholarly journals Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1478
Author(s):  
Chong Song ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Wei Li
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Moving Target ◽  
Ratio Test ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Sar Images ◽  
Steering Vector ◽  
Current Cell ◽  
Data Vector

A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.

Researching a Moving Target Detection Method Based on Magnetic Flux Induction Technology

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1967
Author(s):  
Chaoqun Xu ◽  
Li Yang ◽  
Kui Huang ◽  
Yang Gao ◽  
Shaohua Zhang ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Dipole ◽  
Target Detection ◽  
Electromotive Force ◽  
Detection Method ◽  
Field Tests ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Modern Warfare

The ocean is a very important arena in modern warfare where all marine powers deploy their military forces. Due to the complex environment of the ocean, underwater equipment has become a very threatening means of surprise attack in modern warfare. Therefore, the timely and effective detection of underwater moving targets is the key to obtaining warfare advantages and has important strategic significance for national security. In this paper, magnetic flux induction technology was studied with regard to the difficulty of detecting underwater concealed moving targets. Firstly, the characteristics of a magnetic target were analyzed and an equivalent magnetic dipole model was established. Secondly, the structure of the rectangular induction coil was designed according to the model, and the relationship between the target’s magnetism and the detection signal was deduced. The variation curves of the magnetic flux and the electromotive force induced in the coil were calculated by using the numerical simulation method, and the effects of the different motion parameters of the magnetic dipole and the size parameters of the coil on the induced electromotive force were analyzed. Finally, combined with the wavelet threshold filter, a series of field tests were carried out using ships of different materials in shallow water in order to verify the moving target detection method based on magnetic flux induction technology. The results showed that this method has an obvious response to moving targets and can effectively capture target signals, which verifies the feasibility of the magnetic flux induction detection technology.

scholarly journals Fixed Echo Rejection in Sodar Using Noncoherent Matched Filter Detection and Gaussian Mixture Model–Based Postprocessing

2019 ◽  
Vol 36 (1) ◽  
pp. 3-16
Author(s):  
Paul Kendrick ◽  
Sabine von Hünerbein
Keyword(s):  
Radial Velocity ◽  
Matched Filter ◽  
Gaussian Mixture ◽  
Urban Environments ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Chirp Pulse ◽  
Signal Processing Algorithm ◽  
Doppler Sodar ◽  
Wind Profiles

AbstractDoppler sodar is a technology used for acoustic-based remote sensing of the lower planetary boundary layer. Sodars are often used to measure wind profiles; however, they suffer from problems caused by noise (both acoustic and electrical) and echoes from fixed objects, which can bias radial velocity estimates. An experimental bistatic sodar was developed with 64 independent channels. The device enables flexible beamforming; beams can be tilted at the same angle irrelevant of frequency, a limitation in most commercial devices. This paper presents an alternative sodar signal-processing algorithm for wind profiling using a multifrequency stepped-chirp pulse. A noncoherent matched filter was used to analyze returned signals. The noncoherent matched filter combines radial velocity estimates from multiple frequencies into a single optimization. To identify and separate sources of backscatter, noise, and fixed echoes, a stochastic pattern-recognition technique, Gaussian mixture modeling, was used to postprocess the noncoherent matched filter data. This method allowed the identification and separation of different stochastic processes. After identification, noise and fixed echo components were removed and a clean wind profile was produced. This technique was compared with traditional spectrum-based radial velocity estimation methods, and an improvement in the rejection of fixed echo components was demonstrated; this is one of the major limitations of sodar performance when located in complex terrain and urban environments.

scholarly journals Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

2021 ◽  
Vol 13 (19) ◽  
pp. 3855
Author(s):  
Yulun Li ◽  
Chunsheng Li ◽  
Xiaodong Peng ◽  
Shuo Li ◽  
Hongcheng Zeng ◽  
...  
Keyword(s):  
Image Sequences ◽  
Moving Target ◽  
Ratio Test ◽  
Moving Targets ◽  
Low Resolution ◽  
Motion Signal ◽  
Resolution Imaging ◽  
Noise Statistics ◽  
The Cost ◽  
Area Monitoring

Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

2021 ◽  
Author(s):  
M. Bharat Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Fuzzy System ◽  
Deep Convolutional Neural Network ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Deep Cnn

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

scholarly journals A Method of Marine Moving Targets Detection in Multi-Channel ScanSAR System

2020 ◽  
Vol 12 (22) ◽  
pp. 3792
Author(s):  
Junying Yang ◽  
Xiaolan Qiu ◽  
Mingyang Shang ◽  
Lihua Zhong ◽  
Chibiao Ding
Keyword(s):  
Spatial Distribution ◽  
Reference Value ◽  
Sparse Recovery ◽  
Velocity Estimation ◽  
High Signal ◽  
Moving Target ◽  
Moving Targets ◽  
Position Information ◽  
Wide Area Monitoring ◽  
Area Monitoring

Azimuth multi-channel Synthetic Aperture Radar (SAR) system operated in burst mode makes high-resolution ultrawide-swath (HRUS) imaging become a reality. This kind of imaging mode has excellent application value for the maritime scenarios requiring wide-area monitoring. This paper suggests a moving target detection (MTD) method of marine scenes based on sparse recovery, which integrates detection, velocity estimation, and relocation. Firstly, the typical phenomenon of scene folding in the coarse-focused domain is introduced in detail. Given that the spatial distribution of moving vessels is highly sparse, the idea of sparse recovery is utilized to acquire the azimuth time characterizing the position of the moving target reasonably. Subsequently, the radial velocity and position information about the targets are obtained simultaneously. What makes the proposed method effective are two characteristics of the moving targets in ocean scenes, high signal-to-clutter ratio (SCR) and sparsity of the spatial distribution. Then, estimation performances under different SCR are analyzed by Monte Carlo experiments. And the actual SCR of the vessels in the ocean scene obtained by GaoFen-3 dual-receive channel mode is invoked as a reference value to verify the effectiveness. Besides, some simulation experiments demonstrate the capability to indicate marine moving targets.

Use of Visual Feedback during Rapid Aiming at a Moving Target

1993 ◽  
Vol 76 (2) ◽  
pp. 690-690 ◽  
Author(s):  
Digby Elliott
Keyword(s):  
Visual Feedback ◽  
Moving Target ◽  
Moving Targets

Performance of 10 subjects aiming at moving targets, with and without vision, indicated that visual feedback can be processed in approximately 100 msec.

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