APPROACH OF MULTIPLE MOVING TARGETS DETECTION FOR MICROWAVE SURVEILLANCE SENSORS

2007 ◽  
Vol 04 (01) ◽  
pp. 57-68 ◽  
Author(s):  
WENQIN WANG
Keyword(s):  
Information Acquisition ◽  
Fractional Fourier Transform ◽  
Target Velocity ◽  
Fourier Domain ◽  
Moving Target ◽  
Moving Targets ◽  
True Position ◽  
Simulation Results ◽  
Multiple Moving Targets ◽  
Magnitude Improvement

Multiple moving targets detection is one of the fundamental problems in information acquisition. In this paper, the use of a transformable period and symmetrical linear frequency modulated (TPS-LFM) waveform for microwave surveillance sensor multiple moving targets identification, is proposed. In order to accurately estimate target's true position and velocity, a relatively unknown yet powerful technique, the so-called fractional Fourier transform (FrFT), is applied to estimate the moving target parameters. By mapping a target's signal onto a fractional Fourier axis, the FrFT permits a constant-velocity target to be focused in the fractional Fourier domain thereby affording orders of magnitude improvement in signal-clutter-ratio. Moving target velocity and position parameters are derived and expressed in terms of an optimum fractional angle and a measured fractional Fourier position, allowing a target to be accurately located. Moreover, to resolve the problem whereby weak targets are covered by the sidelobes of strong ones, the CLEAN technique is also applied. Simulation results show that the method is effective in estimating target velocity and position parameters for microwave surveillance sensors.

scholarly journals Searching and Tracking an Unknown Number of Targets: A Learning-Based Method Enhanced with Maps Merging

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1076
Author(s):  
Peng Yan ◽  
Tao Jia ◽  
Chengchao Bai
Keyword(s):  
Moving Targets ◽  
Search Region ◽  
Area Of Interest ◽  
Markov Decision ◽  
Simulation Results ◽  
Average Observation ◽  
High Flexibility ◽  
Multiple Moving Targets ◽  
Partially Observable ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) have been widely used in search and rescue (SAR) missions due to their high flexibility. A key problem in SAR missions is to search and track moving targets in an area of interest. In this paper, we focus on the problem of Cooperative Multi-UAV Observation of Multiple Moving Targets (CMUOMMT). In contrast to the existing literature, we not only optimize the average observation rate of the discovered targets, but we also emphasize the fairness of the observation of the discovered targets and the continuous exploration of the undiscovered targets, under the assumption that the total number of targets is unknown. To achieve this objective, a deep reinforcement learning (DRL)-based method is proposed under the Partially Observable Markov Decision Process (POMDP) framework, where each UAV maintains four observation history maps, and maps from different UAVs within a communication range can be merged to enhance UAVs’ awareness of the environment. A deep convolutional neural network (CNN) is used to process the merged maps and generate the control commands to UAVs. The simulation results show that our policy can enable UAVs to balance between giving the discovered targets a fair observation and exploring the search region compared with other methods.

scholarly journals Moving Target Detection in Multichannel SAR Framework using Adaptive Neuro Fuzzy Decisive Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 4517-4523 ◽  
Keyword(s):  
Target Detection ◽  
Target Location ◽  
Fractional Fourier Transform ◽  
Research Area ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Background Clutter

Precise and efficacious detection of moving targets is a prominent task in on-going synthetic aperture radar (SAR) technique. The perception of moving object allows quite significant data about the situation under observation for both surveillance and intelligence activities. The task of accurately locating moving targets against strong background clutter in minimum of time is of utmost interest in the current research area. Fractional Fourier Transform (FrFT) concentrates the energy of the required chirp signal so that it can be well separated from the chirp like noise. The proposed SAR Moving Target Detection (MTD) process is based on the combination of FrFT with the adaptive-neuro fuzzy decisive technique. The correlation among the received signal and the FrFT of the received signal are computed which maximizes the required signal energy and applied to the adaptive-neuro fuzzy decisive module that detects the target location adaptively using the fuzzy linguistic rules. The simulation is performed by changing the number of targets, different Pulse repetition intervals, antenna turn velocity, iterations and the analysis is carried out based on the metrics, like detection time, missed target rate, and Mean Square Error (MSE), proving that the proposed Adaptive-Neuro Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48.

scholarly journals Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

2020 ◽  
Vol 12 (18) ◽  
pp. 3083 ◽  
Author(s):  
Xiaqing Yang ◽  
Jun Shi ◽  
Yuanyuan Zhou ◽  
Chen Wang ◽  
Yao Hu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Bp Algorithm ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Back Projection ◽  
Moving Targets ◽  
New Approach ◽  
Moving Target Tracking ◽  
Accuracy Requirement ◽  
Multiple Moving Targets

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

scholarly journals Multiple Moving Targets Detection and Parameters Estimation in Strong Reverberation Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ge Yu ◽  
Shengchun Piao
Keyword(s):  
Fractional Fourier Transform ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Estimation Method ◽  
Parameters Estimation ◽  
Significant Feature ◽  
Moving Targets ◽  
Variance Matrix ◽  
Linear Frequency Modulation Signal ◽  
Multiple Moving Targets

This paper considers the problem of multiple moving targets detection and parameters estimation (direction of arrival and range) in strong reverberation environments. As reverberation has a strong correlation with target echo, the performance of target detection and parameters estimation is significantly degraded in practical underwater environments. In this paper, we utilize two uniform circular arrays to receive plane wave of the linear frequency modulation signal reflected from far-field targets. On the basis of received signal, we build a variance matrix of multiple beams by using modal decomposition, conventional beamforming, and fractional Fourier transform (FrFT). We then propose a novel detection method and an estimation method of parameters based on the constructed image. A significant feature of the proposed methods is that our design does not involve any a priori knowledge about targets number and parameters of marine environments. Finally, we demonstrate via numerical simulation examples that the detection probability and the accuracy of estimated parameters of the proposed method are higher than the existing methods in both low signal-to-reverberation ratio and signal-to-noise ratio environment.

scholarly journals Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2377
Author(s):  
Jing Liu ◽  
Xiaoqing Tian ◽  
Jiayuan Jiang ◽  
Kaiyu Huang
Keyword(s):  
Compressed Sensing ◽  
Moving Target ◽  
Moving Targets ◽  
Common Component ◽  
Distributed Compressed Sensing ◽  
Variational Bayesian ◽  
Dual Channel ◽  
Bayesian Approximation ◽  
Multiple Moving Targets ◽  
Ground Moving Target Indication

The dual-channel synthetic aperture radar (SAR) system is widely applied in the field of ground moving-target indication (GMTI). With the increase of the imaging resolution, the resulting substantial raw data samples increase the transmission and storage burden. We tackle the problem by adopting the joint sparsity model 1 (JSM-1) in distributed compressed sensing (DCS) to exploit the correlation between the two channels of the dual-channel SAR system. We propose a novel algorithm, namely the hierarchical variational Bayesian based distributed compressed sensing (HVB-DCS) algorithm for the JSM-1 model, which decouples the common component from the innovation components by applying variational Bayesian approximation. Using the proposed HVB-DCS algorithm in the dual-channel SAR based GMTI (SAR-GMTI) system, we can jointly reconstruct the dual-channel signals, and simultaneously detect the moving targets and stationary clutter, which enables sampling at a further lower rate in azimuth as well as improves the reconstruction accuracy. The simulation and experimental results show that the proposed HVB-DCS algorithm is capable of detecting multiple moving targets while suppressing the clutter at a much lower data rate in azimuth compared with the compressed sensing (CS) and range-Doppler (RD) algorithms.

scholarly journals A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 608 ◽  
Author(s):  
Bong-seok Kim ◽  
Youngseok Jin ◽  
Sangdong Kim ◽  
Jonghun Lee
Keyword(s):  
Fourier Transform ◽  
Continuous Wave ◽  
Low Complexity ◽  
Radar System ◽  
Moving Target ◽  
Moving Targets ◽  
One Dimensional ◽  
Fmcw Radar ◽  
Target Indicator ◽  
Simulation Results

This paper proposes a low-complexity frequency-modulated continuous wave (FMCW) surveillance radar algorithm using random dual chirps in order to overcome the blind-speed problem and reduce the computational complexity. In surveillance radar algorithm, the most widely used moving target indicator (MTI) algorithm is proposed to effectively remove clutter. However, the MTI algorithm has a so-called ‘blind-speed problem’ that cannot detect a target of a specific velocity. In this paper, we try to solve the blind-speed problem of MTI algorithm by randomly selecting two beat signals selected for MTI for each frame. To further reduce the redundant complexity, the proposed algorithm first performs one-dimensional fast Fourier transform (FFT) for range detection and performs multidimensional FFT only when it is determined that a target exists at each frame. The simulation results show that despite low complexity, the proposed algorithm detects moving targets well by avoiding the problem of blind speed. Furthermore, the effectiveness of the proposed algorithm was verified by performing an experiment using the FMCW radar system in a real environment.

scholarly journals Azimuth Multichannel Reconstruction for Moving Targets in Geosynchronous Spaceborne–Airborne Bistatic SAR

2020 ◽  
Vol 12 (11) ◽  
pp. 1703
Author(s):  
Wei Xu ◽  
Zhengbin Wei ◽  
Pingping Huang ◽  
Weixian Tan ◽  
Bo Liu ◽  
...  
Keyword(s):  
Velocity Estimation ◽  
Target Velocity ◽  
Moving Target ◽  
Reconstruction Method ◽  
Moving Targets ◽  
Raw Data ◽  
Stationary Target ◽  
Slant Range ◽  
Geometry Model ◽  
Imaging Results

In a multichannel geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) system, the airborne receiver can obtain high-resolution microwave images with good signal-to-noise ratios (SNRs) by passively receiving echoes from the desired area. Since the Doppler modulation and range history of a moving target are obviously different from a stationary target, a signal geometry model for moving targets in multichannel GEO-SA-BiSAR is established in this paper. According to simulation results, the along track velocity introduces target defocusing in azimuth, and the slant range velocity mainly causes multiple false targets. To resolve these problems, a modified multichannel reconstruction method in azimuth channel GEO-SA-BiSAR is proposed according to the azimuth multichannel impulse response of the imaged moving target. Before azimuth multichannel raw data combination, both spatial-variant range cell migration correction (RCMC) and azimuth nonlinear chirp scaling (ANLCS) should be performed to reduce the influence of the range offset and lower the Doppler bandwidth of the whole raw data, respectively. Afterward, a novel azimuth multichannel reconstruction algorithm is carried out via the modified reconstruction matrix based on the estimated target velocity. The target slant range velocity estimation is implemented by introducing the signal intensity ratio (SIR). Compared with the conventional method for the stationary target to handle the raw data of the moving target, the false targets could be obviously suppressed by using the proposed approach. Imaging results on both simulated point and distributed scene targets validate the proposed multichannel reconstruction approach.

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.

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