Moving Target Detection via Efficient ATI-GoDec Approach for Multichannel SAR System

Li, Jie; Huang, Yan; Liao, Guisheng; Xu, Jingwei

doi:10.1109/lgrs.2016.2584083

Cited by 37 publications

(20 citation statements)

References 11 publications

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“…However, this method failed to track large number of targets in crowded areas. In [3], an efficient ATI-GoDec approach is introduced for detecting the moving targets. But it takes more time to detect the object in video frames.…”

Section: Revised Manuscript Received On October 15 2019mentioning

confidence: 99%

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Rajamanickam*

2019

IJEAT

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Detection And Tracking Of Multiple Moving Objects From A Sequence Of Video Frame And Obtaining Visual Records Of Objects Play An Important Role In The Video Surveillance Systems. Transform And Filtering Technique Designed For Video Pattern Matching And Moving Object Detection, Failed To Handle Large Number Of Objects In Video Frame And Further Needs To Be Optimized. Several Existing Methods Perform Detection And Tracking Of Moving Objects. However, The Performance Efficiency Of The Existing Methods Needs To Be Optimized To Achieve More Robust And Reliable Detection And Tracking Of Moving Objects. In Order To Improve The Pattern Matching Accuracy, A Quantized Kalman Filter-Based Pattern Matching (Qkf-Pm) Technique Is Proposed For Detecting And Tracking Of Moving Objects. The Present Phase Includes Three Functionalities: Top-Down Approach, Kernel Pattern Segment Function And Kalman Filtering. First, The Top-Down Approach Based On Kalman Filtering (Kf) Technique Is Performed To Detect The Chromatic Shadows Of Objects. Next, Kernel Pattern Segment Function Creates The Seed Points For Detecting Moving Object Pattern. Finally, Object Tracking Is Performed Using The Proposed Quantized Kalman Filter Based On The Center Of Seed Point Affinity Feature Values Are Used To Track The Moving Objects In A Particular Region Using The Minimum Bounding Box Approach. Experimental Results Reveals That The Proposed Qkf-Pm Technique Achieves Better Performance In Terms Of True Detection Rate, Pattern Matching Accuracy, Pattern Matching Time, And Object Tracking Accuracy With Respect To The Number Of Video Frames Per Second.

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Section: Revised Manuscript Received On October 15 2019mentioning

confidence: 99%

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Rajamanickam*

2019

IJEAT

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“…A weighting matrix W or a transformation τ can also be used in the constraint of the minimization. In literature, numerous publications used the robust PCA formulation by improving its computational efficiency and its adequacy to the concerned application in (1) signal processing applications like in satellite communication [150], seismology [67], [53], speech enhancement [125] [290], Synthetic-Aperture Radar (SAR) imaging [127], [307], [240], [159], [155], [329], [23], [24], [95] and direction-of-arrival tracking [167], [61], (2) computer vision applications like in image processing, video processing and 3D computer vision as developed in Bouwmans et al [28], (3) computer science applications such as the detection traffic anomalies [191] [216], and (4) astronomy for auroral substorm detection [304] and exoplanet detection [101], [217]. A full list of publications of RPCA in these different applications is available at the DLAM website 1 .…”

Section: Introduction P Rincipal Component Analysis Was Introducedmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

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“…With the increasing degree of spatial freedom, multichannel SAR can work well even at low SCR due to its strong clutter suppression ability. Conventional multichannel SAR-GMTI techniques include space time adaptive processing (STAP) [13], displacement phase center antenna (DPCA) [14,15], along-track interferometry (ATI) [16], and velocity SAR (VSAR) [17]. STAP has excellent performance for suppressing strong stationary clutter in theory.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration

Tang

Tian

2019

Sensors

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It is difficult for multichannel maneuvering synthetic aperture radar (SAR) to achieve ground moving target 2D velocity estimation and refocusing. In this paper, a novel method based on back projection (BP) and velocity SAR (VSAR) is proposed to cope with the issues. First, the static scene is reconstructed by BP to solve the imaging problem of multichannel maneuvering SAR. Then, the static clutter is suppressed, and the range velocity is estimated via VSAR processing. As for azimuth velocity estimation and refocusing, a velocity search method based on velocity-aided BP (VA-BP) and VSAR is proposed to accomplish them. First, each azimuth velocity in the search and the estimated range velocity are used to image the moving target in a small-sized subimage space by VA-BP, i.e., matching the range history and the Doppler phase of the moving target in the image processing. Then, multiple sets of multichannel SAR subimages corresponding to different azimuth velocities are generated, and the clutter of each set of multichannel SAR subimages is also suppressed by VSAR processing. After that, the azimuth velocity is estimated by searching the clutter-suppressed subimage of the first spatial receiving channel in each set of multichannel SAR subimages with the best refocusing quality measured by the minimum entropy. Simulation results show the proposed method can reach high accuracy in moving target 2D velocity estimation and refocusing with the absolute error of 2D velocity estimation smaller than 0.1 m/s.

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Moving Target Detection via Efficient ATI-GoDec Approach for Multichannel SAR System

Cited by 37 publications

References 11 publications

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

On the Applications of Robust PCA in Image and Video Processing

Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration

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