A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing

Wang Xue-Jun

doi:doi:10.11648/j.jeee.20160402.14

| Peer-Reviewed

A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing

Wang Xue-Jun

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)

Received: 6 April 2016 Published: 7 April 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.

Published in	Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)
DOI	10.11648/j.jeee.20160402.14
Page(s)	24-30
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Compressed Sensing (CS), Pulse-Doppler (PD) Radar, Target Tracking, Orthogonal Matching Pursuit (OMP), Sparsity Adaptive Matching Pursuit (SAMP) Algorithm

References

[1]	Joel A. Tropp, Anna C. Gilbert. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit [J]. IEEE Transactions on Information Theory, 2007. Vol. 53, No. 12. 4655-4666.
[2]	Thong T. Do, Lu Gan, et al. Sparsity Adaptive Matching Pursuit Algorithm for Practical Compressed Sensing [C]. Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2008, 581-587.
[3]	Dai Qionghai, Fu changjun, et al. Compressed Sensing Research [J]. Chinese Journal of Computers, 2001. Vol. 34, No. 3. 428-434.
[4]	Emmanuel J. Candes, Terence Tao. Decoding by Linear Programming. IEEE Transactions on Information Theory, 2005, Vol. 51, No. 12. 4203-4215.
[5]	Ye Linmei. Compressed Sensing-Based Radar Signal Processing Applications [D]. Xiamen: Xiamen University, 2014.
[6]	J Liu, DQ Han, CZ Han, et al. Adaptive compressed sensing based joint detection and tracking algorithm for airborne radars with high resolution. In: International Conference on Information Fusion, 2014, 1-8.
[7]	M Miller, J Hinze, M Saquib, et al. Adjustable Transmitter Spacing for MIMO Radar Imaging With Compressed Sensing [J]. IEEE Sensors Journal, 2015. Vol. 15, No. 11. 6671-6677.
[8]	Z Liu, X Wei, X Li. Aliasing-Free Moving Target Detection in Random Pulse Repetition Interval Radar Based on Compressed Sensing [J]. IEEE Sensors Journal, 2013. Vol. 13, No. 7. 2523-2534.
[9]	Gou Yonggang, Pi Yiming, et al. Target Tracking System Simulation of Airborne Pulse Doppler Radar [J]. Journal of Chinese Electronics Science Institute, 2009, Vol. 4, No. 1. 82-85.
[10]	Han Fang. Signal Application Research of Pulse Doppler Radar [D]. Haerbin: Harbin Engineering University, 2007.
[11]	Spatyabrata Sen, Arye Nehorai. Sparsity-based Multi-Target Tracking Using OFDM Radar [J]. IEEE Transactions on signal processing, 2011. Vol. 59, No. 4. 1902-1906.
[12]	Phani Chavali, A Low-Complexity Sparsity-Based Multi-Target Tracking Algorithm for Urban Environments [A]. IEEE Radar Conference, 2011, 309-314.
[13]	Yang Jun, Zhang Qun, et al. Compressed sensing-based Multi-Target Tracking Using Cognitive Radar [J]. Journal of Radar, 2014.
[14]	Baraniuk R G. Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 2007. Vol. 24, No. 4. 118-121.
[15]	Jacques L, Hammond DK, et al. Dequantizing compressed sensing: When oversampling and non-gaussian constraints combine. IEEE Transactions on Information Theory, 2011. Vol. 57, No. 1. 559-571.
[16]	Shi Guangming, Liu Danhua, et al. Compressed Sensing Theory and It’s Theory Development [J]. Electronics Journals, 2009. Vol. 37, No. 5. 1072-1080.
[17]	Li Fanghua. Compressed Sensing-based Target Location Algorithm by Radar [D]. Changsha: Hunan University, 2012.
[18]	Satyabrata Sen. Adaptive OFDM Radar for Target Detection and Tracking [D]. St. Louis: Washington University, 2010.
[19]	Tong T Do, Lu Gan, et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In: Proc of the 42nd Asilomar Conference on Signals, Systems and Computers. Pacific Grove, 2008, 581-587.
[20]	PB Tuuk, SL Marple. Compressed sensing radar amid noise and clutter using interference covariance information [J]. IEEE transactions on Aerospace & Electronic Systems, 2014. Vol. 50, No. 50. 887-897.

Cite This Article

Plain Text BibTeX RIS

APA Style

Wang Xue-Jun. (2016). A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. Journal of Electrical and Electronic Engineering, 4(2), 24-30. https://doi.org/10.11648/j.jeee.20160402.14

Copy | Download

ACS Style

Wang Xue-Jun. A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. J. Electr. Electron. Eng. 2016, 4(2), 24-30. doi: 10.11648/j.jeee.20160402.14

Copy | Download

AMA Style

Wang Xue-Jun. A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. J Electr Electron Eng. 2016;4(2):24-30. doi: 10.11648/j.jeee.20160402.14

Copy | Download

@article{10.11648/j.jeee.20160402.14,
  author = {Wang Xue-Jun},
  title = {A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {2},
  pages = {24-30},
  doi = {10.11648/j.jeee.20160402.14},
  url = {https://doi.org/10.11648/j.jeee.20160402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160402.14},
  abstract = {In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing
AU  - Wang Xue-Jun
Y1  - 2016/04/07
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160402.14
DO  - 10.11648/j.jeee.20160402.14
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160402.14
AB  - In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Wang Xue-Jun

School of Electronic Information Engineering, Beihang University, Beijing, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Wang Xue-Jun. (2016). A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. Journal of Electrical and Electronic Engineering, 4(2), 24-30. https://doi.org/10.11648/j.jeee.20160402.14

Copy | Download

ACS Style

Wang Xue-Jun. A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. J. Electr. Electron. Eng. 2016, 4(2), 24-30. doi: 10.11648/j.jeee.20160402.14

Copy | Download

AMA Style

Wang Xue-Jun. A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing. J Electr Electron Eng. 2016;4(2):24-30. doi: 10.11648/j.jeee.20160402.14

Copy | Download

@article{10.11648/j.jeee.20160402.14,
  author = {Wang Xue-Jun},
  title = {A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {2},
  pages = {24-30},
  doi = {10.11648/j.jeee.20160402.14},
  url = {https://doi.org/10.11648/j.jeee.20160402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160402.14},
  abstract = {In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing
AU  - Wang Xue-Jun
Y1  - 2016/04/07
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160402.14
DO  - 10.11648/j.jeee.20160402.14
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 24
EP  - 30
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160402.14
AB  - In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.
VL  - 4
IS  - 2
ER  -

Copy | Download