An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results.

[1]  Sylvie Marcos,et al.  Subspace-based and single dataset methods for STAP in heterogeneous environments , 2012 .

[2]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[3]  E Aboutanios,et al.  Hybrid Detection Approach for STAP in Heterogeneous Clutter , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[4]  M. Wicks,et al.  Practical joint domain localised adaptive processing in homogeneous and nonhomogeneous environments. Part 2: Nonhomogeneous environments , 2000 .

[5]  Jian Li,et al.  Knowledge-Aided Space-Time Adaptive Processing , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[6]  Xie Wenchong,et al.  Local Degrees of Freedom of Airborne Array Radar Clutter for STAP , 2009, IEEE Geoscience and Remote Sensing Letters.

[7]  Sylvie Marcos,et al.  Deterministic Aided STAP for Target Detection in Heterogeneous Situations , 2013 .

[8]  Richard Klemm,et al.  Applications of Space-Time Adaptive Processing , 2004 .

[9]  Laurent Savy,et al.  An extended formulation of the Maximum Likelihood Estimation algorithm. Application to space-time adaptive processing , 2011, 2011 12th International Radar Symposium (IRS).

[10]  W.L. Melvin,et al.  A STAP overview , 2004, IEEE Aerospace and Electronic Systems Magazine.

[11]  Jian Li,et al.  High Resolution Angle-Doppler Imaging for MTI Radar , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Jacques G. Verly,et al.  Geometry-Induced Range-Dependence Compensation for Bistatic STAP with Conformal Arrays , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Braham Himed,et al.  Image feature-based space-time processing for ground moving target detection , 2006, IEEE Signal Processing Letters.

[14]  Diego Cristallini,et al.  A Robust Direct Data Domain Approach for STAP , 2012, IEEE Transactions on Signal Processing.

[15]  Shu Wang,et al.  Adaptive Sparsity Matching Pursuit Algorithm for Sparse Reconstruction , 2012, IEEE Signal Processing Letters.

[16]  B. Mulgrew,et al.  A STAP algorithm for radar target detection in heterogeneous environments , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[17]  L.E. Brennan,et al.  Theory of Adaptive Radar , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[18]  T. Sarkar,et al.  A deterministic least-squares approach to space-time adaptive processing (STAP) , 2001 .

[19]  Gang Li,et al.  A novel STAP algorithm using sparse recovery technique , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[20]  Volkan Cevher,et al.  Bearing Estimation via Spatial Sparsity using Compressive Sensing , 2012, IEEE Transactions on Aerospace and Electronic Systems.