Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

Sen, Satyabrata

doi:10.1109/JSTSP.2015.2464187

Title: Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

Abstract

We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positive semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition basedmore »« less

Authors:

Sen, Satyabrata ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Tue Aug 04 00:00:00 EDT 2015

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1235829

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: IEEE Journal of Selected Topics in Signal Processing

Additional Journal Information:: Journal Volume: 9; Journal Issue: 8; Journal ID: ISSN 1932-4553

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Citation Formats


                    Sen, Satyabrata. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar.  United States: N. p., 2015. 
        Web.  doi:10.1109/JSTSP.2015.2464187.

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                    Sen, Satyabrata. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar.  United States.  https://doi.org/10.1109/JSTSP.2015.2464187

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                    Sen, Satyabrata. 2015.  
        "Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar".  United States.  https://doi.org/10.1109/JSTSP.2015.2464187.  https://www.osti.gov/servlets/purl/1235829.

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@article{osti_1235829,

  title        = {Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar},

  author       = {Sen, Satyabrata},

  abstractNote = {We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positive semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.},

  doi          = {10.1109/JSTSP.2015.2464187},

  url          = {https://www.osti.gov/biblio/1235829},
  journal      = {IEEE Journal of Selected Topics in Signal Processing},

  issn         = {1932-4553},

  number       = 8,

  volume       = 9,

  place        = {United States},

  year         = {Tue Aug 04 00:00:00 EDT 2015},

  month        = {Tue Aug 04 00:00:00 EDT 2015}

}

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https://doi.org/10.1109/JSTSP.2015.2464187

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Works referencing / citing this record:

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments
journal, April 2018

Wang, Wei; Zou, Lin; Wang, Xuegang
EURASIP Journal on Advances in Signal Processing, Vol. 2018, Issue 1
https://doi.org/10.1186/s13634-018-0546-8

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Similar Records

Title: Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

Abstract

Citation Formats

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments journal, April 2018

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments
journal, April 2018