A Robust Space-time Adaptive Processing Algorithm based on Particle Swarm Optimization for Non-stationary Clutter Suppression

Hanchao Wang; Lili Fang; Chuanfang Zhang

doi:10.1088/1742-6596/1802/3/032145

A Robust Space-time Adaptive Processing Algorithm based on Particle Swarm Optimization for Non-stationary Clutter Suppression

Hanchao Wang, Lili Fang^*, Chuanfang Zhang

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.

Original language	English
Article number	032145
Journal	IOP Conference Series: Earth and Environmental Science
Volume	1802
Issue number	3
DOIs	https://doi.org/10.1088/1742-6596/1802/3/032145
Publication status	Published - 9 Mar 2021
Event	2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020 - Busan, Korea, Republic of Duration: 14 Nov 2020 → 15 Nov 2020

Keywords

Clutter compensation
Conformal array
Space-time adaptive processing (STAP)
Sparse recovery (SR)

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10.1088/1742-6596/1802/3/032145

Cite this

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title = "A Robust Space-time Adaptive Processing Algorithm based on Particle Swarm Optimization for Non-stationary Clutter Suppression",

abstract = "A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.",

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AU - Wang, Hanchao

AU - Fang, Lili

AU - Zhang, Chuanfang

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2021/3/9

Y1 - 2021/3/9

N2 - A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.

AB - A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.

KW - Clutter compensation

KW - Conformal array

KW - Space-time adaptive processing (STAP)

KW - Sparse recovery (SR)

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VL - 1802

JO - IOP Conference Series: Earth and Environmental Science

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Y2 - 14 November 2020 through 15 November 2020

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A Robust Space-time Adaptive Processing Algorithm based on Particle Swarm Optimization for Non-stationary Clutter Suppression

Abstract

Keywords

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