Reduced-dimension sparse representation-based space–time adaptive processing method for airborne radar using simplified time–time transform spectrum

Qiang Wang; Yongshun Zhang; Yiduo Guo; Zhihui Li

doi:10.1117/1.JRS.12.025015

13 June 2018 Reduced-dimension sparse representation-based space–time adaptive processing method for airborne radar using simplified time–time transform spectrum

Qiang Wang, Yongshun Zhang, Yiduo Guo, Zhihui Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 2, 025015 (June 2018). https://doi.org/10.1117/1.JRS.12.025015

Abstract

Considering the high computational burden of typical space–time adaptive processing (STAP) based on sparse representation (SR) (SR-STAP) method, a reduced-dimension (RD) SR-STAP method using simplified time–time (STT) transform spectrum is proposed to overcome this issue. First, the STT transform spectrum formula of clutter on cell under test (CUT) is deduced and the main energy of CUT in the STT transform domain is extracted. Second, to design the RD matrix, an adjustable RD threshold is defined, which is used to make a comparison with STT transform spectrum energy. Third, the RD SR dictionary is constructed to estimate the clutter spatial–temporal spectrum. Numerical simulation results demonstrate that the proposed sparse representation based on simplified time-time-STAP method reduces the computational burden significantly and has a highly similar clutter suppression performance compared with the typical SR-STAP.

Citation Download Citation

Qiang Wang, Yongshun Zhang, Yiduo Guo, and Zhihui Li "Reduced-dimension sparse representation-based space–time adaptive processing method for airborne radar using simplified time–time transform spectrum," Journal of Applied Remote Sensing 12(2), 025015 (13 June 2018). https://doi.org/10.1117/1.JRS.12.025015

Received: 25 September 2017; Accepted: 23 May 2018; Published: 13 June 2018

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KEYWORDS

Associative arrays

Radar

Target detection

Doppler effect

Lithium

Neodymium

Numerical simulations

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