Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 41, Pages: 1-8
D. Venu1* and N. V. Koteswara Rao2
*Author for correspondence
D. Venu Osmania University, Hyderabad - 500007, Telangana, India; [email protected]
Objectives: In the current work, we propose a Space-Time Adaptive Processing (STAP) technique for bistatic passive radar by fully adaptive STAP to provide effective clutter mitigation by using only few set of data samples. Methods/ Statistical Analysis: Several methods exist but they are computationally complex or require the knowledge of the radar parameters. Moreover our modified Adaptive STAP technique aims two fold one is to improve the Signal to Interferenceplus-Noise Ratio (SINR), especially at short ranges and other greatly reduces the rank of the covariance matrix to further improve performance and reduce processing requirements. Findings: In this paper, propose a methodology to precisely approximate the Clutter Covariance Matrix (CCM) and implement STAP based on only few number of secondary data samples. We also simulated relationship for clutter suppression such that ,SINR for the optimum and tapered fully adaptive STAP, as well as Doppler straddling losses are deliberated clearly. We observed that in Optimum STAP, with its indirect uniform taper, fallouts in space-time filters with Doppler responses to be narrow, so there is increase in additional straddling losses, as compared to fully adaptive tapered. Correspondingly a distinct weight vector was calculated for every potential target Doppler, which penalties on optimum SINR curve to be smooth upper bound over the enactment feasible with other suboptimum STAP algorithm. Finally with less input interference-to-noise ratio, the optimum SINR achieves an SINR improvement of 76.01dB over the center of the Doppler space. Application/Improvements: As the STAP eliminates the clutter, hence non-cooperative transmitter meritoriously makes the target detection simple in severe environments like air-ground.
Keywords: Bistatic Radar, Clutter Mitigation, Covariance Matrix, Space-Time Adaptive Processing, SINR
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