Waveform agile sensing approach for tracking benchmark in the presence of ECM using IMMPDAF

Abstract

This paper presents an efficient approach based on waveform agile sensing, to enhance the performance of benchmark target tracking in the presence of strong interference. The waveform agile sensing library consists of different waveforms such as linear frequency modulation (LFM), Gaussian frequency modulation (GFM) and stepped frequency modulation (SFM) waveforms. Improved performance is accomplished through a waveform agile sensing technique. In this method, the selection of waveform to be transmitted at each scan is determined, by jointly computing ambiguity function of waveform and Cramer-Rao Lower Bound (CRLB) matrix of measurement errors. Electronic counter measures (ECM) comprises of stand-off jammer (SOJ) and self-screening jammer (SSJ). Interacting multiple model probability data association filter (IMMPDAF) is employed for tracking benchmark trajectories. Experimental results demonstrate that, waveform agile sensing approach require only 39:98 percent lower mean average power compared to earlier studies. Further, it is observed that the position and velocity root mean square error values are decreasing as the number of waveforms are increasing from 5 to 50.