Waveform Design For Track-Before-Detect-Based Cognitive Radars

Abstract

Detect-before-track-based cognitive radars in which threshold detections are taken as the input of tracking, irreversibly result in high false alarm under the case of low signal-to-noise ratio (SNR). To solve this problem, in this paper, we propose a framework of cognitive radars based on track-beforedetect (TBD) technique. This framework includes the TBD measurement model consisting of received ambiguity function without threshold detection, cubature Kalman filter to estimate target state, and the feedback mechanism and optimization criterion for the next transmitted waveform. In particular, waveform design problem in the TBD-based cognitive radars is emphasized. This work opens the door to the cognitive radars based on TBD technique, and reveals their potential in target tracking under the case of low SNR. Numerical results demonstrate that better target tracking performance can be achieved by the TBD-based cognitive radars, as compared with conventional radars.