Expanding Window Dynamic-Programming-Based Track-Before-Detect With Order Statistics in Weibull Distributed Clutter

Abstract

This article considers radar detection and tracking of weak fluctuating targets using dynamic programming (DP)-based track-before-detect (TBD). The clutter is modeled using a Weibull distribution, and the well-known Swerling type 0, 1, and 3 targets are considered. An efficient algorithm is proposed, which employs order statistics in DP-based TBD to detect weak fluctuating targets. In addition, a novel expanding window track-before-detect (EW-TBD) technique for multiframe processing is presented to improve the detection performance with reasonable computational complexity compared to batch processing. It is shown that EW-TBD has lower complexity than existing multiframe processing techniques. Simulation results are presented, which confirm the superiority of the proposed expanding window technique in detecting targets even when they are not present in every scan in the window. In addition, the throughput of the proposed technique is higher than that with batch processing.