Knowledge-Based Track-Before-Detect Strategies for Fluctuating Targets in K -Distributed Clutter

In this paper, we address the problem of detecting fluctuating targets in heavy-tailed clutter through the use of dynamic programming based track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of K distribution, while the well-known Swerling targets of types 1 and 3 are considered to capture the target amplitude fluctuation scan-toscan. Conventional DP-TBD suffers significant performance loss in this case due to the high frequency of target-like outliers. In this paper, we resort to the knowledge-based techniques, i.e., use a priori information to enhance radar detection performance. More precisely, knowledge-based DP-TBD (KB-DP-TBD) strategies are developed by incorporating the amplitude information into the integration process of DP-TBD. Simulations are used to assess performances and computational complexities of different DP-TBD strategies. The relevant result is that KB-DP-TBD can improve the detection performance over the conventional DP-TBD, especially for very heavy-tailed K-distributed clutter.