A fast implementation of Dynamic Programming based Track-Before-Detect for radar system

Abstract

This paper addresses the problem of computational efficiency with respect to Dynamic Programming (DP) based Track-Before-Detect (TBD). Generally, DP-TBD is a grid-based method that estimates target trajectory by means of searching all the physically admissible paths in a finite discrete state space. However, its computational complexity increases nonlinearly as the state space expands with size or dimension, which heavily restricts its applications for many realistic scenarios, e.g., radar target detection. To alleviate this problem, a fast twostep implementation of DP-TBD is proposed in this work. In the first step, to reduce the computational costs, target states are roughly estimated by searching a discrete grid with larger cell size. In the second step, an accurate search is performed only on the parts of the state space indicated by the results in the first step with reasonable computational cost. Additionally, the proposed algorithm is also suitable for the surveillance radar scenarios. Simulation results show that the proposed algorithm can effectively improve the computational efficiency of DP-TBD with limited performance degradation.

Research on the SAR Image Target Tracking Algorithm Based on Sparse Representation and Dictionary Learning

“…But the algorithm cannot adapt to intense illumination change and target deformation and keep out the influence of the interference factors. If template update can adapt to illumination changes and target deformation, while under the shade adopts the appropriate strategy to continue to track, then target tracking algorithm will greatly improve the adaptability and can meet the needs of practical engineering [1][2][3].…”

Fast scan‐to‐scan integration algorithm for small target in sea clutter

“…Track-before-detect (TBD) is an effective method which is generally implemented after SFD [3]. Unfortunately, in strong clutter, conventional TBD algorithms are unaffordable because of numerous false-alarm points detected [4]. In a typical scenario, the detection is done with a low threshold to ensure the probability of detection (PD), and this result in a high probability of false alarms (PFAs) of 10 −2 -10 −3 [5].…”