Parallel Computing Based Dynamic Programming Algorithm of Track-before-Detect

Abstract

The conventional dynamic programming-based track-before-detect (DP-TBD) methods are usually intractable in multi-target scenarios. The adjacent targets may interfere with each other, and the computational complexity is increased with the number of targets. In this paper, a DP-TBD method using parallel computing (PC-DP-TBD) is proposed to solve the above problems. The search region of the proposed PC-DP-TBD is divided into several parts according to the possible target movement direction. The energy integration is carried out independently and parallel in each part. This contributes to reducing the computational complexity in each part, since the divided search region is smaller than the whole one. In addition, the target energy can only be integrated adequately in the part in which the search direction matches the target movement. This is beneficial to improve the ability to detect the targets with various movement directions in different parts with different search directions. The solution to the problem of the adjacent targets interfering with each other is discussed. The procedure of the parallel computing in the proposed PC-DP-TBD is presented in detail. Simulations are conducted to verify the superiority of the proposed PC-DP-TBD in terms of detection probability and computational complexity.

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

“…To find the area to which the target trajectory belongs, premises ( 12) and ( 17) must be true. Based on ( 21) and (22), it can be concluded that the probability of premise (17) being true is larger than that of premise (12), meaning the second power optimal merit function-based method is more reliable than the first power, in theory.…”

“…The state search efficiency of the maneuvering target is improved with optimization of the state transition strategy. PC-DP-TBD (DP-TBD method using parallel computing) [22] was proposed to solve the problem that the adjacent targets may interfere with each other and the computational complexity is increased with the number of targets. Although some progress has been made in DP-TBD, there are several problems to be solved [23,24]: In recent years, DP-TBD has become a hot research direction.…”

DP-TBD algorithm based on Hungarian assignment algorithm

“…While DP-TBD technology has been successfully applied in optical and infrared fields, there are still challenges to be addressed in algorithm implementation and engineering practice. [15] The high computational load of the DP-TBD algorithm poses difficulties in meeting real-time requirements in engineering applications with limited hardware capabilities [16] . Therefore, reducing the calculation load of DP-TBD algorithm through an effective way is the key problem to apply TBD technology to real-time system.…”

Dynamic programming network for point target detection

“…To address the aforementioned problems, many improved algorithms for merit function have been proposed, including algorithms based on amplitude constraints [12], system memory coefficients [13], multi-level thresholds [14,15], velocity space partition (VSP) [16,17], velocity space matching (VSM) [18][19][20], high-order DP [21][22][23][24][25], and DP ring (DPR) structure [25]. The introduction of amplitude constraints [12], system memory coefficients [13], and multi-level thresholds [14,15] has a good effect in suppressing the merit function diffusion of strong targets, with a signal-to-noise ratio (SNR) of more than two, but can degrade the detection performance of dim targets.…”