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

Abstract

The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%.

DP-TBD algorithm based on Hungarian assignment algorithm

“…It is reasonable and feasible to find a target state sequence satisfying (10) in discrete state space by using DP algorithm recursively and iteratively.…”

“…There are several main TBD algorithms, including the Maximum likelihood TBD algorithm [6] , Hough transform TBD (HT-TBD) algorithm [7] , dynamic programming TBD (DP-TBD) algorithm [8][9][10] , particle filter TBD (PF-TBD) algorithm [11] and random finite set based TBD algorithm [12] . Among these, the Dynamic Programming TBD algorithm has gained attention due to its high target detection performance [13][14] .…”

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

“…Due to the long detection distance and low signal-to-noise ratio of targets, it is difficult to detect targets on the image plane. Therefore, researchers have conducted many theoretical analyses and simulations on the detection and recognition of dim moving targets in deep space backgrounds [7][8][9][10][11]. Satellites located in specific orbits can use the passive observation of IR sensors to obtain Line-of-Sight (LOS) measurement information from the sensor to the target.…”

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

“…The detection of moving targets has important applications in security monitoring, military reconnaissance, and satellite detection [1][2][3]. In some scenarios, such as early warning against space debris [4] and small faint bodies in near-Earth space or against naval ships and fighters, optical remote sensing detection has the characteristics of long distance and large field of view [5].…”