Abstract:

Bayesian track-before-detect can integrate signals in the form of probability among data frames by making use of target motion model, which can improve the detection performance for stealthy targets. However,  the detect threshold of Bayesian track-before-detection is difficultly set under Neyman-Pearson criterion. Based on the analysis of basic principles of Bayesian track-before-detect, the paper analyzes the relationship between the false alarm probability and the detection threshold in detail, and presents an approach to determine the detection threshold in white Gaussian noise. Using the approach, the detection threshold can be ascertained according to the false alarm probability in demand. Finally, the computer simulation experiments verify the feasibility of the approach of determining the detection threshold.