Cognitive target detection based on Bayesian approach in radar

Abstract

Cognitive radar (CR), which uses a closed-loop feedback perception-action cycle system, aims to develop a higher level of intelligence. In this study, the authors develop an efficient Bayesian approach to target detection in CR. The approach, which uses the Neyman-Pearson criterion with priori probability, can improve the target detection effectively. The recursive form of computing priori probability is also discussed. Numerical simulations illustrate that the proposed target detector can improve the performance of detection with the usage of priori probability, which can be computed recursively using the previous measurements.

Keywords

• feedback
• bayes methods
• probability
• closed loop systems
• object detection
• cognitive target detection
• cognitive radar
• closed-loop feedback perception-action cycle system
• efficient bayesian approach
• neyman-pearson criterion
• priori probability
• target detector