Performance Evaluation for Unknown Deterministic Target Signals Detection via Detection Information

Abstract

The detection method, such as Newman-Pearson (NP) method, can provide an accurate prediction for the performance of target detection under all signal-to-noise ratio regions. However, the performance limits of radar signal detection have not been extensively studied yet. In this paper, we propose a novel detection method for unknown deterministic signals in radar system, which utilizes mutual information to characterize the uncertainty of the existence state of signal. The a posteriori probability density function of existence state of signal can be directly obtained via the Bayesian framework, ensuring that there is no loss of information regarding to the target's existence state during the mutual information computation process. Numerical simulations show that the proposed method exhibits superior detection performance compared to the NP detection method.