Cognitive radar recognition with Kolmogorov-Smirnov test and momentum gradient descent

Abstract

The emission parameters of cognitive radars can adaptively change according to the environment, which poses a challenge to radar electronic countermeasures (ECM). To counter cognitive radars, it is essential to identify the cognitive characteristics. In this paper, a method is proposed to recognize cognitive radars with power allocation function. The signal-to-interference-plus-noise ratio (SINR) distribution of cognitive radars is derived through feature functions, and hypothesis test is used to identify whether the target radar has cognitive function by designing a Kolmogorov-Smirnov (K-S) detector to recognize adaptive optimization power allocation. Subsequently, a momentum gradient descent algorithm is used to optimize the signal of the jamming machine to reduce type II error probability of radar recognition. K-S detector is simulated and compared with Afriat detector, SVM and MLP detector. Results demonstrate that the K-S detector outperforms both the Afriat and MLP detectors in identifying cognitive radars with dynamic power allocation functionality. At the same detection probability, the K-S detector achieves a 2 dB improvement over the MLP detector and a 4 dB improvement over the Afriat detector.