Affiliation:
1. College of Science, National University of Defense Technology, Changsha 410073, China
Abstract
When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model that characterizes the relationship between the signal-to-interference ratio (SIR) and the anti-jamming rate. The logistic curve’s inflection point and growth rate serve as crucial indices for evaluating radar anti-jamming performance, providing insights into the SIR threshold for successful jamming mitigation. The proposed model allows for the derivation of posterior distributions for these parameters using the MCMC sampling method and kernel density estimation. It also enables the fusion of anti-jamming data from multiple stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Through extensive simulations, our method achieves a remarkably low root mean square error (RMSE) of 0.0552. Compared with a conventional BETA fusion model, our proposed logistic fusion approach demonstrates superior performance and robustness in accurately estimating the anti-jamming rate. The fusion of multi-stage data, even with varying levels of reliability, improves the overall accuracy of the performance evaluation.
Funder
National Natural Science Foundation of China
NSAF Joint Fund
Reference36 articles.
1. Time-Interleaved Noise Jamming;Claassen;IEEE Trans. Aerosp. Electron. Syst.,2023
2. Wei, J., Wei, Y., Yu, L., and Xu, R. (2023). Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm. Remote Sens., 15.
3. Deceiving-Based Anti-Jamming Against Single-Tone and Multitone Reactive Jammers;Pourranjbar;IEEE Trans. Commun.,2022
4. Application of AHP and D-S evidential theory in radar seeker anti-interference performance evaluation;Liu;J. Eng.,2019
5. Shuang, B., Jun, H., and Zhiyong, N. (2020, January 4–6). Research on Evaluation Method of Radar Anti-jamming Effectiveness Based on Experimental Big Data. Proceedings of the 2020 6th International Conference on Big Data and Information Analytics (BigDIA), Shenzhen, China.