Radar system performance index dynamic evaluation method based on Bayesian machine learning

Abstract

The invention discloses a radar system performance index dynamic evaluation method based on Bayesian machine learning. The method comprises the following steps: modeling a likelihood function of radartarget detection data into Gaussian distribution; modeling the prior distribution of the target performance index to be evaluated into Laplace distribution; performing hierarchical modeling on the target to-be-evaluated performance index by utilizing the Bayesian hierarchical probability model; and performing approximate solution on the posterior distribution of the to-be-evaluated performance indexes of each target by using a variational Bayesian expectation maximization method, thereby obtaining a posterior probability density function and the like. The Bayesian machine learning method is adopted to realize the dynamic evaluation of the radar performance indexes, the biggest advantage is that under the model assumption condition, the analyzed dynamic indication result can be obtained only by observing the data once, the test frequency can be obviously reduced, the test cost and period are reduced, the analyzed index dynamic change range is provided, and the availability and the robustness are obviously improved. Therefore, the applicability for the dynamic evaluation of the radar system performance indexes is good.

Description

technical field [0001] The invention belongs to the technical field of radar performance index evaluation, in particular to a radar system performance index dynamic evaluation method based on Bayesian machine learning. Background technique [0002] At present, most of the performance evaluation methods for radar systems are based on static index measurement. This type of method is to determine the value of the radar system performance index to be evaluated under fixed target characteristics and environmental variables. At this time, only fixed values ​​can be used The size reflects the pros and cons of performance, so the indication is limited and there are limitations in practical applications. [0003] Then people use the Monte Carlo (MC) pseudo-dynamic evaluation method to evaluate the performance of the radar system. The specific process is based on the theorem of large numbers and is calculated by repeated experiments that randomly select the signal-to-interference-to-n...

AI Technical Summary

Problems solved by technology

[0005] To sum up, how to use the existing radar system performance index evaluation method and Bayesian learning method to improve the index dynamic evaluation method to make the evaluation performance of the radar system better is also a new challenge

Images