Variational Bayesian Inference-Based Airborne Radar Target Tracking Algorithm in Strong Clutter

The strong clutter interference suffered by the airborne radar and the strong maneuvering of the target make noise non-Gaussian and heavy-tailed. Besides, the movement of the carrier aircraft induces the target is totally submerged by the clutter, so that the radar cannot detect the target. To this end, a target tracking algorithm for missing measurements in strong clutter is designed. Student t distribution is utilized to model the heavy-tailed property of non-Gaussian noise. The posterior probability density function(PDF) of the summation form is converted into the probability mass function(PMF) of the product form by introducing Bernoulli random variables. And a hierarchical state space model is further devised. Based on this model, a robust variational Bayesian smoother for measurement dropouts(RVBSD) is designed. An example that the airborne radar tracks an airborne target is given to verify the effectiveness of the proposed algorithm. © 2022 Chinese Institute of Electronics. All rights reserved.