Innovative Cognitive Approaches for Joint Radar Clutter Classification and Multiple Target Detection in Heterogeneous Environments

TL;DR

This paper introduces innovative detection architectures that classify clutter types and detect multiple targets in heterogeneous radar environments, enhancing situational awareness and detection performance.

Contribution

It proposes novel detection architectures utilizing latent variable models and EM algorithms to classify clutter and detect multiple targets with unknown positions and numbers.

Findings

Effective in heterogeneous scenarios

Provides initial environmental snapshot

Improves detection and estimation performance

Abstract

The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community. In this paper, we provide a solution to this problem by devising detection architectures capable of classifying the range bins according to their clutter properties and detecting possible multiple targets whose positions and number are unknown. Remarkably, the information provided by the proposed architectures makes the system aware of the surrounding environment and can be exploited to enhance the entire detection and estimation performance of the system. At the design stage, we assume three different signal models and apply the latent variable model in conjunction with estimation procedures based upon the expectation-maximization algorithm. In addition, for some models, the maximization step cannot be computed in closed-form (at least to the best of authors' knowledge) and, hence, suitable approximations are pursued, whereas, in other cases, the maximization is exact. The performance of the proposed architectures is assessed over synthetic data and shows that they can be effective in heterogeneous scenarios providing an initial snapshot of the radar operating scenario.