Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-like Targets: Designs and Comparisons

TL;DR

This paper introduces two novel radar detection and parameter estimation methods for multiple point-like targets, combining maximum likelihood, Bayesian learning, and adaptive strategies for interference and target number estimation.

Contribution

It presents the first joint maximum likelihood and Bayesian learning approach for target parameter estimation and introduces an adaptive method for estimating the number of targets.

Findings

The joint ML and Bayesian approach improves parameter estimation accuracy.

The energy-based detection strategy enhances detection performance at high SNR.

Adaptive estimation of target number improves robustness in varying scenarios.

Abstract

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.