Some results on the Weiss-Weinstein bound for conditional and unconditional signal models in array processing

TL;DR

This paper analyzes the Weiss-Weinstein bound for source localization in array processing, comparing conditional and unconditional models, and applies findings to specific array geometries and prior assumptions.

Contribution

It provides new insights into the Weiss-Weinstein bound for various source models and array configurations, including cases with different priors and array geometries.

Findings

Derived bounds for multiple sources without specific matrix structures

Analyzed the impact of uniform and Gaussian priors on bounds

Applied results to single-source scenarios with specific array geometries

Abstract

In this paper, the Weiss-Weinstein bound is analyzed in the context of sources localization with a planar array of sensors. Both conditional and unconditional source signal models are studied. First, some results are given in the multiple sources context without specifying the structure of the steering matrix and of the noise covariance matrix. Moreover, the case of an uniform or Gaussian prior are analyzed. Second, these results are applied to the particular case of a single source for two kinds of array geometries: a non-uniform linear array (elevation only) and an arbitrary planar (azimuth and elevation) array.