Estimation of Bistatic Radar Detection Performance Under Discrete Clutter Conditions Using Stochastic Geometry

TL;DR

This paper introduces a stochastic geometry-based metric to evaluate bistatic radar detection performance in environments with discrete clutter, providing analytical insights and validation through simulations.

Contribution

It develops an analytical framework using stochastic geometry to estimate detection probability under clutter, and derives system design insights for bistatic radars.

Findings

The detection probability can be analytically estimated considering clutter as a Poisson process.

The framework helps determine the effective range and optimal power for bistatic radars.

Validation confirms the accuracy of the analytical model with Monte Carlo simulations.

Abstract

We propose a metric called the bistatic radar detection coverage probability to evaluate the detection performance of a bistatic radar under discrete clutter conditions. Such conditions are commonly encountered in indoor and outdoor environments where passive radars receivers are deployed with opportunistic illuminators. Backscatter and multipath from the radar environment give rise to ghost targets and point clutter responses in the radar signatures resulting in deterioration in the detection performance. In our work, we model the clutter points as a Poisson point process to account for the diversity in their number and spatial distribution. Using stochastic geometry formulations we provide an analytical framework to estimate the probability that the signal to clutter and noise ratio from a target at any particular position in the bistatic radar plane is above a predefined threshold. Using the metric, we derive key radar system perspectives regarding the radar performance under noise and clutter limited conditions; the range at which the bistatic radar framework can be approximated to a monostatic framework; and the optimal radar transmitted power and bandwidth. Our theoretical results are experimentally validated with Monte Carlo simulations.