Stochastic Geometry Interference Analysis of Radar Network Performance

TL;DR

This paper models mutual interference in radar networks using stochastic geometry, deriving formulas to analyze how system parameters affect detection and false alarms, providing practical design insights.

Contribution

It introduces a stochastic geometry-based analytical framework for radar interference analysis, including closed-form expressions and design guidelines.

Findings

Interference significantly impacts radar detection range and false alarm rate.

System parameters like network density and antenna directivity are key tradeoffs.

Model accuracy is validated through network simulations.

Abstract

This work characterises the effect of mutual interference in a planar network of pulsed-radar devices. Using stochastic geometry tools and a strongest interferer approximation, we derive simple closed-form expressions that pinpoint the role played by key system parameters on radar detection range and false alarm rate in the interference-limited region. The fundamental tradeoffs of the system between radar performance, network density and antenna directivity are captured for different path-loss exponents in the no-fading and Rayleigh-fading cases. The discussion highlights practical design hints for tuning the radar parameters. The accuracy of the model is verified through network simulations, and the role of random noise on detection in sparse, non interference-limited networks is characterised.