Optimization of Radar Parameters for Maximum Detection Probability Under Generalized Discrete Clutter Conditions Using Stochastic Geometry

TL;DR

This paper develops an analytical stochastic geometry framework to optimize radar parameters for maximum detection probability in environments with generalized discrete clutter, validated through simulations.

Contribution

It introduces a novel stochastic geometry-based model for radar detection in cluttered environments and derives optimal radar bandwidth and power settings for improved detection performance.

Findings

Optimal radar bandwidth for maximum detection probability.

Peak transmitted power beyond which no improvement occurs.

Validated analytical results with FDTD simulations.

Abstract

We propose an analytical framework based on stochastic geometry (SG) formulations to estimate a radar's detection performance under generalized discrete clutter conditions. We model the spatial distribution of discrete clutter scatterers as a homogeneous Poisson point process and the radar cross-section of each extended scatterer as a random variable of the Weibull distribution. Using this framework, we derive a metric called the radar detection coverage probability as a function of radar parameters such as transmitted power, system noise temperature and radar bandwidth; and clutter parameters such as clutter density and mean clutter cross-section. We derive the optimum radar bandwidth for maximizing this metric under noisy and cluttered conditions. We also derive the peak transmitted power beyond which there will be no discernible improvement in radar detection performance due to clutter limited conditions. When both transmitted power and bandwidth are fixed, we show how the detection threshold can be optimized for best performance. We experimentally validate the SG results with a hybrid of Monte Carlo and full wave electromagnetic solver based simulations using finite difference time domain (FDTD) techniques.