Accurate Clutter Synthesis for Heterogeneous Textures and Dynamic Radar Environments

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch’s specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code.

I. Introduction

Clutter synthesis is important for developing a radar system that is capable of various radar operation modes such as the ground moving target indicator (GMTI), sea moving target indicator (SMTI), and tracking modes [1]–[3]. For this purpose, mathematical models of clutter are used in most phases of maritime radar development [4]–[6]. It is important to understand the temporal, spatial, and statistical characteristics of clutter signals to design an effective detection and tracking algorithm that operates well under real-world conditions.