Radar Anti-Jamming Performance Evaluation: A Novel Model Based on Measurement Error and RCS Distributions

Abstract

The growing complexity of the electromagnetic environment makes accurate radar anti-jamming performance evaluation essential for assessing the effectiveness of electronic countermeasure systems. In jamming scenarios, smaller measurement errors in range indicate a radar's enhanced ability to counteract interference. This paper presents a novel approach to radar anti-jamming performance evaluation based on statistical models of radar cross-section (RCS) fluctuations. Assuming that the RCS follows one of several distributions—Swerling I–II, Swerling III–IV, or Rayleigh—we derive the corresponding distributions of radar parameter measurement errors. In our model, the measurement error is assumed to follow a conditional Gaussian distribution, with its standard deviation modelled as a random variable dependent on both the RCS and the signal-to-interference ratio (SIR). This formulation establishes a quantitative relationship between measurement error, SIR, and RCS, and enables derivation of the error's probability density function (PDF). Consequently, we obtain a novel expression for the radar anti-jamming rate. We compare this model to two conventional approaches: one that assumes a constant error variance across all target ranges and another that assumes a fixed variance that varies with target ranges but without incorporating distributional uncertainty. The proposed Error Distribution Estimation (EDE) model leverages the full probability distribution of measurement errors together with real-time parameter error data fusion. This integration provides a more continuous and nuanced evaluation of radar anti-jamming performance, potentially leading to more reliable assessments under a range of operating conditions.