When evaluating the effectiveness of radar antijamming techniques, the limited sample size poses a challenge. To overcome this limitation, this paper proposes a Signal-toInterference Ratio Fusion (SIRF) model that integrates multi-stage data. The SIRF model is based on the relationship between the signal-to-interference ratio and radar target range, considering three fluctuation models for radar cross section: Swerling I-II, Swerling III-IV, and Rayleigh. By employing Bayesian theory and the anti-entropy weight method, the SIRF model enables the fusion of data collected from various stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Numerical results demonstrate that the proposed SIRF model exhibits superior consistency and robustness compared to the conventional fusion model based on the beta distribution.

When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model that characterizes the relationship between the signal-to-interference ratio (SIR) and the anti-jamming rate. The logistic curve’s inflection point and growth rate serve as crucial indices for evaluating radar anti-jamming performance, providing insights into the SIR threshold for successful jamming mitigation. The proposed model allows for the derivation of posterior distributions for these parameters using the MCMC sampling method and kernel density estimation. It also enables the fusion of anti-jamming data from multiple stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Through extensive simulations, our method achieves a remarkably low root mean square error (RMSE) of $0.0552$. Compared with a conventional BETA fusion model, our proposed logistic fusion approach demonstrates superior performance and robustness in accurately estimating the anti-jamming rate. The fusion of multi-stage data, even with varying levels of reliability, improves the overall accuracy of the performance evaluation.

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.