Numerical Integration-Based Performance Analysis of Amplitude-Comparison Monopulse Algorithm in Correlated Noise

In this paper, the performance analysis of the amplitude-comparison monopulse (ACM) algorithm under a correlated noise effect is dealt with. The noise received by a monopulse antenna is caused by various sources, such as jamming, multipath, clutter, and thermal noise. The noise variables caused by these noise sources may be correlated with each other when received by the antenna elements. We explicitly analyzed the angle estimation performance of the monopulse algorithm when a correlated noise is received by deriving the probability density function (PDF) of the channel noise variables. In this process, correlation coefficients between noise variables received by antenna elements are defined, and variance and correlation coefficients of channel noise variables are derived. The performance of the angle estimation is analyzed by calculating the root mean square error (RMSE) for various variances and correlation coefficients of the received noise variables. The expectation operation required for calculating the RMSE is performed via numerical integration. Consequently, the analytically derived RMSE results show excellent agreements with the Monte Carlo simulation-based RMSE result, and it is confirmed that the RMSE decreases as the correlation coefficient between the received noise variables increases. When the SNR is high and on-axis, the RMSE decreases by 20% whenever the correlation coefficient between the reception noise variables increases by 0.2.

Automated summary

This paper analyzes the performance of the amplitude-comparison monopulse (ACM) algorithm under the influence of correlated noise. The angle estimation performance is studied by deriving the probability density function (PDF) and calculating the root mean square error (RMSE). The results show that the error decreases as the correlation coefficient between the received noise variables increases.