Performance tradeoffs for multi-channel parametric adaptive radar algorithms

Airborne radar systems employing radar sensor arrays utilize multi-channel (MC) signal processing techniques for optimal detection and localization of targets. The detection and localization statistics have mathematical structures that typically require evaluating the inverse of an estimated covariance matrix. Due to the size of sensor arrays and the number of pulses in a coherent processing interval (CPI), the dimension of the covariance arrays is very large (1000s); the computational burden of estimating and inverting such large arrays has led to the development of parametric methodologies that significantly reduce both the computational requirements and the amount of measured data to create the estimated inverse covariance matrix. This paper compares the relative merits, by using performance tradeoff plots of six different parametric algorithms when compared to the conventional sample matrix inversion (SMI) approach.