A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced.
Abstract
A generalization of Capon's maximum-likelihood technique for detection and estimation of seismic signals is introduced. By using a multi-dimensional autoregressive approximation of seismic array noise, we have developed a technique to use Capon's multi-channel filter for on-line processing. Such autoregressive adaptation to the curent noise matrix power spectrum is shown to yield good suppression of mutually correlated array noise processes. As an example, this technique is applied to detection of a small Semipalatinsk underground explosion recorded at the ARCESS array.
Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming Environment
