Abstract
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.
Highlights
With the development of new countermeasure technology, radar is faced with an increasingly complex electromagnetic environment [1]
It is well known that the frequency agile radar (FAR) that adopts the pulse-to-pulse random frequency agility over shorter intervals can improve the performance in target detection [3,4], and its thumbtack ambiguity function [5] implies that one is able to obtain high range–velocity resolution and avoid range–velocity coupling problems which are present in traditional frequency agility waveforms [6]
(13) can obtain the optimal solution of parameter estimation, the computational complexity resulted from fine grid-search is unacceptable
Summary
With the development of new countermeasure technology, radar is faced with an increasingly complex electromagnetic environment [1]. As for the target detection, the early studies were mainly focused on incoherent integration such as the Hough transform [9] and the Radon transform based methods [10]. It has integration loss compared with the coherent method. Radon–Fourier transform (WSRFT) method for coherent integration of high-speed targets, but only for fixed carrier frequency radars. The computational complexities of CRT and CS are very high which greatly limits their applications In this communication, a computationally efficient algorithm for estimating target parameters of FAR under jamming environment is developed. The experimental results show that this method has a lower computational complexity at the cost of trivial performance loss
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