Eigen filtering is known as the best method for maximizing signal/clutter improvement factor (IF) in radar signal processing. Up to now, however, the eigen filtering method has not been well studied in radar signal processing because of its excessive computational burden.
In this thesis work, an efficient implementation of the eigen filtering method is proposed and its performance is studied with applications to adaptive radar clutter rejection and adaptive moving target detection. The basic idea for the efficient implementation of the eigen filtering method is that clutter signals can be modeled as weakly stationary random processes, which have Hermitian Toeplitz covariance matrices. A simple iterative procedure is devised to obtain the minimum eigenvalue of the clutter signal covariance matrix by exploting the Hermitian Toeplitz property.
In order to obtain the effectiveness of the proposed implementation in clutter rejection, performance comparison was done between the proposed method and the adaptive clutter rejection algorithm based on whitening filtering via computer simulation. When we use IF as a performance measure, the simulation results show that the proposed algorithm yields lower computational complexity and better performance than those of the whitening filter algorithm.
In addition, we obtained the performance of the proposed method in moving target detection. The radar receiver for moving target detection is composed of an eigen filter cascaded with a narrow-band Doppler filter, bank (DFB). In the radar receiver, several window types are applied to reduce spectral leak between adjacent DFB's. Simulation results show 3~5 dB performance improvement in signal-to-noise ratio for the Dolph-Chebyshev window compared with those for the other window types.
