Research On Wide-Band Direction Finding Algorithms

DOA (direction of arrival) estimation has wide applications in mobile and satellite communication systems, information warfare, radar, passive sonar, seismology, astronomy, navigation, video conferencing, time series analysis and spectrum estimation, and has received much attention in recent years. But the current literature about direction finding algorithms pays more attention to narrow-band signal, that is to say bandwidth of signal is far smaller than center frequency of signal and so complex envelop of signal keeps invariant in the sensors of the array. The signal arrives on different sensors at different time delays and so phase difference is caused, then DOA is estimated in time domain.With the development of communication technologies, wide-band signals are used more and more widely in communication systems. In the other hand, there are many wide-band signals in nature, such as seismology wave, acoustic signal. So the research on wide-band direction finding becomes more and more important.Based on the characteristic of wide-band signal, this dissertation makes full use of the large time-bandwidth product and proposes wide-band maximum likelihood direction finding algorithms, wide-band sub-space direction finding algorithms based on DOA pre-estimation, wide-band direction finding algorithms under the condition of complete not knowing DOA information and wide-band beamformer, analyzing their performance theoretically and verifying it through simulation. The main research work and contributions of the dissertation are as follows:(1) Under the assumption of Gaussian noise, direction finding algorithms based on maximum likelihood are optimal, but the optimization of cost function is an intractable problem. The dissertation combines alternating projection algorithm and EM algorithm to optimize the wide-band deterministic maximum likelihood direction finding algorithm cost function, making full use of fast convergence rateof alternating projection algorithm and eliminating the sensitivity of EM algorithm to initial value.(2) The dissertation applies genetic algorithm to optimize the cost function of wide-band deterministic maximum likelihood direction finding and wide-band weighted sub-space fitting direction finding, and attains the relation between DOA estimation results and operation coefficients of genetic algorithm, which supplies basis for the engineering application of wide-band direction finding.(3) The dissertation modifies traditional RSS wide-band direction finding algorithm, not only attaining lower complexity of computation, but also improving estimation precision and resolution ability.(4) In traditional wide-band direction finding algorithms based on array manifold transform, the computation of focusing matrix needs DOA pre-estimation, but there is often bias in pre-estimation, so it can lead to uncertain effect on the final estimation result. The dissertation proposes wide-band direction finding algorithm based on noiseless covariance matrix transform, because the computation of transform matrix uses sensors data directly, so there is no effect of pre-estimation bias on the final result.(5) The dissertation proposes wide-band direction finding algorithm based on pseudo-covariance matrix eigen-decompositioin. A pseudo-data matrix and then a pseudo-covariance are constructed using the signal eigen-vectors of eigen-decomposition of narrow-band covariance matrix. After the eigen-decomposition of the pseudo-covariance matrix, noise sub-space is got and sub-space method is utilized to get DOA estimation.(6) The dissertation proposes the improved version of wide-band steered minimum variance beamformer. Simulation results based on ULA mode demonstrates that, compared with the original algorithm, the performance of improved algorithm improves in estimation precision and resolution ability.