Maximum Likelihood Natural And Music Algorithm For Doa Estimation Of Dsp Realization

In the recent decade many high-resolution DOA (Direction Of-Arrival) algorithms have been developed in the field of Array Signal Processing. There are mainly two type of algorithms used for spatial spectrum estimation: one is those based on Bayesian Maximum Likelihood method, like the ML (Maximum Likelihood) algorithm, Maximal Entropy method and etc., the others are based on the spatial decomposition or projection of correlation matrix, this kind of algorithm include Vector Characterization Method, MUSIC (multiple signal classification) algorithm, Projection Matrix Method, etc. MUSIC is a classic spatial spectrum estimation algorithm that has a super high resolution and is widely used today, however, it cannot estimate successfully the azimuths and elevations of signals that are correlated. The Maximum Likelihood algorithm can be used for the DOA estimation of correlated signals, but it needs a huge computation and long run time. In this paper, MUSIC is chosen as the algorithm for the DOA estimation of non-correlated signals and ML as the algorithm for the correlated signals. To determin the number of signals, we adopt the rule of MDL or AIC which are based on the information theory. And we use Householder transfer method and QR decomposition method to accomplish the eig-decomposing of Hermite matrix, which is needed in the MUSIC algorithm. The above-mentioned algorithm and their implementation procedures are fully discussed in this paper. ADSP21160 is used as the digital signal processor in this spatial spectrum estimation system. It communicates with the front-end receiver and data are transferred into the DSP RAM through chain DMA. The result of DOA estimation is transmitted to the PC by UART port communication and displayed on the terminal display unit. To improve the veracity of the result, we introduced a "window" into the 2-dimention searches of the azimuth and elevation. The "window" is a rectangle, which ensures no more than one signal appear in each "windowed"2-dimension region. By this way we can decrease the probability that more than one computed output represent the same signal while some other signals missing. And we theoretically analyzed it and prove this way effective in the paper. Take advantage of the ADSP21160-concerned Evaluator and Emulator we debugged the software system and the whole spatial spectrum estimation system, and solved all the problems that appeared.