Application Study Of Array Signal Processing On The Radar And Mobile Communication

Array signal processing is a new field of signal processing, and it is a very important content of modern signal processing. Array signal processing has wide engineering application such as radar, sonar, weather predicting, land and ocean exploring, seismic and biomedical signal processing etc. On different spatial positions, a few of sensors are placed by some rules so that these sensors form an array of sensors. Though the type of these sensors may be different, such as antenna, sound sensor under water, sound sensor under earth, ultrasonic sensor, X-radial sensor etc, but the function of these sensors is the same, that is, the interested spatial information is connected to signal processor. By the array of sensors, the electromagnetic wave is sent or the spatial signal is received. So the information of spatial sources is gain by signal processor. The process above is called as array signal processing. The aim of array signal processing is to acquire the useful information of spatial sources form received signal.Now, array signal processing has wide and important application in radar and mobile communication. To a radar being provided with phase control array, adaptive beamforming plays a very important role in noise and interference suppression. To a mobile communication, DOA estimation is a key technology in separation of signal sources.In this paper, the basic theory of array signal processing is first introduced. Then, the four part contents, namely adaptive beamforming of array, DOA and Doppler frequency estimation of sources with non-ideal uniform line array (ULA), DOA estimation of sources with uniform circular array (UCA), DOA estimation or parameter estimation of complex signal sources, are detailedly discussed. These researches have important effects on the theory and application of array signal processing. When the array response vector for the desired signal is not known, adaptive beamforming, that is blind adaptive beamforming, is a problem of people' attention now. In 3rd chapter of this paper, a few of basic beamforming, such as minimum mean square error beamforming, linearly constrained minimum variance beamforming and Eigenspace-Based beamforming (ESB), are first introduced. Then, an algorithm of blind adaptive beamforming is proposed. In this method, the DOA of desired source with unknown array response vector is first estimated. The DOA of desired source is regarded as the constrained steer vector. The optimum weigh vector of ESB is further acquired. Finally, the beamforming to desired signal is formed. The advantage of this method is that it can eliminate the signal cancellation when a desired is contained in the correlation matrix. When there are a few of desired signals in beamforming, this algorithm can be applied.In presence of general array errors, such as amplitude and phase error of sensors, setting position error of sensors etc, the performance of high-resolution estimator such as MUSIC will degrade drastically and even fail. To estimate DOA of sources in the presence of general array errors still is a difficult problem. The problem is detailedly discussed in 4th chapter. Three estimating algorithms are provided. The first one is the estimation of 2-D angle and Doppler frequency in the presence of gain amplitude errors of sensors. The second one is the estimation of 1-D angle and Doppler frequency in the presence of general array errors. The third one is the estimation of 2-D angle and Doppler frequency in the presence of general array errors. In these algorithms, the DOA matrix method or the time-spatial DOA matrix method firstly separates signal sources. Then, DOA of source is estimated by Total Least Squares. By comparison, the advantages of third algorithm are that the estimating accuracy is the highest of three algorithms and the robusticity to amplitude and phase errors of sensors is strong. But this algorithm is sensitive to setting position errors of sensors. The estimating accuracy of second algorithm and the robusticity to amplitude and phase errors of sensors ar...