Researches On Cyclostationarity-based DOA Estimation Techniques

Direction of arrival (DOA) estimation techniques have been widely used in many military and civil areas, such as radar, sonar, communication, biological engineering and so on. Although the ordinary spatial spectrum estimation techniques break the Rayleigh Resolution Limit, they do not sufficiently exploit the signals'time domain information. And they usually have some drawbacks in multiple signal processing and low SNR signal processing. With the development of researches, people found that many man-made and natural signals possess the character of cyclostationarity. And, by exploiting the cyclostationarity of the signals, better DOA estimation performances in signal selective direction finding, interference or noise suppression and multiple signal processing can be achieved. Thus, the cyclostationarity-based DOA estimation techniques have been gradually becoming one of the hot research topics in array signal direction finding areas.Most of the cyclostationarity-based DOA estimation researches are focused on the one-dimensional (1-D) case. As for the two-dimensional (2-D) case, which is much more required, the existing researches are inadequate, and they commonly suffered from the lag selecting problem. Besides, few researches on the non-uniform array-based 2-D DOA estimation can be found. Considering the above limitations along with the practical environments with coherent signals and wideband signals, the following topics on the cyclostationarity-based DOA estimation techniques are investigated in this dissertation.To improve the robustness of the DOA estimation, which is threatened by the lag selecting problem, the ML-CCDM and the SML-CCDM are proposed. Both methods are based on the idea of combing the multiple lag sampling and the aperture extension oriented spatial-time equivalent array constructing. The ML-CCDM utilizes two parallel uniform linear arrays. By exploiting the conjugate cyclic correlations of the sensor outputs at multiple lags, this method not only avoids the optimal lag selecting problem or the 2-D spectrum peak searching, but also obtains a space-time equivalent array with twice the aperture of the real array. Compared with the CCDM, the ML-CCDM achieves better performance in estimation accuracy, noise suppressing and multiple signal processing. Based on the framework of the ML-CCDM, the SML-CCDM not only gets rid of the drawbacks of two parallel geometry arrays, but also obtains a performance which is almost the same as that of the ML-CCDM with fewer sensors.By combining the minimum redundancy linear array (MRLA) with the multiple lag sampling-based spatial-time equivalent arrays constructing idea, two sparse arrays along with the corresponding 2-D DOA estimation methods are proposed. These methods extend the application of MRLA to the 2-D DOA estimation of conjugate cyclostationary signals. The two sparse array-based methods not only inherit the merits of ML-CCDM and SML-CCDM separately, but also achieve larger array aperture with the same number of sensors, which also leads to an improved accuracy in DOA estimation. Besides, when compared with the uniform array, the sparse array possesses larger sensor distances. Therefore, the effects of mutual coupling in real-world implementations can be reduced by the above sparse array-based methods.By combing the spatial-time equivalent array with the existing decorrelating methods, a VA-FBSS method for DOA estimation of coherent signals is proposed. The VA-FBSS avoids the optimal lag selection problem faced by the FBSS-CMUSIC, and obtains a better performance in accuracy. Besides, when compared with the CFBLP, remarkable improvements in performance can be achieved on the condition of low SNR and relatively closer impinging signals. Considering the VA-FBSS is a 1-D method, the virtual array-based spatial smoothing technique is also extended to the 2-D case.Based on the existing cyclic autocorrelation-based method, a cyclic crosscorrelation-based signal processing model along with the corresponding 2-D DOA estimation method for cyclostationary signals is induced. This method improves the accuracy of the DOA estimation.