The Research On Key Technology Of Direction Of Arrival Estimation For Array Signal Processing

Direction-of-arrival(DOA) estimation based on the antenna array is an important research area in array signal processing. The main goal of DOA estimation is to estimate parameters such as the DOA of the spatial incident angle, the number of source signals and the frequency of the source signals by making full use of the received measurement data of the sensor array which are arranged in a certain way in the space. DOA estimation has been widely applied in the field of military and civilian including radar, passive sonar, biomedicine, radio astronomy, seismic exploration, etc. In this thesis, the key technologies of DOA estimation are studied. The main contributions of the thesis are summarized as follows.Firstly, based on the uniform linear array(ULA), a low-complexity approach called MFOC-OPM algorithm is proposed. In the proposed MFOC-OPM algorithm, the MFOC matrix is achieved via removing the redundant information encompassed in the primary FOC. Meanwhile, the the extended effective aperture of virtual array keeps unchanged, and then the direction-of-arrivals(DOAs) estimation of source signals can be resolved by exploiting the OPM. The simulation results indicate that, the proposed MFOC-OPM algorithm can reduce computational complexity signifycantly. Based on the aforementioned MFOC-OPM algorithm, a high-precision and low-complexity approach called TFOC-OPRM algorithm is also presented. In the proposed algorithm, the Toepltiz structure of the MFOC matrix is recovered, and then the DOAs of incident signals can be estimated depending on the polynomial rooting method. The simulation results indicate that the proposed TFOC-OPRM algorithm can improve the estimation performance, as well as reduce computational complexity significantly.Secondly, for two parallel uniform linear arrays(ULAs), a novel two dimensional(2-D) DOA estimation algorithm based on matrix reconstruction is proposed to achieve the DOAs of coherent source signals. In the proposed algorithm, two equivalent covariance matrices are reconstructed by utilizing cross-correlation information of received data between the two parallel ULAs and the reference element, and then the decorrelation of the coherent signals can be achieve. Meanwhile, 2-D DOA estimation of the coherent signals can be estimated by using EVD of the new constructed matrix. The simulation results show that the proposed algorithm can not only achieve high estimation performance, but also resolve more number of signals.Thirdly, a spatial differencing algorithm for DOA estimation is proposed to cope with the scenario where a number of uncorrelated and coherent narrowband signals simultaneously impinge on the far-field of the ULA. The DOAs of uncorrelated sources are firstly estimated by utilizing the property of the moduli of eigenvalues of the DOA matrix. Afterwards, the contributions of uncorrelated sources and the interference of noise are eliminated completely by exploiting the improved spatial differencing technique and only the coherent components remain in the spatial differencing matrix. Finally, the remaining coherent sources can be resolved by performing the improved spatial smoothing scheme on the spatial differencing matrix. The simulation results show that the presented algorithm can resolve more number of sources than that of the array elements and distinguish the uncorrelated and coherent sources that come from the same direction, as well as improve the estimation performance.Finally, another Toeplitz matrix reconstruction-based orthonormal propagator method called TMR-OPM algorithm is proposed. The coherency of incident signals is firstly decorrelated through reconstructing a Toeplitz matrix formed from FOC. Finally, the coherent signals can be resolved by performing the OPM. Simulation results indicate that the proposed algorithm can provide significantly lower computational complexity and yield good estimation performance especially in low SNR.