Performance Analysis Of Typical Spatial Spectrum Algorithms

Spatial spectrum estimation is one of the two research areas in array signal processing, which is used in radar, communication, biomedical engineering and many other areas of the military and the national economy. Within the system processing bandwidth, spatial spectrum estimation technique has greatly improved the space signal estimation accuracy of the angle, the angle resolution and the accuracy other relevant parameters. Spatial spectrum estimation is usually named DOA estimation.In this thesis, it focuses on the theme of spatial spectrum estimation, systematically analyze some classic algorithms, described in detail and simulate related the algorithms. Finally, the performance of the MUSIC algorithm, the ESPRIT algorithm and the SAGE algorithm were compared.First, subspace decomposition algorithms are introduced. There are MUSIC algorithm and ESPRIT algorithm. Then analyze detailed the character of MUSIC algorithm and simulate it. As the changing of the SNR and array spacing, simulate the relationship of the MUSIC spectrum and angle. The theory of the ESPRIT DOA estimation algorithm is analyzed detail, its basic idea is estimation of signal parameters via rotational invariance subspace. It is required that the geometric structure of array is invariant. As the SNR and array space changing, the extracting angle of the ESPRIT is simulated. Based on above, we compare the statistical properties of the two algorithms.Second, SAGE algorithm is analyzed. It can estimate the broadband channel angle. We introduce that SAGE use in the channel measurement and the realization of the channel estimation: form the definition of the emission and receive signal and propagation model beginning, gradually introduce the principle of the SAGE algorithm (E step and M step). Through simulation, the estimation angle of arrival, departure angle and delay are achieved. Finally, at the condition of different number of array element and different array element spacing, we compare the performance of subspace decomposition algorithm and SAGE algorithm in case of narrow-band signals. The result is that the performance of SAGE algorithm is best, but the maximum of the computation of the SAGE algorithm. The performance of ESPRIT algorithm is worst, but the fastest of the computing speed.