New Approaches for Two-Dimensional DOA Estimation of Coherent and Noncircular Signals with Acoustic Vector-sensor Array

This thesis is mainly concerned with the two-dimensional direction of arrival (2D-DOA) estimation using acoustic vector-sensor array for coherent signals and noncircular signals. As for coherent signals, the thesis proposes two algorithms, namely, a 2D-DOA estimation algorithm with acoustic vector-sensor array using a single snapshot, and an improved 2D-DOA estimation algorithm of coherent signals. In the first algorithm, only a single snapshot is employed to estimate the 2D-DOA, and the second is an improved algorithm based on the method of Palanisamy et al. Compared to the existing algorithm, the proposed algorithm has the following advantages: (1) lower computational complexity, (2) better estimation performance, and (3) acquiring automatically-paired 2D-DOA estimates. As for noncircular signals, we propose real-valued space PM and ESPRIT algorithms for 2D-DOA estimation using arbitrarily spaced acoustic vector-sensor array. By exploiting the noncircularity of incoming signals to increase the amount of effective data, the proposed algorithms can provide a better 2D-DOA estimation performance with fewer snapshots, which means a relatively lower sample rate can be used in practical implementations. Compared with the traditional PM and ESPRIT, the proposed algorithms provide better estimation performance while having similar computational complexity. Furthermore, the proposed algorithms are suitable for arbitrary arrays and yield paired azimuth and elevation angle estimates without requiring extra computationally expensive pairing operations

Approximate maximum likelihood estimation of two closely spaced sources

The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

Comparison of SAGE and classical multi-antenna algorithms for multipath mitigation in real-world environment

The performance of the Space Alternating Generalized Expectation Maximisation (SAGE) algorithm for multipath mitigation is assessed in this paper. Numerical simulations have already proven the potential of SAGE in navigation context, but practical aspects of the implementation of such a technique in a GNSS receiver are the topic for further investigation. In this paper, we will present the first results of SAGE implementation in a real world environmen