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Maximum likelihood estimation of direction of arrival using an acoustic vector-sensor
Dovid Levin1, Emanuël A P Habets, Sharon Gannot
1Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
The Journal of the Acoustical Society of America
|February 23, 2012
Summary
This study introduces a new maximum likelihood (ML) estimator for direction of arrival (DOA) using vector-sensors. The ML estimator offers superior accuracy in noisy conditions compared to existing methods.
Area of Science:
- Signal Processing
- Array Signal Processing
- Electromagnetics
Background:
- Direction of Arrival (DOA) estimation is crucial for various applications.
- Traditional methods struggle with isotropic noise and internal device noise.
- Vector-sensors offer enhanced capabilities for DOA estimation.
Purpose of the Study:
- To develop a novel Maximum Likelihood (ML) estimator for DOA using vector-sensors.
- To analyze the performance of the ML estimator against existing methods.
- To investigate the computational efficiency of the proposed algorithm.
Main Methods:
- Derivation of a Maximum Likelihood (ML) DOA estimator for vector-sensors.
- Comparison with Steered Response Power (SRP) maximization.
- Analysis of computational complexity and asymptotic efficiency.
Main Results:
- The ML estimator is a special case of SRP maximization.
- A computationally inexpensive algorithm is presented for SRP maximization.
- The ML estimator achieves asymptotic efficiency, outperforming existing methods in Mean Square Angular Error (MSAE).
Conclusions:
- The proposed ML estimator provides superior DOA estimation accuracy.
- The ML estimator demonstrates improved performance in the presence of isotropic noise.
- The beampattern of the ML estimator aligns with minimum power distortionless response beamformers for signal enhancement.
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