Threshold region performance of deterministic maximum likelihood DOA estimation of multiple sources

2003 ◽  
Author(s):  
F. Athley
Keyword(s):  
Maximum Likelihood ◽  
Doa Estimation ◽  
Threshold Region ◽  
Multiple Sources ◽  
Deterministic Maximum Likelihood

scholarly journals General Improvements of Heuristic Algorithms for Low Complexity DOA Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Haihua Chen ◽  
Haoran Li ◽  
Mingyang Yang ◽  
Changbo Xiang ◽  
Masakiyo Suzuki
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Heuristic Algorithms ◽  
Super Resolution ◽  
Doa Estimation ◽  
Low Complexity ◽  
Evolutionary Strategies ◽  
Deterministic Maximum Likelihood ◽  
Dimensional Optimization ◽  
Number Of Particles

Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization. Traditional heuristic algorithms usually need a large number of particles and iteration times. As a result, the computational complexity is still a bit high, which prevents the application of these super-resolution techniques in real systems. To reduce the computational complexity of heuristic algorithms for these super-resolution techniques of DOA, this paper proposes three general improvements of heuristic algorithms, i.e., the optimization of the initialization space, the optimization of evolutionary strategies, and the usage of parallel computing techniques. Simulation results show that the computational complexity can be greatly reduced while these improvements are used.

scholarly journals The upper bound of multi-source DOA information in sensor array and its application in performance evaluation

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Weilin Tu ◽  
Dazhuan Xu ◽  
Ying Zhou ◽  
Chao Shi
Keyword(s):  
Performance Evaluation ◽  
Upper Bound ◽  
Array Signal Processing ◽  
Additive White Gaussian Noise ◽  
Doa Estimation ◽  
Posteriori Probability ◽  
Theoretical Expression ◽  
Source Information ◽  
Multiple Sources ◽  
Dimensional Parameter

Abstract Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform linear array. At high SNRs and under the sparse-source assumption obtained is the upper bound of DOA information contained in K sparse sources which can be regarded as the sum of all single-source information minus the uncertainty of sources’ order logK!. Moreover, because of the uncertainty of multi-sources’ order, the posteriori probability distribution of DOA no longer obeys single peak Gaussian distribution so that the mean square error is unsuitable in evaluating the performance of multi-dimensional parameter estimation. Consequently, entropy error (EE) is used as a new performance evaluation metric, whose relationship with DOA information is given.

scholarly journals Effect of varying Number of Spacing between Antenna Elements, Snapshots, SNR on AP ML, AP-SSF and ESPRIT Algorithm

2021 ◽  
Vol 11 ◽  
pp. 143-150
Author(s):  
Vinod Kumar ◽  
Sanjeev Kumar Dhull
Keyword(s):  
Signal Processing ◽  
Maximum Likelihood ◽  
Key Words ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Performance Comparison ◽  
Direction Of Arrival Estimation ◽  
Multiple Sources ◽  
Alternating Projection ◽  
Esprit Algorithm

The direction of arrival estimation is the main key problem in array signal processing. In this paper, the alternating projection maximum Likelihood (AP-ML), Alternating projection sub space framework (APSSF) and ESPRIT algorithm are studied. The simulation is performed in MATLAB for single and multiple sources. The effect of the varying number of spacing between antenna elements, number of snapshots and SNR are studied. The performance comparison shows that ESPRIT algorithm performs better as compared to the AP-ML and AP-SSF. Key-Words: - AP-ML, AP-SSF, Direction of Arrival, ESPRIT, Snapshots, SNR

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