Exact Formulation for Stochastic ML Estimation of DOA

Chen, Haihua; Suzuki, Masato

doi:10.1587/transfun.e93.a.2141

Cited by 12 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed in literatures [10,24], the criteria of SML above, i.e., Equation (10) is still valid except for some rare cases for example for very low SNRs or very few snapshots. And also as we will show next that our proposed Joint-PSO algorithm is general, it can also be applied to the criteria proposed in [10]. Since this paper focuses on the problem of reducing the computational complexity of SML estimation and our proposed algorithm is general, the proposed algorithm is applied to the SML criteria (10).…”

Section: System Model and Problem Formulationsmentioning

confidence: 98%

“…Note that there are literatures [10,24] discussing about the rigor of SML criteria derived above. In [10], a constraint condition in which the estimated signal covariance matrix must be non-negative definite is imposed and a more complex criteria is derived.…”

Section: System Model and Problem Formulationsmentioning

confidence: 99%

“…In [10], a constraint condition in which the estimated signal covariance matrix must be non-negative definite is imposed and a more complex criteria is derived. As discussed in literatures [10,24], the criteria of SML above, i.e., Equation (10) is still valid except for some rare cases for example for very low SNRs or very few snapshots. And also as we will show next that our proposed Joint-PSO algorithm is general, it can also be applied to the criteria proposed in [10].…”

Section: System Model and Problem Formulationsmentioning

confidence: 99%

“…The representative algorithms are Deterministic Maximum Likelihood (DML) [8], Stochastic Maximum Likelihood (SML) [9,10] and Weighted Subspace Fitting (WSF) [11]. These algorithms can deal with small number of snapshots.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Modification of PSO Algorithm for SML Estimation of DOA

Chen

Liu

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.

show abstract

Section: System Model and Problem Formulationsmentioning

confidence: 98%

Section: System Model and Problem Formulationsmentioning

confidence: 99%

Section: System Model and Problem Formulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Modification of PSO Algorithm for SML Estimation of DOA

Chen

Liu

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…They can also handle small number of snapshots with much higher resolution. It has also been shown that the resolution of WSF is very high when the number of independent signals is known [16], [19]. However, when the number of independent signals is not correctly estimated, the performance are strongly deteriorated.…”

Section: Introductionmentioning

confidence: 99%

A Novel AWSF Algorithm for DOA Estimation in Virtual MIMO Systems

Chen¹,

Pan

Tian³

et al. 2013

IEEE J. Select. Areas Commun.

Self Cite

View full text Add to dashboard Cite

Both Multiple Input and Multiple Output (MIMO) and Smart Antenna (SA) have been widely accepted as promising schemes to improve the spectrum efficiency and coverage of mobile communication systems. This paper addresses the issue of Direction of Arrival (DOA) estimation in Virtual MIMO (VMIMO) systems which adopt SA simultaneously. First of all, we propose a VMIMO scheme for DOA estimation in which a set of User Equipments (UEs) are grouped together to simultaneously communicate with the Base Station (BS) on a given Resource Block (RB). In this scheme, the BS has multiple antennas and can estimate DOA of each UE in the group simultaneously. However, in practical environment because of reflection and refraction of signals, the received signals may be coherent. It is desirable that the DOA estimation in VMIMO could provide high resolution when the number of independent signals is unknown, which can not be achieved by existing algorithms. In order to solve this problem, we propose an Automatic Weighted Subspace Fitting (AWSF) algorithm that can detect the number of independent signals automatically and show accurate DOA estimation. Then with consideration of computational cost and sampling cost while using the AWSF algorithm, we propose a utility factor to determine the optimal number of UEs in VMIMO scheme, i.e., the order of VMIMO. Finally, the performance of the proposed AWSF algorithm and the efficiency of the utility factor criterion are demonstrated by simulations.

show abstract