Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

Boccato, Levy; Krummenauer, Rafael; Attux, Romis; Lopes, Amauri

doi:10.1016/j.sigpro.2011.12.004

Cited by 33 publications

(41 citation statements)

References 28 publications

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“…Usually, it is set to be 0.5 [19,20,25]. In our research, we find that when w is relatively large between 0.4 to 0.9, the particles move dramatically, when w is small between 0.4 to 0.9, the particles move smoothly.…”

Section: Pso Algorithm For Smlmentioning

confidence: 64%

“…Generally, the value of w should be between 0.1 to 0.9 and is usually set to be 0.5;

c_{1} = c_{2} = 2

. [19,20]…”

Section: Pso Algorithm For Smlmentioning

confidence: 99%

“…To solve the multi-dimensional non-linear optimization problem of SML estimation, there are some classic solving algorithms such as Alternating Minimization (AM) algorithm [9], Genetic Algorithm (GA) [17], ant colony algorithm [18] and Particle Swarm Optimization (PSO) algorithm [19,20]. Especially, the PSO algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Modification of PSO Algorithm for SML Estimation of DOA

Chen

Liu

et al. 2016

Sensors

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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.

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Section: Pso Algorithm For Smlmentioning

confidence: 64%

“…Generally, the value of w should be between 0.1 to 0.9 and is usually set to be 0.5;

c_{1} = c_{2} = 2

. [19,20]…”

Section: Pso Algorithm For Smlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Modification of PSO Algorithm for SML Estimation of DOA

Chen

Liu

et al. 2016

Sensors

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“…In general, likelihood function is a nonlinear and multimodal cost function that makes it less attractive in applications. The exponential complexity involved in solving the ML problem has led to the proposal of various computationally efficient techniques, which include expectation maximization (EM) [3][4][5][6] , space alternating generalized EM (SAGE) [3][4][5], genetic algorithm (GA) [7], simulated annealing (SA) [8], particle swarm optimization (PSO) [9,10],differential evolution (DE) [10], clonal selection algorithm(CLONALG), etc.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with GA and SA, PSO is generally characterized as simpler in concept, easier to implement, and faster in convergence. The reference [10] gives some comparative results for optimization performances of ML estimate adopting means such as PSO, DE and CLONALG. The nature search methods can offer higher quality estimates, with a higher chance to attain the global optimum, and are less sensitive to initialization, the angular resolution and source correlation.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to ML DOA estimation based on eigenfiltering and stochastic search

Sun

2012

2012 IEEE 14th International Conference on Communication Technology

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The paper proposes a novel approach that a new likelihood function is derived from observation data after filtered with eigenfilters, and hybrid gravitational search algorithm (H-GSA) optimization is collaboratively applied to maximum likelihood (ML) estimation of the direction of arrival (DOA) parameters of multiple signals impinging on a sensor array. This method prevents the ML estimation performances from deteriorating severely where the angular separation between signal sources is small and the SNR / sample size are low. Simultaneously due to the use of H-GSA, we make direct maximization of likelihood realistic in practice. In order to examine the performances of the proposed method, four kinds of situations are designed. Simulation results indicate that the proposed method offers significant performance enhancement at low signal to noise ratios, and hybrid GSA stochastic search technique is therefore efficient and reliable.

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Modeling of the operating characteristics of photovoltaic modules based on particle swarm optimization

Provensi

Grala

Zukeram

et al. 2022

Env Prog and Sustain Energy

Self Cite

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This work evaluates the use of particle swarm optimization to extract the parameters that represent a photovoltaic cell in the model of one diode and five parameters (1D5P) to obtain current versus voltage characteristic curves (I Â V) of a photovoltaic module operating under a wide range of temperature and irradiation scenarios. In all the modeling and simulation processes, only information of the module's data sheet provided by the manufacturers was used, without depending on results from other simulations or external data collections. To validate the study, the simulation of the current versus voltage (I Â V) curves of a commercial photovoltaic module was made, and this was compared with data provided by the manufacturer and also compared with Chenni's method, which is consolidated by the literature. Errors of less than 0.3% were obtained for the simulations performed under the standard test conditions and in temperature and irradiation situations commonly found in practice, as is the case, of G = 700 W/m 2 and T c = 40 C, the 1D5P method showed an average error of approximately 1%, slightly surpassing the Chenni's method where the error was approximately 1.4%. Statement of Industrial Relevance:In developing countries, such as Brazil, a significant increase in the portion referring to solar energy in the Brazilian energy grid is noted. As in 2016, this portion was only 0.02%, though in 2020, according to the Brazilian Solar Energy Association (ABSOLAR), 1 this portion had an increase of 75 times and reached the value of 1.5%. In addition, ABSOLAR's forecast for 2030 is that solar energy will reach 10% of the Brazilian energy grid. One of the reasons for this is that industrial consumer units are increasingly looking for alternative energy sources due to high energy inflation. Photovoltaic generators are an excellent option for industries because they are clean and sustainable energy. 2 It is silent and brings a financial return for the investor. Although, even with all the benefits, the photovoltaic generator has a high acquisition cost, especially for consumer units that demand much energy, as is the case of industries. Thus, correct and precise dimensioning for the photovoltaic generator is necessary. One way to increase the accuracy of dimensioning is to use simulation of the photovoltaic modules, which is the objective of this article.Novelty or Significance: The developed method uses the particle swarm optimization with constriction factor to simulate I Â V curves of a photovoltaic module for any

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Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

Cited by 33 publications

References 28 publications

A Novel Modification of PSO Algorithm for SML Estimation of DOA

A Novel Modification of PSO Algorithm for SML Estimation of DOA

A novel approach to ML DOA estimation based on eigenfiltering and stochastic search

Modeling of the operating characteristics of photovoltaic modules based on particle swarm optimization

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