Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Zaman, Fawad; Qureshi, Ijaz Mansoor; Naveed, A.; Khan, Junaid Ali; Raja, Muhammad Asif Zahoor

doi:10.2528/pierb12022109

Cited by 32 publications

(43 citation statements)

References 28 publications

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“…Evolutionary algorithms are swarm-based intelligent optimization methods, some of which have been investigated for direction finding and localization. Zaman et al [23], [24] proposed a GA hybridized with pattern search for the joint amplitude and DOA estimation using a special array of L-type shape. Lui et al [25] employed PSO for positioning a source with TDOA measurements.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array

Cui

2015

IEEE Trans. Instrum. Meas.

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This paper focuses on direction finding of a signal source using time-difference-of-arrival (TDOA) measurements at a 3-D acoustic sensor array. Two evolutionary computation methods are proposed to solve the direction finding problem, which are the genetic algorithm and the particle swarm optimization algorithm. Sound speed is used in the development of the algorithms, which is estimated based on observed weather parameters and initial direction estimation results from the least square (LS) estimator which is presently the key method in TDOA-based direction finding. All reference-free TDOA measurements are adopted in defining cost function to improve performance. To guarantee fast convergence, an LS estimator is also utilized to provide initial direction estimates for the two swarm intelligent algorithms. Simulation results demonstrate that the proposed methods with a full TDOA set are superior to the Cramer-Rao lower bound with a limited set of referencebased TDOA measurement, significantly outperforming the LS estimator. Extensive field experiments were conducted and there is good agreement between the experimental results and simulation results.Index Terms-Acoustic arrays, array signal processing, artificial intelligence, direction of arrival (DOA) estimation, time-delay arrays.

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Section: Introductionmentioning

confidence: 99%

Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array

Cui

2015

IEEE Trans. Instrum. Meas.

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“…Neural Network approaches have also been used in beamforming [33][34][35][36][37][38][39]. Zaman et al utilized a GA hybridized with a pattern search for DOA analysis [40].…”

Section: Beamformingmentioning

confidence: 99%

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

2017

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An approach for using a Genetic Algorithm (GA) to select radar design parameters related to beamforming and angle of arrival estimation is presented in this article. This was accomplished by first developing a simulator that could evaluate the localization performance with a given set of design parameters. The simulator output was utilized as part of the GA objective function that searched the solution space for an optimal set of design parameters. Using this approach, the authors were able to more than halve the mean squared error in degrees of the localization algorithm versus a radar design using human-selected design parameters. The results of this study indicate that this kind of approach can be used to aid in the development of an actual radar design.

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“…Among EC techniques, genetic algorithm (GA) is considered to be one of the powerful and reliable tools to optimize the problems in any engineering field [11,12]. GA is easy to understand and implement and avoids getting stuck in local minima.…”

Section: Introductionmentioning

confidence: 99%

Null Placement and Sidelobe Suppression in Failed Array Using Symmetrical Element Failure Technique and Hybrid Heuristic Computation

Khan¹,

Qureshi²,

Zaman³

et al. 2013

PIER B

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Abstract-In this paper, we have addressed three major problems of uniform linear array in case of a sensor failure at any position. We assume that sensor position is known. The problems include increase in sidelobe levels, displacement of nulls and diminishing of null depth. The desired null depth is achieved by making the weight of symmetrical counterpart element passive. Genetic algorithm (GA) along with pattern search (PS) is used for reduction of sidelobe levels, and adjustment of nulls. Fitness function minimizing the error between the desired and estimated beam pattern along with null constraints is used. Simulation results for diversified scenarios have been given to demonstrate the validity and performance of the proposed algorithm.

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Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Abstract: Abstract-In this work, we propose a method based

Cited by 32 publications

References 28 publications

Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array

Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

Null Placement and Sidelobe Suppression in Failed Array Using Symmetrical Element Failure Technique and Hybrid Heuristic Computation

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