An evolutionary computing approach for the target motion analysis (TMA) problem for underwater tracks

Ince, Levent; Sezen, Bülent; Saridogan, Erhan; İnce, Hüseyin

doi:10.1016/j.eswa.2008.02.046

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…For the BO-TMA problem in this paper, the quadratic cost function is defined as [ 7 ]

where

is an estimated state that is expected to be the initial target state at

and

is the covariance of the bearing measurement noise. In general, in batch processing techniques, the difference between the measurements and the estimated bearings is normalized with the standard deviation of measurement error, and the square sum of this value is used as the cost function [ 7 , 9 , 10 , 12 ].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, studies on heuristic algorithms that are relatively free from initialization problems have been introduced. Genetic algorithm (GA), a heuristic algorithm, was used in the batch estimation to solve the BO-TMA problem [ 12 ]. The performance of the modified EKF (MEKF) was improved compared to that of the traditional EKF when using heuristic algorithms (i.e., particle swarm optimization, genetic algorithm, cuckoo search) for batch estimation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Shi

Choi

2021

Sensors

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Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton–Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton–Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.

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“…For the BO-TMA problem in this paper, the quadratic cost function is defined as [ 7 ]

where

is an estimated state that is expected to be the initial target state at

and

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Shi

Choi

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The bearings-only target motion analysis (BOTMA) is essentially a non-linear parameter estimation problem. Moreover, the single-station BOTMA system is unobservable and does not have a consistent and feasible solution method [13,14]. Early BOTMA algorithm mainly used prior information to construct deterministic solution formulas, which had poor applicability [15,16].…”

Section: Related Workmentioning

confidence: 99%

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

Zhang,

Wang,

Zhang

et al. 2023

IET Radar Sonar & Navi

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Accurate bearing‐time recordings play a crucial role in hydroacoustic target motion analysis and situation estimation. The target bearing estimated by vector hydrophone in the interference environment suffers from poor display and low signal‐to‐noise ratio. To solve this problem, a two‐stage bearing‐only motion analysis method is proposed. Firstly, the bearing peak information and bearing dispersion are extracted for each sampling moment of the bearing‐time recording. Custom constraint regions and adaptive parameters are then set based on this information. The order truncate average algorithm is improved to enhance the quality of the low‐quality bearing‐time recording map. Secondly, a bearing‐only target motion model is established based on the bearing‐time recording distribution features, and a bearing‐time recording trust interval is constructed. Then a modified pseudo‐linear estimation algorithm is proposed to solve the target motion situation information, including target abeam time, target abeam bearing, target course, velocity‐to‐initial‐distance ratio, and so forth. Finally, by comparing the solved values of the sea trial data with the automatic identification system calculation results, the enhancement effect of the target bearing‐time recording map is significant and the solving error of the target motion elements meets the needs of engineering applications.

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“…The mechanisms of functioning of genetic algorithms are inspired by the mechanisms that are present in living nature, such as, for example, generation of populations, selection, crossing, mutation. Thus genetic algorithm is an optimization algorithm that simulates the process of biological evolution [17]. Although genetic algorithms are one of the best optimization mechanisms for optimizing tasks related to topology and graphs [18,19], there are specific problems associated with their application for this type of problem.…”

Section: Problem Statementmentioning

confidence: 99%

Solution of the Problem of Empty Car Distribution between Stations and Planning of Way-Freight Train Route Using Genetic Algorithms

Prokhorov¹,

Калашнікова²,

Rybalchenko³

et al. 2018

IJET

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In this paper we consider the problem of distributing empty freight cars in a railway polygon. We show how the process can be improved using an optimization model. The optimization model can be characterized as a combination of minimum-cost flow problem with vehicle routing problem. In general, problem of empty railroad car distribution between stations and definition of way-freight train route is presented as integer combinatorial optimization problem. Computational tests show that the model can be solved in acceptable time for real size problems, and indicate that the model generates distribution plans that can improve the quality of the planning process.

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An evolutionary computing approach for the target motion analysis (TMA) problem for underwater tracks

Cited by 12 publications

References 12 publications

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

Solution of the Problem of Empty Car Distribution between Stations and Planning of Way-Freight Train Route Using Genetic Algorithms

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