Hybrid Location Algorithm of TDOA / AOA Based on Extended Kalman Filter

Jiang, Huiyao; Zhang, Kun; Shen, Chong; Zhu, Jigui; Xu, Liwen

doi:10.1109/icct52962.2021.9658015

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…x min ≤ x ≤ x max y min ≤ y ≤ y max (17) As shown in Equation ( 17), x min and x max are the minimum and maximum values of the horizontal coordinates in the range formed by the base station and y min and y max are the minimum and maximum values of the vertical coordinates in the range formed by the base station.…”

Section: Crow Search Algorithm Optimized By Particle Swarm Optimizationmentioning

confidence: 99%

“…The TOA-AOA positioning method combines the TOA and AOA positioning techniques. The fundamental tenet of this system is that the mobile station’s service base station monitors the angle and time at which its transmit signal approaches the mobile station [ 17 ]. The hybrid TDOA/AOA positioning algorithm only needs to synchronize the time of the stationary station and the time of the serving base station, which can be accomplished through the synchronization channel of the base stations.…”

Section: Introductionmentioning

confidence: 99%

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Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cao

Chen

et al. 2023

Biomimetics

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A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.

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Section: Crow Search Algorithm Optimized By Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cao

Chen

et al. 2023

Biomimetics

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“…For reducing the deviations easily generated by traditional positioning methods, many scholars have improved the TDOA/AOA hybrid algorithm [18]. A hybrid positioning strategy based on TDOA/AOA was suggested by Jiang et al to enhance the position performance utilizing just two sites [19]. The sensor networks containing the positioning optimization models based on AOA and TDOA are established, respectively.…”

Section: Introductionmentioning

confidence: 99%

A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation

et al. 2023

Wireless Communications and Mobile Computing

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To address the difficulty in calculating the nonlinear equation of time difference of arrival (TDOA) positioning, as well as the problem of measurement error in the hybrid time difference of arrival/angle of arrival (TDOA/AOA) positioning algorithm, an improved sparrow search algorithm is proposed to optimize positioning, and the optimization mechanism is retained on the basis of improving the performance of the original algorithm. The maximum likelihood estimation method is used to calculate the objective function, and then, the estimated function of the mobile station is used as the fitness function to generate the initial population of sparrows. Then, using particle swarm optimization, optimize the sparrow search algorithm and obtain the population’s optimal solution in order to obtain the optimal position. The simulation results show that, when compared to the existing algorithm, increasing the number of base stations increases the average accuracy of the sparrow search algorithm (SSA) positioning method by 18.54% and 4.5%, respectively, and, when compared to the proposed particle swarm optimization (PSO) positioning method, by 13.79% and 11.6% as the radius increases. The SSA hybrid positioning algorithm performs better in terms of positioning accuracy, convergence speed, and robustness.

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An Improved Indoor Positioning Method by Fusing Ultra-Wideband and Millimeter Wave Radar Technologies

Fan,

Li,

Wang

et al. 2024

2024 6th International Conference on Electronic Engineering and Informatics (EEI)

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Hybrid Location Algorithm of TDOA / AOA Based on Extended Kalman Filter

Cited by 4 publications

References 8 publications

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation

An Improved Indoor Positioning Method by Fusing Ultra-Wideband and Millimeter Wave Radar Technologies

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