Passive Geolocation with Unmanned Aerial Vehicles using TDOA-AOA Measurement Processing

Fokin, Grigoriy

doi:10.23919/icact.2019.8702023

Cited by 21 publications

(2 citation statements)

References 25 publications

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“…An interesting approach to achieving multiple sensor localization with just two sensors suggests using a stationary sensor and a moving sensor [44]. This allows for TDOA/AOA 17 In a two-dimensional localization problem, a non-azimuth angle lies outside the solution space and cannot be used directly to locate the target.…”

Section: ) Limited Sensorsmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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In multilateration, distances between an unknown location and several known points are used to determine the location of the unknown point. Papers on algorithms for multilateration, such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA), have proliferated in the literature. These use the same equations, but are often written in a way that hides the underlying geometry and make it difficult to see the similarity in approaches between different papers. This paper provides a review of TOA and AOA-based localization approaches, with a focus on linear positioning algorithms. This paper presents simple and consistent versions of TOA, TDOA, and AOA position equations with intuitive explanations for how those equations relate to the geometry of the localization problem. This perspective can make it easier to implement these algorithms in a way that produces stable results and provides insight into the underlying geometric problem. We also review the literature relating to TDOA and AOA localization algorithms and multistatic radar.

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Section: ) Limited Sensorsmentioning

confidence: 99%

A Review of Linear Multilateration Techniques and Applications

Widdison,

Long

2024

IEEE Access

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“…In 2002, a hybrid TDOA/AOA placement method was presented. 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].…”

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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