Practical considerations of optimal three-dimensional indoor localization
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The problem of locating objects and people in indoor environments is lately gaining high interest in research and industry. In this context, the geometrical constellation of source and sensors plays an important role and heavily influences the accuracy for the position estimate. This paper uses existing optimal solutions of the problem of three-dimensional TDOA localization, and relates them to practical application. The extension is necessary, since the optimal solution is only valid for one single position and not for a complete area, where a localization capability is desired. Therefore, it is shown by simulations that the optimum is very useful for localization purposes in typical indoor scenarios. In addition, real-world measurements using an ultra-wideband (UWB) localization system have been carried out in two geometrical scenarios. It can be shown that the simulations, which are based on theoretical derivations, are a good approximation of the reality. Finally, it can be demonstrated that the optimal solution can be adapted to typical indoor environments and still yield accurate three-dimensional positioningKeywords:
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본 논문에서는 UWB (Ultra Wide Band) 시스템에서 PSO (Particle Swarm Optimization)를 사용하는 향상된 TDoA (Time Difference of Arrival) 무선측위 기법을 제안한다. 제안된 기법은 TDoA 파라미터 재추정과 태그(Tag) 위치 재측정을 수행하는 두 단계로 구성된다. 이들 두 단계에서 PSO 알고리즘은 무선측위 성능 향상을 위해 고용된다. 첫 번째 단계에서 TDoA 추정 오차를 줄이기 위해, 제안된 기법은 전형적인 TDoA 무선측위 방식으로부터 얻어진 TDoA 파라미터를 재추정한다. 두 번째 단계에서 무선측위 오차를 최소화시키기 위해, 첫 번째 단계에서 추정된 TDoA 파라미터를 가지고 제안된 기법은 태그의 위치를 다시 측정한다. 모의실험 결과, 제안된 기법은 LoS (Line-of-Sight)와 NLoS (Non-Line-of-Sight) 채널 환경에서 모두 전형적인 TDoA 무선측위 방식에 비해 우수한 무선측위 성능을 달성하는 것을 확인할 수 있었다.
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Aiming at the position problem of emitter on the ground, this paper puts forward one TDOA/AOA combined position algorithm. In this algorithm, one sensor can be placed on one fixed platform on the ground to measure the azimuth and pitch angle between this sensor and the emitter. Another sensor can be placed on one moving platform, and the different TDOA (Time Difference Of Arrival) can be measured at different time, then the LS (Least Square) algorithm can be reached to solve the evaluated value of the emitter’s position. The simulation results show that this algorithm can increase the position precision effectively, especially the more TDOA measurements are used, the more position precision is.
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Decentralized time difference of arrival (TDOA) measurement in large wireless sensor networks (WSNs) is helpful to reduce transmission and computation costs, but it also brings the intractable problem of source position solving. This paper investigates TDOA-based localization with uncommon reference nodes (URNs). We propose an efficient localization algorithm taking advantage of both the algebraic and the iterative methods. More importantly, the localization-refused area (LRA), caused by the sensor-source geometries, is theoretically analyzed. Simulation results are included to examine the performance of the proposed algorithm and to demonstrate the LRAs with different network topologies.
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Estimate the randomly deployed nodes position of a Wireless Sensor Network(WSN) by TDOA(Time Difference of Arrival),determining the distance of the nodes is needed.Sometimes,the localization accuracy of TDOA can not meet the requirement.Against this problem,an improvement algorithm on TDOA localization is addressed and the improvement algorithm reduces the error of the localization.At last,the advantage of the improvement algorithm is proved by the simulation.
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An algorithm for solving the simultaneous hyperbolic equations that arise in multilateration using time difference of arrival measurements (TDOA) is presented. This weighted, non-iterative algorithm is able to reduce error by more than 50% over unweighted variations of the algorithm. This algorithm was developed for use in a passive multistatic sensor network. By using low gain antennas and TDOA, we are able to provide platforms with situational awareness while maintaining a low RCS.
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We propose an improved TDoA (Time Difference of Arrival) localization scheme based on PSO (Particle Swarm Optimization) in UWB (Ultra Wide Band) systems. The proposed scheme is composed of two steps: the re-estimation of TDoA parameters and the re-localization of tag position. In both steps, the PSO algorithm is employed to improve the performance. In the first step, the proposed scheme re-estimates the TDoA parameters obtained by traditional TDoA localization to reduce the TDoA estimation error. In the second step, the proposed scheme with the TDoA parameters estimated in the first step, re-localizes the tag to minimize the location error. Simulation results show that the proposed scheme achieves better location performance than the traditional TDoA localization in various channel environments.
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Locating ability to decide target position is exceptionally valuable in numerous application fields. Multilateration is one of the method to get the position prediction using hyperbolic algorithm. This method exploits the Time Difference of Arrival (TDOA) information from least four receivers to get 3D position of target. This paper centers around the passive multilateration by using target's communication burst signal. Received burst signals are being correlated to get the TDOA. At that point by utilizing TDOA and receiver positions information, we can calculate the multilateration algorithm. This paper additionally shows the accuracy of position prediction in some example places of target.
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This chapter contains section titled: Extension of time position under JCT 98 Extension of time position under IFC 98 Extension of time position under MW 98 Extension of time position under WCD 98 Extension of time position under PCC 98 Extension of time position under MC 98 Extension of time position under TC/C 02 Extension of time position under MPF 03
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It is widely acknowledged that random receiver position error can significantly impair the performance of Time Difference of Arrival (TDOA) localization systems. This paper presents a method incorporating a calibration source whose position is precisely known into the localization system, thereby mitigating the impact of positioning errors caused by random receiver position inaccuracies. The proposed algorithm comprises two stages: first, it calibrates receivers' position error, and then it estimates the target position using the TDOA measurements. Numerical simulations demonstrate the effectiveness and robust performance of this algorithm.
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Position error
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The paper presents results of localization of an acoustic emission source in the two-dimensional space based on signals acquired using microphones and the TDoA (Time Difference of Arrival) technique. The delay between signals in the channels is measured using their cross-correlation. Then the location is obtained using multilateration. The Firefly Algorithm and Gradient Descent Algorithm are used for the purpose of estimating the location.
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Direction of arrival
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