Performance Metric on the Best Achievable Accuracy for Hybrid TOA/AOA Target Localization

Li, Yinya; Qi, Guoqing; Sheng, Andong

doi:10.1109/lcomm.2018.2833544

Cited by 73 publications

(35 citation statements)

References 13 publications

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“…Here, we use the simple grid-based algorithm [30]. The grid spacing d 0 , which is the distance between each two candidate targets, is related to the Cramér-Rao lower bound (CRLB) [31] of localization error.…”

Section: Initialization Of Candidate Targetsmentioning

confidence: 99%

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

Zhang

et al. 2019

Sensors

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To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.

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Section: Initialization Of Candidate Targetsmentioning

confidence: 99%

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

Zhang

et al. 2019

Sensors

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“…A number of localization methods have been proposed based on the characteristic parameters of the received signal and the application environment, including timeof-arrival (TOA), 1 time-difference-of-arrival (TDOA), 2 angle-of-arrival (AOA), 3 received-signal-strength (RSS), 4 and hybrid localization methods of various localization techniques. 5,6 These localization methods generally assume that the propagation between the signal source and the sensor is in line-of-sight (LOS). However, obstacles are often impeded in harsh environments such as complex cities or indoors.…”

Section: Introductionmentioning

confidence: 99%

Time-of-arrival–based localization algorithm in mixed line-of-sight/non-line-of-sight environments

Wang

Chang

et al. 2020

International Journal of Distributed Sensor Networks

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A novel time-of-arrival–based localization algorithm in mixed line-of-sight/non-line-of-sight environments is proposed. First, an optimization problem of target localization in the known distribution of line-of-sight and non-line-of-sight is established, and mixed semi-definite and second-order cone programming techniques are used to transform the original problem into a convex optimization problem which can be solved efficiently. Second, a worst-case robust least squares criterion is used to form an optimization problem of target localization in unknown distribution of line-of-sight and non-line-of-sight, where all links are treated as non-line-of-sight links. This problem is also solved using the similar techniques used in the known distribution of line-of-sight and non-line-of-sight case. Finally, computer simulation results show that the proposed algorithms have better performance in both the known distribution and the unknown distribution of line-of-sight and non-line-of-sight environments.

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“…As an efficient tool to evaluate the positioning accuracy, the CRLBs of single metric, such as AoA, RSS and ToF, have been derived in [9]- [11]. In [12], the CRLBs of hybrid time (difference) of arrival (ToA/TDoA) and RSS were derived for wireless sensing network, and the CRLBs of hybrid ToA/AoA for UWB-based scheme and passive cooperative localization in [13], [14], respectively. In this paper, we derive the closed-form CRLB for hybrid AoA/RSS/ToF fingerprinting systems.…”

Section: Introductionmentioning

confidence: 99%

CRLB-based Positioning Performance of Indoor Hybrid AoA/RSS/ToF Localization

Trogh

David

et al. 2019

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Fingerprinting indoor localization provides high positioning accuracy with low cost and easy deployment. Considering the unsatisfying precision of received signal strength (RSS)based fingerprinting, hybrid metrics including angle-of-arrival (AoA) and time-of-flight (ToF), are incorporated to the RSS fingerprinting system. To evaluate the positioning performance of hybrid metrics, the closed-form Cramér-Rao lower bound (CRLB) is derived in this paper. The existence conditions of CRLBs, as well as the relationship of the CRLBs between single and hybrid metrics is revealed. Numerical results based on an office building scenario show that hybrid metrics greatly improve the positioning performance and the robustness to measured standard deviations compared to the single metric's case. Furthermore, hybrid schemes of the AoA/RSS/ToF metrics are also investigated, and simulations reveal that the scheme of AoA/ToF-supporting access points (AP) enhanced with single RSS-supporting APs achieves the best positioning accuracy among all hybrid schemes.

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Performance Metric on the Best Achievable Accuracy for Hybrid TOA/AOA Target Localization

Cited by 73 publications

References 13 publications

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

Time-of-arrival–based localization algorithm in mixed line-of-sight/non-line-of-sight environments

CRLB-based Positioning Performance of Indoor Hybrid AoA/RSS/ToF Localization

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