Data-Selective Least Squares Methods for Elliptic Localization With NLOS Mitigation

Xiong, Wenxin; Bordoy, Joan; Schindelhauer, Christian; Gabbrielli, Andrea; Fischer, Georg; Schott, Dominik Jan; Höflinger, Fabian; Rupitsch, Stefan J.; So, Hing Cheung

doi:10.1109/lsens.2021.3087422

“…On the other hand, although the SX algorithm guarantees that || pT-TXl || = rTXl-T , it does not necessarily satisfy the identity in Eq. (12).…”

Section: Nonlinearly Constrained Least Squarementioning

confidence: 99%

“…In [8], a nonlinearly constrained least square (NLCLS) solution, defined for a scenario with M RXs and 1 TX, was proposed considering the complete mathematical problem in Eq. (12). By considering all the constraints of the original nonlinear problem in Eq.…”

Section: Nonlinearly Constrained Least Squarementioning

confidence: 99%

“…Other techniques select data based on comparisons of costfunction evaluations over different combinations of TDOA data. The works in [19] and [12] select, respectively, TDOAs in a source location system and TSOAs in a passive location system. They define a fixed number of selected data, and the data set that obtains the least cost is employed to compute the final location.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consistent Volumetric Search for Accurate Passive Coherent Location

RODRIGUEZ¹,

Martins²,

Apolinario³

et al. 2023

Preprint

0

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<p>This paper addresses the problem of selecting suitable time difference of arrival (TDOA) measurements for passive coherent location with multiple-transmitters/receivers. Firstly, we propose a unified mathematical framework for the following target location algorithms: spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS). This paper generalizes the currently available models for scenarios with multiple-transmitters/one-receiver and one-transmitter/multiple-receivers. The SI/SX algorithms use closed-form expressions to tackle the location problem without considering all the nonlinear relationships among the optimization variables. As for the NLCLS algorithm, such nonlinearities are taken into account with the help of nonlinear constraints. Effects like multipath propagation and shadow fading increase TDOA measurement values resulting in outliers. To remove the outliers, we propose three TDOA selection processes. The first one uses a simple comparison rule. The second approach iteratively detects outliers based on cost function comparisons. The last approach divides the search region into cuboids. The cuboid-based approach separates consistent TDOAs from outliers, and its centroid represents a new location estimate. The numerical experiments show that the proposed cuboid-based method has greater robustness when increasing the probability of outliers.</p>

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“…This is because there have been plentiful error-mitigated positioning solutions for the traditional TDOA model in the literature [27], extensible to the HF TDOA localization scenarios here upon proper modifications. For instance, one may employ anomaly detection techniques to identify the outliers [28], or robustify the estimator with other cost functions less sensitive to the deviating samples [27].…”

Section: Simulations and Practical Issuesmentioning

confidence: 99%

Globally Optimized TDOA High-Frequency Source Localization Based on Quasi-Parabolic Ionosphere Modeling and Collaborative Gradient Projection

Xiong

¹

,

Schindelhauer

²

,

So

³

2023

IEEE Trans. Aerosp. Electron. Syst.

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6

0

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We investigate the problem of high frequency (HF) source localization using the time-difference-of-arrival (TDOA) observations of ionosphere-refracted radio rays based on quasiparabolic (QP) modeling. An unresolved but pertinent issue in such a field is that the existing gradient-type scheme can easily get trapped in local optima for practical use. This will lead to the difficulty in initializing the algorithm and finally degraded positioning performance if the starting point is inappropriately selected. In this paper, we develop a collaborative gradient projection (GP) algorithm in order to globally solve the highly nonconvex QPbased TDOA HF localization problem. The metaheuristic of particle swarm optimization (PSO) is exploited for information sharing among multiple GP models, each of which is guaranteed to work out a critical point solution to the simplified maximum likelihood formulation. Random mutations are incorporated to avoid the This work was supported by the state graduate funding coordinated by Uni Freiburg.

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“…PCL solutions either for multiple TXs and one RX, or for multiple RXs and one TX. However, the authors in [12] present a least square solution of a passive location system based on time sum of arrival (TSOA) measurements in a scenario of multiple TXs and multiple RXs. TSOA represents the travel time of the signal reflected in the target and associated with a specific TX-RX pair.…”

Section: Introductionmentioning

confidence: 99%

TDOA Data Selection for Accurate Passive Coherent Location

RODRIGUEZ¹,

Martins²,

Apolinario³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

<p>This paper addresses the problem of selecting suitable time difference of arrival (TDOA) measurements for passive coherent location with multiple-transmitters/receivers. Firstly, we propose a unified mathematical framework for the following target location algorithms: spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS). This paper generalizes the currently available models for scenarios with multiple-transmitters/one-receiver and one-transmitter/multiple-receivers. The SI/SX algorithms use closed-form expressions to tackle the location problem without considering all the nonlinear relationships among the optimization variables. As for the NLCLS algorithm, such nonlinearities are taken into account with the help of nonlinear constraints. Effects like multipath propagation and shadow fading increase TDOA measurement values resulting in outliers. To remove the outliers, we propose three TDOA selection processes. The first one uses a simple comparison rule. The second approach iteratively detects outliers based on cost function comparisons. The last approach divides the search region into cuboids. The cuboid-based approach separates consistent TDOAs from outliers, and its centroid represents a new location estimate. The numerical experiments show that the proposed cuboid-based method has greater robustness when increasing the probability of outliers.</p>

show abstract

Data-Selective Least Squares Methods for Elliptic Localization With NLOS Mitigation

Cited by 16 publications

References 18 publications

Consistent Volumetric Search for Accurate Passive Coherent Location

Consistent Volumetric Search for Accurate Passive Coherent Location

Globally Optimized TDOA High-Frequency Source Localization Based on Quasi-Parabolic Ionosphere Modeling and Collaborative Gradient Projection

TDOA Data Selection for Accurate Passive Coherent Location

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