Constrained total least-squares localisation algorithm using time difference of arrival and frequency difference of arrival measurements with sensor location uncertainties

Yu, Hongyi; Huang, Gan; Gao, Jun

doi:10.1049/iet-rsn.2011.0205

Cited by 38 publications

(37 citation statements)

References 11 publications

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“…ρ (ij,g) , ρ (ij,0) Distance between the assessed and reference locations (defined by (29) and (38), respectively). n…”

Section: (Ij) Bpmentioning

confidence: 99%

“…k in the pre-filtering procedure is the reference, (29) becomes: (38) where superscript 0 represents the prediction. Similar to the calculation of P…”

Section: B Assessment Of Bpes By Crvmentioning

confidence: 99%

“…Cooperative localization is often modeled as geometric optimization problems. The linear/nonlinear/weighted least squares based estimator and maximum likelihood (ML) based estimator are frequently used [12], [23], [30], [31], [38]. Convex relaxation is usually employed by transforming the nonconvex problems to convex one to find the global optimal solution [9], [11], [13], [39].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Yin

Deng

2021

IEEE Trans. Cybern.

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Localization plays a key role in Internet of Things (IoT). This paper proposes a novel intelligent cooperative multisensor localization method called Edge Cloud Cooperative Localization (ECCL) which has the range and angle observations from the neighbour nodes along with the location observations from an absolute coordinate localization system like Global Positioning System (GPS). The edge cloud structure is proposed which employs several distributed Kalman Filters (KFs) in sensor nodes edge and a centralized cooperative fusion unit in the cloud. For a robust fusion, a Cooperative Redundancy Validation (CRV) method is proposed to detect the outliers. The proposed ECCL scheme has the advantages of both distributed and centralized localization, which satisfies the needs of high reliability and high accuracy, especially when sensor nodes have limited computational resources. The simulation and experiment results show that our proposed ECCL algorithm outperforms the other schemes both in outlier detection and localization accuracy.

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“…ρ (ij,g) , ρ (ij,0) Distance between the assessed and reference locations (defined by (29) and (38), respectively). n…”

Section: (Ij) Bpmentioning

confidence: 99%

“…k in the pre-filtering procedure is the reference, (29) becomes: (38) where superscript 0 represents the prediction. Similar to the calculation of P…”

Section: B Assessment Of Bpes By Crvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Yin

Deng

2021

IEEE Trans. Cybern.

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“…The original two-stage method was recently general ized to the scenario where the sensor locations are subject to random errors [5,6]. The newly obtained two-stage solu tion inherits the advantages of being closed-form, not requir ing proper initialization as in the iterative method and hav ing lower computational load than e.g., the constrained total least squares (CTLS) algorithm [7] and the multidimension al scaling (MDS)-based method [8]. [9] examined the bias of two-stage WLS algorithm in the case where there is no sensor position errors.…”

Section: Introductionmentioning

confidence: 99%

BIAS analysis of an algebraic solution for TDOA localization with sensor location errors

Yang

Deng

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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The nonlinearity inherent in the time difference of arrival (TDOA)-based source localization problem leads to bi ased source location estimates. The estimation bias of the closed-form TDOA-positioning technique,two-stage least squares(TSWLS) technology was established analytically in previous works. This paper extends the theoretical develop ments in the case where there exists no sensors location errors to investigate the bias of TDOA-positioning in the presence of sensor location errors. Specifically, the estimation bias of the algebraic two-stage TDOA localization algorithm proposed is derived. Simulations validate the obtained theoretical re sults. It is shown that different from the findings in previous works where the estimation bias of the two-stage solution mainly comes from its Stage-l processing, both stages of the localization algorithm considered in this work can introduce significant estimation biases when sensor location errors are present.Index Terms-time difference of arrival, bias analysis, source localization, sensor location error

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“…The observations can be carried out through measurement of Angle of Arrival (AoA) [3], [4], [5], Time Difference of Arrival (TDoA) [6], [7], [8], or Received Signal Strength (RSS) [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. When the sensors are mobile, Frequency Difference of Arrival (FDoA) can also be used as an additional source of information [24], [25], [26]. AoA, TDoA and FDoA approaches require sophisticated sensors, and, therefore do not fit well within the limitations of wireless sensor networks, specifically for energy and complexity constraints of the nodes.…”

mentioning

confidence: 99%

On the existence of an MVU estimator for target localization with censored, noise-free binary detectors

Shoari

Seyedi

2016

Physical Communication

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The problem of target localization with censored noise free binary detectors is considered. In this setting only the detecting sensors report their locations to the fusion center. It is proven that if the radius of detection is not known to the fusion center, a minimum variance unbiased (MVU) estimator does not exist. Also it is shown that when the radius is known the center of mass of the possible target region is the MVU estimator. In addition, a sub-optimum estimator is introduced whose performance is close to the MVU estimator but is preferred computationally. Furthermore, minimal sufficient statistics have been provided, both when the detection radius is known and when it is not. Simulations confirmed that the derived MVU estimator outperforms several heuristic location estimators.Our appreciation goes out to Mona Komeijani for preparing the illustrative graphs of this paper. Also we would like to thank Professor Mark Bocko and Professor Azadeh Vosoughi for their valuable feedback in finalizing this manuscript.A. Shoari is with the

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Constrained total least-squares localisation algorithm using time difference of arrival and frequency difference of arrival measurements with sensor location uncertainties

Cited by 38 publications

References 11 publications

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

BIAS analysis of an algebraic solution for TDOA localization with sensor location errors

On the existence of an MVU estimator for target localization with censored, noise-free binary detectors

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