A Novel ToA Location Algorithm Using LoS Range Estimation for NLoS Environments

Venkatraman, S.; Caffery, J.; You, Hojun

doi:10.1109/tvt.2004.832384

Cited by 240 publications

(169 citation statements)

References 23 publications

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“…The RBPF An RBPF can be used when the state space model is linear Gaussian or almost linear Gaussian assuming a part of the state vector is known. For the problem at hand, the posterior distribution of interest can be factorized according to Bayes' rule (19) Note that all the information about and is present in the posterior (19). According to this expression, the distribution can be estimated by an EKF or a UKF, the latter being the strategy favored in this paper.…”

Section: Estimation Methodsmentioning

confidence: 99%

“…The detection of NLOS using statistical information (in order to discard biased measures) was studied in [18]. This statistical information was combined with geometrical information in [19]. The ANs closer to the MT are considered more likely to be in LOS.…”

Section: B Statistical Analysismentioning

confidence: 99%

“…The algorithm proposed in [8] cannot be applied since it assumes constant bias. The idea developed in [19] has been discarded because it is based on geometrical information which has not been considered in the simulation tests. Finally, the algorithm proposed in [28] has not been considered since it is based on statistical information obtained online from a set of measurements.…”

Section: B Six Ans With Rtoa Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

Espı́

Vidal

Giremus

et al. 2009

IEEE J. Sel. Top. Signal Process.

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Section: Estimation Methodsmentioning

confidence: 99%

Section: B Statistical Analysismentioning

confidence: 99%

Section: B Six Ans With Rtoa Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

Espı́

Vidal

Giremus

et al. 2009

IEEE J. Sel. Top. Signal Process.

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show abstract

“…This approach is effective when a small number of the distributed sensors are NLOS, but become computationally intensive when there are many distributed sensors and may not be possible in situations where the signal source is moving. Because NLOS-caused errors are always positive, this constraint can be imposed on the search for a position estimate (Wang et al, 2003;Venkatraman et al, 2004;and Cong and Zhuang, 2005), but normally, prior knowledge of NLOS error statistics is required to correct a LOS estimate. The procedure for estimating a source location when some distributed sensors are NLOS is more complicated using TDOAs because the NLOS error for a reference sensor will be transferred to all TDOAs computed using that sensor.…”

Section: B Algorithms Dealing With Specific Scenariosmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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“…This causes the signal to arrive the receiver from a different angle than the direct path between blind nodes and anchor nodes, and for ranging measurements (or equivalently, time of arrival), it will add a large positive error in addition to standard measurement error [9]- [10]. Although many location methods such as NLOS identification algorithm [11], inequality constraint [12] and scatter information [13] were addressed to suppress NLOS errors, their performance improvement is not significant since all of these methods don't consider the prior information on sample points. Based on the prior information of sample points, several learning location methods have been proposed to estimate the position of blind nodes [14]- [16] and obtain the higher positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

LS-SVM Based WSN Location Algorithm in NLOS Environments

Zhang¹,

Zheng²,

Wang³

et al. 2017

dtcse

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Location methods based on learning theory perform well in wireless cellular networks. These methods may be further improved since range measurements among all nodes are not taken into consideration, while these range measurements in the WSN location system are generally available. In this paper, we propose an improved LS-SVM based location algorithm to solve mobile location problem in a NLOS environment. We extend LS-SVM method from wireless cellular networks to WSN location system. Compared with LS-SVM in wireless cellular networks only using the range measurements between anchor nodes and blind nodes, the proposed method can improve the positioning accuracy by using all the range measurements among the nodes. Moreover, steepest descent method is used in the proposed method to iterative search the optimal position estimation of blind nodes. The simulations results in different cases illustrate that the proposed algorithm outperforms the kernel method and LS-SVM method on location accuracy.

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A Novel ToA Location Algorithm Using LoS Range Estimation for NLoS Environments

Cited by 240 publications

References 23 publications

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

LS-SVM Based WSN Location Algorithm in NLOS Environments

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