Recurrent Algorithm for TDOA Localization in Sensor Networks

Tovkach, Igor O.; Zhuk, S. Ya.

doi:10.5028/jatm.v9i4.727

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…into the state vector of the estimated parameters (Tovkach and Zhuk 2017b), which can be represented in the form u T (k) = (u trT (k), υ 0 (k)). In this case, the model of the process to be filtered is transformed to Eq.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The presence of correlated errors makes it difficult to use traditional recurrent target coordinates evaluation algorithms. This difficulty can be avoided through the introduction of error υ 0 (k) into the state vector of the estimated parameters (Tovkach and Zhuk 2017b), which can be represented in the form u T (k) = (u trT (k), υ 0 (k)). In this case, the model of the process to be filtered is transformed to Eq.…”

Section:  mentioning

confidence: 99%

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Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

Tovkach

Zhuk

2019

J.Aerosp. Technol. Manag.

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In modern conditions unmanned aerial vehicles (UAVs) generate new classes of threats, including their use for terrorist purposes. A feature of modern UAVs is the ability to perform sudden maneuvers and to keep the same position in the point in space. For the description of the UAV movement with various types of maneuver it is used a rectangular coordinate system. We use the model in the form of stochastic dynamic system with random structure in the discrete time in which the change type UAV movement occurs at random times. When a UAV emits a sign, its location can be determined by wireless sensor networks (WSN) using the TDOA method. On the basis of a mathematical apparatus of the mixed Markov processes for in discrete time optimal and quasi-optimal adaptive algorithms for filtration of UAV movement parameters based on the TDOA-measurement, sensor networks are synthesized. Devices that realize these algorithms are multichannel and belong to the class of devices with feedback between channels. At the same time, in a quasi-optimal algorithm, a sequential procedure of the arriving measurements from sensors of a sensor network is realized, which allows to avoid the inversion of large-dimensional matrices. An analysis of the quasi-optimal adaptive algorithm is performed using statistical modeling. On the intervals of hovering and of the UAV movements without maneuver, the developed algorithm allows to increase significantly the accuracy of the estimation of the UAV coordinates, and also to recognize various types of its movement with high probability level.

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Section: Problem Definitionmentioning

confidence: 99%

Section:  mentioning

confidence: 99%

Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

Tovkach

Zhuk

2019

J.Aerosp. Technol. Manag.

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“…Recently, based on extended Kalman Filter, Tovkach et.al. [16] developed an recurrent algorithm to localize the source.…”

Section: Introductionmentioning

confidence: 99%

SOLVIT:A Reference-Free Source Localization Technique using Majorization Minimization

Jyothi

Babu

2019

Preprint

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We consider the problem of localizing the source using range and range-difference measurements. Both the problems are known to be non-convex and non-smooth, which makes it challenging to solve. In the case of range-difference measurements, most of the current positioning techniques choose a reference sensor, however the source positioning accuracy has been reported to be affected by this approach. In this paper, we localize the source from range-difference measurements without choosing a reference sensor. We develop an iterative algorithm to solve the range-difference problem using Majorization Minimization (MM) approach-in which we employ a novel upper bound and minimize it to get a closed form solution at every iteration. The proposed algorithm is referred to as SOLVIT: Source Localization Via an Iterative technique. We also solve the source localization problem based on range measurements and rederive the Standard Fixed Point (SFP) algorithm developed in [1] using the MM approach. By doing so, we show a less intricate way to prove the convergence of the SFP algorithm. Next, we show a computationally efficient way to implement SOLVIT. Various simulations and experiments were conducted to compare SOLVIT with the existing methods. Experiments were conducted in an anechoic chamber to localize an acoustic source. Simulations and experiments confirm that SOLVIT performs better than existing methods in terms of source positioning accuracy. Also, it was found that when compared to the existing methods, SOLVIT does not depend on the positioning of source and sensors to localize a source.

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“…A large variety of range-based localization methods have been proposed with respect to a set of sensors with known positions [3,4]. Several range-based geolocation techniques have been used to estimate the target position including the time-of-arrival (TOA) [5][6][7], the time-difference-of-arrival (TDOA) [8,9], the angle-of-arrival (AOA) [10][11][12], the received signal strength (RSS) [13][14][15][16] based techniques, or hybrid location techniques [17,18]. Among these location techniques, TOA and AOA have attracted much attention because of their high positioning accuracy [16,18].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Estimator for Target Localization in a Multistation Redundancy System without Matrix Inversion

et al. 2018

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This paper focuses on target localization problem in a multistation redundancy system which finds broad applications in sonar, radar, and location-based service. Previous solutions can only be applied to the minimum system, such as the TOA method with three sensors and the AOA method with two sensors or need matrix inversion. To solve this problem, we propose a simple closed-form solution for a multistation redundancy localization system by using the estimation variance as the weighting coefficient to compute an average of each group's localization result. The proposed method, with simple algebraic solution, requires no matrix inversion and can be used for low-cost hardware devices. We derive the method in TOA solution and AOA solution, respectively. The proposed method can also be extended to other locating technologies. Numerical examples are provided to illustrate the performance of the proposed method in root-mean-square error. The positioning accuracy of the proposed method is close to Cramér-Rao low bound both in TOA solution and AOA solution.

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Recurrent Algorithm for TDOA Localization in Sensor Networks

Cited by 11 publications

References 2 publications

Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

SOLVIT:A Reference-Free Source Localization Technique using Majorization Minimization

An Efficient Estimator for Target Localization in a Multistation Redundancy System without Matrix Inversion

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