Adaptive Event Location Technique via Time Difference of Event Arrival

Kim, Do-In; White, Austin; Shin, Yong-June

doi:10.1109/tpwrs.2019.2927613

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The feasibility of Algorithm 1 can be easily shown. For instance, it selects the frequently alarmed sensors (line 2-22), and then estimates the source location using the selected sensor nodes (line [23][24][25][26][27][28][29][30]. The estimated location, in fact, is in the monitored area A because all the sensor nodes are located in the area.…”

Section: Analysis Of Algorithmmentioning

confidence: 99%

“…get reversed. Event localizations have many applications such as localizing intruder, pollutant sources like biological and chemical weapons, enemies positions in combat monitoring, and faults in power systems [1], [28]. We consider wireless sensor networks for source (event) localization, where sensor nodes monitor the source, and the data set is generated when sensor nodes report their decisions to the fusion center.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Source Localization without Sensor Parameters in Wireless Sensor Networks

Hussain¹,

Luo²

2020

Preprint

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In this paper, the source (event) localization problem is studied in decentralized wireless sensor networks under the fault model where the sensor nodes observe the source and report their decisions to the Fusion Center (FC) for estimating source location. Due to fault model, sensor nodes may provide false positive or false negative decisions to the FC. Event localizations have many applications such as localizing intruder, pollutant sources like biological and chemical weapons, enemies positions in combat monitoring, and faults in power systems. We propose two methods to estimate the source location under the fault model: hitting set approach and feature selection method, which utilize the noisy data set at the FC for estimation of the source location. We have shown that these methods are more fault tolerant in estimating the source location and are not complex as well. We also study the lower bound on the sample complexity requirement for hitting set method. These methods have also been extended for multiple sources localizations. Finally, extensive simulations are carried out for different parameters (i.e., the number of sensor nodes and sample complexity) to validate our proposed methods, which show that the proposed methods achieve better performances under the fault model.

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Section: Analysis Of Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized Source Localization without Sensor Parameters in Wireless Sensor Networks

Hussain¹,

Luo²

2020

Preprint

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“…In [26], the authors addressed a joint source location and propagation speed estimation using TDOA; the approach is necessary in scenarios, such as underwater acoustic localisation, where the signal velocity is unknown. TDOA can also be used in very different domains, such as the event localisation technique in power systems based on Phasor Measurement Unit (PMU) [27] or the Compressed Sensing (CS) [28].…”

Section: Introductionmentioning

confidence: 99%

Multi-Network Asynchronous TDOA Algorithm Test in a Simulated Maritime Scenario

Gioia

Sermi

Tarchi

2020

Sensors

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In the last few years, the number of applications relying on position of vessels at sea has grown significantly. Usually, these applications exploit information provided by the Automatic Identification System (AIS). Unfortunately, the cooperative nature of AIS makes it vulnerable to different types of attack. Therefore, especially for critical applications, the veracity of the position information reported in the AIS message needs to be verified. Several techniques can be adopted to this end. This paper presents a mathematical extension of the traditional Time Difference Of Arrival (TDOA) localisation technique allowing merging TDOA measurement from synchronous and non-synchronous receivers. This technique was tested in a simulated scenario, where the position of a moving target was estimated using different configurations of the receivers network. The robustness of the proposed algorithm with respect to the traditional one is demonstrated. The proposed approach, which is derived form satellite applications, is not limited to the AIS signals or to the maritime domain, and it can be adopted to estimate the position of any radiofrequency transmitter, by employing a suitable number of non-synchronous receivers.

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