The distributed Gauss-Newton methods for solving the inverse of approximated Hessian with application to target localization

Wu, Mou; Zhong, Liangji; Xiong, Naixue

doi:10.1145/3403746.3403900

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…We reconstruct the lightning channel geometry of clouds and ground flashes by locating the temporal and spatial changes of the lightning source [17] and use the thunder channel reconstruction model of three, four, and five microphone systems to calculate the error accuracy. Wu, et al [18] used a wireless sensor network with random topology, which is used to solve the problem of target location. Through special decomposition technology to exchange local information with neighboring anchor nodes, the centralized Gauss-Newton method is modified into a distributed solution, which has the ability to resist environmental noise while ensuring convergence.…”

Section: Related Workmentioning

confidence: 99%

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices

Wang

Qin

et al. 2023

Sensors

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Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.

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Section: Related Workmentioning

confidence: 99%

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices

Wang

Qin

et al. 2023

Sensors

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“…The rank-one relaxation may lead to produce a suboptimal solution that is not the optimal solution of the original optimization problem. To obtain the rank-one solution of the convex SDP problem, many mathematical methods are proposed to deal with the troublesome problem [45]- [48]. To solve the low-rank SDP problems, the factorization method is introduced by obtaining a reformulation of the original SDP problem in [49].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements

Qi,

Wu,

Xiong

2020

Preprint

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In this paper, we introduce an intelligent prediction system for mobile source localization in industrial Internet of things. The position and velocity of mobile source are jointly predicted by using Time Delay (TD) measurements in the intelligent system. To predict the position and velocity, the Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rankone constraint leads to produce a suboptimal solution. To improve the performance, we further put forward a Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rankone solution of the optimization problem by introducing the penalty terms. Then an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty by adaptively choosing the penalty coefficient. We conduct experiments in both a simulation environment and a real system to demonstrate the effectiveness of the proposed method. The results have demonstrated that the proposed intelligent APF-SDP algorithm outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.

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A Source Prediction System for Dynamic Networks Based on TDOA Measurements

Xiong

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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The distributed Gauss-Newton methods for solving the inverse of approximated Hessian with application to target localization

Cited by 3 publications

References 22 publications

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices

An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements

A Source Prediction System for Dynamic Networks Based on TDOA Measurements

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