Study on RSS/AOA hybrid localization in life detection in huge disaster situation

Shu, Xueming; Hu, Jun; Zhou, Ruihao; Shen, Shifei; Cao, Siyang

doi:10.1007/s11069-018-3503-9

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…Owing to their improved localization accuracy and reliability, the usage of hybrid measurements for source localization have been a research area of significant interest in past few years. Many target localization algorithms were developed using different combinations of measurements, e.g., RSS-TOA/TDOA [5]- [16], TOA-AOA [17]- [19], TDOA-AOA [20]- [23], RSS-AOA [24]- [36].…”

Section: A Related Workmentioning

confidence: 99%

Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

Panwar¹,

Babu²

2022

Preprint

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This paper investigates the hybrid source localization problem using the four radio measurements -time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS) and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then, in the MLE, which has the least-squares objective, weights determined using the range-based characteristics of the four heterogeneous measurements, are introduced. The resultant weighted leastsquares problem obtained, which is non-smooth and non-convex, is solved using the principle of the majorization-minimization (MM), leading to an iterative algorithm that has a guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the estimation efficiency of the proposed method. The proposed method employing all four measurements is compared against a conventionally used method and also against the proposed method employing only limited combinations of the four measurements. The results obtained indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements. The integration of different measurements also yields good accuracy in the presence of non-line of sight (NLOS) errors.Index Terms-Hybrid source localization, majorizationminimization (MM) algorithm, received signal strength (RSS), time of arrival (TOA), time-difference of arrival (TDOA), angle of arrival (AOA) and wireless sensor networks (WSNs).

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Section: A Related Workmentioning

confidence: 99%

Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

Panwar¹,

Babu²

2022

Preprint

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“…The authors of [27] proved that hybridization of different types of measurements (TOA/RSS) could enhance localization accuracy from Cramer-Rao lower bound (CRLB) analysis, but signal-to-noise ratio, path-loss exponent, as well as anchors placement all affected the hybrid TOA/RSS LLS localization accuracy in different ways. The authors of [28] applied a RSS/AOA hybrid positioning algorithm to increase the accuracy of localization when searching and rescuing the survivals in huge disaster.…”

Section: Related Workmentioning

confidence: 99%

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

Wang

Guan

Wan

et al. 2019

Sensors

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Target localization is one of the essential tasks in almost applications of wireless sensor networks. Some traditional compressed sensing (CS)-based target localization methods may achieve low-precision target localization because of using locally optimal sparse solutions. Solving global optimization for the sparse recovery problem remains a challenge in CS-based target localization. In this paper, we propose a novel energy-level jumping algorithm to address this problem, which achieves high-precision target localization by solving the globally optimal sparse solution of l p -norm ( 0 < p < 1 ) minimization. By repeating the process of energy-level jumping, our proposed algorithm establishes a global convergence path from an initial point to the global minimizer. Compared with existing CS-based target localization methods, the simulation results show that our localization algorithm obtain more accurate locations of targets with the significantly reduced number of measurements.

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“…Localization based on these two radio measurements is very important for future technologies, since RSS is practically available on all devices, while AOA is expected to be broadly available in the fifth generation of networks, due to the use of massive antenna arrays. Recently, measurement integration, such as AOA and RSS, has gained popularity [ 12 , 13 , 14 , 15 ], and will be the main focus of this work. Even though these hybrid techniques can enhance localization accuracy, they come at a certain cost, since additional equipment might be necessary in order to acquire the desired radio measurements [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…In [ 25 ], the authors addressed the problem of optimal sensor placement strategy for single static target localization using the hybrid RSS, AOA, and time of arrival measurements in two-dimensional sensor networks. The authors in [ 12 ] presented a novel survival scheme for life detection after huge disaster situations based on portable cell phone detection system in a two-dimensional space. They considered two simulation settings in which they showed that their scheme based on RSS-AOA measurements works well, as long as certain variables are calibrated correctly.…”

Section: Introductionmentioning

confidence: 99%

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An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks

Costa

Tomic

Beko

2021

Sensors

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This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.

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Study on RSS/AOA hybrid localization in life detection in huge disaster situation

Cited by 9 publications

References 19 publications

Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks

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