Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

Wang, Zengfeng; Zhang, Hao; Lu, Tingting; Gulliver, T. Aaron

doi:10.1109/tvt.2018.2880991

Cited by 97 publications

(75 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MSE is smaller than those in [6]- [8] due to the two-step approximations. For the scenario with MS in the cell of radius R = 2000 m, h i = 1000 m, and environmental data for urban area is a 0 = 45, a 1 = −1.5, b 0 = 10, b 1 = 3.5, the minimum standard deviation in (8) σ i,g i ≈ 30 σ RSS,i , is smaller than that in [6]- [8] (about 115 σ RSS,i ). Thus, (P3) is further converted to a semi-definite programming (SDP) problem:…”

Section: ) Transformation To a Linear Problem Via Taylor's Series Exmentioning

confidence: 85%

“…and perfectly known PLEs, as well as the approach in [19] with unknown and unequal PLEs. This is because the power-form ranging function via piecewise convex approximation and TSE has much lower variance than the exponential-form ranging functions in [6]- [19]. To the best of our knowledge, this is also the first work to investigate the impact of AS on UAV assisted and RSS based localization.…”

Section: B Main Contributionsmentioning

confidence: 94%

“…Unlike the TSE only based methods in [4]- [8], PCAL does not require the PLEs associated with different UAVs to be perfectly known and equal to each other, and therefore it is more practical. 2) Thanks to its robustness against shadowing, especially AS, the proposed PCAL approach with unknown and unequal PLEs achieves higher accuracy than the approaches [6]- [8] with equal 0018-9545 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.…”

Section: B Main Contributionsmentioning

confidence: 99%

“…Noncooperative and cooperative localization approaches with unknown transmit power and unknown path loss exponent (PLE) were studied in [5]. In [6], least-square absolute error of ranging was minimized for localization. In [7], both RSS and differential RSS based localization methods were proposed, with anchor coordinates uncertainties and imperfect knowledge of PLE.…”

Section: A Related Workmentioning

confidence: 99%

See 3 more Smart Citations

UAV and Piecewise Convex Approximation Assisted Localization With Unknown Path Loss Exponents

Liu

Zhu

Jiang

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

In this correspondence, we investigate unmanned aerial vehicle (UAV)-base stations (BSs) assisted and received signal strength (RSS) based mobile station (MS) localization. A practical air-to-ground path loss model is utilized, where the path loss exponent (PLE) varies with the elevation angle and altitude of UAV, and the accurate PLE estimate is often difficult to obtain. With unknown and unequal PLEs for different UAVs, the UAVs assisted localization problem becomes nonlinear and non-convex, which cannot be solved by the existing methods. We propose a piecewise convex approximation aided localization (PCAL) approach to convert the localization problem into linear and convex, without requiring the knowledge of PLE. The proposed PCAL approach with unknown and unequal PLEs achieves much higher accuracy than the existing methods which require perfectly known and equal PLE, due to its higher robustness against shadowing. In addition, a grid search aided ambiguity elimination (GSAE) method, which is more effective than the state-of-the-art methods, is proposed to determine the final MS localization estimate based on multiple tentative estimates derived from PCAL. The effectiveness of PCAL is also verified by the Cramer-Rao lower bound (CRLB) derived.

show abstract

Section: ) Transformation To a Linear Problem Via Taylor's Series Exmentioning

confidence: 85%

Section: B Main Contributionsmentioning

confidence: 94%

Section: B Main Contributionsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 2 more Smart Citations

UAV and Piecewise Convex Approximation Assisted Localization With Unknown Path Loss Exponents

Liu

Zhu

Jiang

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…The target node positioning algorithm can be built in several ways. A triangle centroid positioning algorithm based on the distance or relative angle information between the target node and the anchor node has been proposed in the document [17][18][19]. There are problems of the target node deviating effective locating area and the large positioning error in the positioning algorithm because the node distribution characteristics are not entirely considered.…”

Section: The Target Node Positioning Algorithmmentioning

confidence: 99%

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Chen¹,

Wang²,

Ouyang³

et al. 2019

Preprint

View full text Add to dashboard Cite

The node position information is critical in the wireless sensor network (WSN). However, the existing positioning algorithms commonly have low positioning accuracy because of noise interferences in communication. To solve this problem, this paper presents an iterative positioning model based on distance correction to improve the positioning accuracy of the target node in WSN. First, the log-distance distribution model of received signal strength indication (RSSI) ranging is built and the noise impact factor is derived based on the model. Second, the initial position coordinates of the target node are obtained based on the triangle centroid localization algorithm, thereby calculating the distance deviation coefficient under the influence of noise. Then, the ratio of the distance measured by the log-normal distribution model to the median distance deviation coefficient is taken as the new distance between the anchor node and the target node. Based on the new distance, the triangular centroid positioning algorithm is used again to calculate the target node coordinates. Finally, the iterative positioning model is constructed, and the distance deviation coefficient is updated repeatedly to update the positioning result until the set number of iterations is reached. Experiment results show that the proposed iterative positioning model can improve positioning accuracy effectively.

show abstract

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

LiuTong

Zhaohui

YangHaojun

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

To accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. Firstly, the adaptive discrete particle swarm optimization (ADPSO) algorithm is used to find the global optimal solution to the localization problem. A grid quantization method is proposed to discretize the value space of the traditional PSO algorithm to reduce the estimated localization time. An adaptive factor is also introduced to adjust the value of power for each iteration in the discrete particle swarm optimization (DPSO) algorithm in order to find the global optimal solution efficiently. Secondly, the fixed transmit power range is adopted to calibrate the estimated value of transmit power and the corresponding location. The prototype system comprises four anchor nodes based on a universal software radio peripheral radio frequency (USRP RF). Finally, field experiments are carried out to demonstrate the effectiveness of the proposed localization scheme.

show abstract

Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

Cited by 97 publications

References 47 publications

UAV and Piecewise Convex Approximation Assisted Localization With Unknown Path Loss Exponents

UAV and Piecewise Convex Approximation Assisted Localization With Unknown Path Loss Exponents

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

Contact Info

Product

Resources

About