A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Xu, Jing; He, Jingsha; Zhang, Yuqiang; Xu, Fei; Cai, Fangbo

doi:10.1155/2016/2080536

Cited by 33 publications

(19 citation statements)

References 18 publications

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“…In the location algorithms using ranging, the node's distance or angle is utilized to estimate the position of the target node. The WSN localization algorithms based on ranging mainly include trilateration [23], triangulation [24], and maximum likelihood estimation [25]. Trilateration uses the distances between one target node and three anchor nodes to locate the target node, while triangulation first converts the angles between three anchor nodes and one target node into the corresponding distances, then estimates the target node's location through trilateration.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

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Section: Related Workmentioning

confidence: 99%

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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“…ML estimate of n u s can be obtained by maximizing the conditional probability distribution function ( , , / s ) n u f    , the problem translates into the following equation [12]. The problem is reduced to a weighted least squared problem after linearization.…”

Section: ) Likelihood Function Of Multi-slots Joint Mlementioning

confidence: 99%

“…A centralized maximum likelihood estimation is proposed in [10], [11] to solve the two-dimensional cooperative location estimate problem in wireless sensor network (WNS) with TOA and velocity measurements. In [12], an improved linear optimization process based on first-order optimal condition of MLE is proposed to estimate two-dimension positions of sensor network agents to improve the efficiency of search. In order to ensure altitude accuracy of the aircraft with low-cost GNSS and radar, in [13] different altimeter sensors are fused to constrain the error of altitude direction.…”

Section: Introductionmentioning

confidence: 99%

Multi-Slots Joint MLE Relative Navigation Algorithm Based on INS/JTIDS/BA for Datalink Network

Cao

Qin

et al. 2020

IEEE Access

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High accuracy relative navigation (RELNAV) information is crucial for datalink network members in the situation of joint operations, especially the situations that some datalink network members may not be able to obtain their location with absolute navigation. In this paper, relative navigation algorithm based on JTIDS/INS/BA is proposed, in this system datalink joint units(JUs) are positioned in two-dimensional plane with multi-slots joint maximum likelihood estimation (MLE), and altitude is located by a height filter independently, which overcome the problem of poor positioning geometry of altitude. Multi-slots joint MLE convert TOA measurements in different slots to one moment with short-time inertial navigation system(INS) information , which lead to a higher localization accuracy in two-dimensional plane. We simulate a joint tactical information distribution system (JTIDS) network with multiple members, to verify the performance and analyze the influence factors of the proposed algorithm. The simulation results show that compared to EKF that process TOA measurements sequentially in each navigation slot and MLE without multi-slots joint processing, the multi-slots joint MLE relative navigation algorithm based on INS/JTIDS/BA performs better in two-dimensional plane location, and the height filter based on barometric altimeter (BA) improves the relative height positioning accuracy greatly.

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“…. ., is the related constraints of ( , ), under assumptions of whether the distributions of the measurement noise are known or not and the distributions are normal distributions or not, there are many estimation methods that can be available, such as Maximum Likelihood estimation [21], Bayesian estimation [22,23], Extended Kalman filter [24], and Least Squares. The commonly used one is the Least Squares as follows:…”

Section: (B) the Localization Algorithms Based On Diffusion Modelsmentioning

confidence: 99%

A Study on Water Pollution Source Localization in Sensor Networks

Yang

Luo

2016

Journal of Sensors

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The water pollution source localization is of great significance to water environment protection. In this paper, a study on water pollution source localization is presented. Firstly, the source detection is discussed. Then, the coarse localization methods and the localization methods based on diffusion models are introduced and analyzed, respectively. In addition, the localization method based on the contour is proposed. The detection and localization methods are compared in experiments finally. The results show that the detection method using hypotheses testing is more stable. The performance of the coarse localization algorithm depends on the nodes density. The localization based on the diffusion model can yield precise localization results; however, the results are not stable. The localization method based on the contour is better than the other two localization methods when the concentration contours are axisymmetric. Thus, in the water pollution source localization, the detection using hypotheses testing is more preferable in the source detection step. If concentration contours are axisymmetric, the localization method based on the contour is the first option. And, in case the nodes are dense and there is no explicit diffusion model, the coarse localization algorithm can be used, or else the localization based on diffusion models is a good choice.

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A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Cited by 33 publications

References 18 publications

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Multi-Slots Joint MLE Relative Navigation Algorithm Based on INS/JTIDS/BA for Datalink Network

A Study on Water Pollution Source Localization in Sensor Networks

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