Predicting future locations with hidden Markov models

Mathew, Wesley; Raposo, Ruben; Martins, Bruno

doi:10.1145/2370216.2370421

Cited by 317 publications

(167 citation statements)

References 24 publications

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“…Besides the above methods, Kalman filter [7], restrained non-linear optimal method [19] and Markov based pattern recognition [20] can also be used to location smoothing. However, these methods use the accurate values of arrival time, difference of arrival time and signal strength, and in the non line of sight environment, these values can't be measured directly, so they are also not suitable to the problem of non line of sight.…”

Section: Paper Game Theory Based Location Smoothing In Wireless Sensomentioning

confidence: 99%

Game Theory Based Location Smoothing in Wireless Sensor Networks

Yongqiang

Qin²,

Wang³

2016

Int. J. Onl. Eng.

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Abstract-Location estimation of mobile target is an important research in wireless sensor networks. However, the phenomenon of non line of sight affects the accuracy of location estimating algorithms greatly. In order to improve the accuracy of location estimation of mobile targets, we proposed a game theory based location method algorithm in wireless sensor networks. In the proposed method, we first estimate the initial locations of mobile target with a support vector regression model, and then use game theory to smooth the already estimated locations. In the game model, game based filter and noise generator are two game rivals. The game filter aims to minimize the objective function of the game model, and the noise generator aims to maximize the same objective function. We propose a differential game theory based algorithm for solving the optimal resolution. The experiments show that, the proposed method is more powerful, and can be used to mobile target locating and tracking.

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Section: Paper Game Theory Based Location Smoothing In Wireless Sensomentioning

confidence: 99%

Game Theory Based Location Smoothing in Wireless Sensor Networks

Yongqiang

Qin²,

Wang³

2016

Int. J. Onl. Eng.

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“…One of these methods is the Kalman filter solution, which is represented as a random process. The Kalman filter is expressed using stochastic models [16]; such models include the first or higher order Gauss-Markov model [17] and auto-regressive models [18]. These models provide only an approximate description of movement behavior.…”

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confidence: 99%

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Kaloop

Sayed

2015

IJGI

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This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS) technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS) time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1)the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2) the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and 2340(3) The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

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“…For instance, Order-k Markov model was used in [198] to predict the movement of users in Wi-Fi network cells. In [180], a model based on hidden Markov models is proposed for modeling movements from one stay-point to another, while authors of [199] used mixed Markov model for the same purpose. A mixed autoregressive hidden Markov model is proposed in [179] on stay-points.…”

Section: Probabilistic Trajectory Modelingmentioning

confidence: 99%

“…Firstly, trajectories are formed by components with different speeds (stay-points and transitions) being repeated with different frequencies. A model, which only captures frequency of visit to places, turns out to be biased to stay-points [179,180]. On the other hand, preprocessing trajectories to take out segments with similar speed is time and energy consuming.…”

Section: Introductionmentioning

confidence: 99%

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Lattitude, Longitude, and Beyond : Mining Mobile Object's Behavior

Baratchi¹

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Predicting future locations with hidden Markov models

Cited by 317 publications

References 24 publications

Game Theory Based Location Smoothing in Wireless Sensor Networks

Game Theory Based Location Smoothing in Wireless Sensor Networks

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Lattitude, Longitude, and Beyond : Mining Mobile Object's Behavior

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