Mobility Tracking Based on Autoregressive Models

Zaidi, Zainab; Mark, Brian L.

doi:10.1109/tmc.2010.130

Cited by 31 publications

(18 citation statements)

References 39 publications

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“…Otherwise, it is a normal time step. The AR model [18,19] is used for parameter prediction and for determining whether a time step is abnormal using prediction residual and threshold, and the Bayesian information criterion (BIC) and least-squares method are implemented for model parameter calibration.…”

Section: Temporal Event Analysis Based On Local Informationmentioning

confidence: 99%

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Xie

et al. 2017

Water

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Abstract:As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions.

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Section: Temporal Event Analysis Based On Local Informationmentioning

confidence: 99%

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Xie

et al. 2017

Water

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“…The measurements in this setting usually involve RSSI signals from known base stations [3], [4]. The signal model is usually some form of Gauss-Markov model.…”

Section: Related Workmentioning

confidence: 99%

“…Zaidi et al [4] study ad hoc networks with intermittent connectivity. The algorithm for mobility tracking developed in [4] uses RSSI measurements from neighboring nodes modeled as a linear system driven by a discrete semi-Markov process in combination with an efficient averaging filter and an EKF.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm for mobility tracking developed in [4] uses RSSI measurements from neighboring nodes modeled as a linear system driven by a discrete semi-Markov process in combination with an efficient averaging filter and an EKF. The proposed algorithm allows robust mobility tracking in ad hoc networks using RSSI measurements.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm is able to follow mobile trajectories accurately over a wide range of parameter values. The proposed mobility tracking algorithm can be applied in a variety of scenarios, such as adaptive clustering, routing, and mobility management in ad hoc networks [4].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Mobility estimation using an extended Kalman filter for unmanned ground vehicle networks

Thulasiraman

Clark

Beach

2014

2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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Abstract-An ad hoc unmanned ground vehicle (UGV) network operates as an intermittently connected mobile delay tolerant network (DTN). In this paper, we develop a mobility estimation algorithm that can be coupled with a cooperative communication routing algorithm to provide a basis for real time path planning in UGV-DTNs. A Gauss-Markov state space model is used for the node dynamics. The nonlinear measurement signals are constant-power RSSI (Received Signal Strength Indicator) signals transmitted from fixed-position base stations. An extended Kalman filter (EKF) is derived for estimating the position, velocity and acceleration of a UGV node in a twodimensional spatial grid environment. We use Matlab to simulate a single mobile node traveling along a trajectory that includes abrupt maneuvers. Estimation performance is measured using zero-mean whiteness tests on the innovations sequences, root mean square error (RSME) of the state estimates, weighted sum squared residuals (WSSRs), and the posterior Cramer-Rao lower bound (PCRLB). Under these performance indices, we demonstrate that the mobility estimation algorithm performs effectively.

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Brownian Bridge Model for High Resolution Location Predictions

Lin

Hsu

2014

Advances in Knowledge Discovery and Data Mining

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Mobility Tracking Based on Autoregressive Models

Cited by 31 publications

References 39 publications

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Mobility estimation using an extended Kalman filter for unmanned ground vehicle networks

Brownian Bridge Model for High Resolution Location Predictions

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