Noise tolerance analysis of marginalized particle filter for target tracking

Jayamohan, Sanil; Mathurakani, M.

doi:10.1109/aicera-icmicr.2013.6576017

Cited by 2 publications

(1 citation statement)

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“…In addition to measurements, tracking algorithms can employ a state-space model to refine the position estimation based on its previous position. For instance, a first-order model is used with a Kalman filter in [15], and with a particle filter in [11], whereas [16] employs a second-order one. However, these models are only reliable for targets having slightly varying velocities or accelerations.…”

Section: Introductionmentioning

confidence: 99%

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

et al. 2014

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This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The target's accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process.The Kalman filter is used afterwards to combine predictions of the target's positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison to well-known algorithms is also provided.

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Section: Introductionmentioning

confidence: 99%

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

et al. 2014

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A Novel Adaptive Marginalized Particle Filter for Mixed Linear / Nonlinear State-Space Models

Jayamohan

Santhi

Ramar

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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The speed with which the state parameters can be estimated with acceptable variance is an important issue in any target tracking application. In most of these problems, a linear Gaussian substructure is present in the model which is made use of and estimation is usually done using Marginalized Particle Filter (MPF) than the most general Particle Filter (PF). An important parameter that affects the performance of such filters is the amount of noise present in the measurement which may not remain constant, varying between a low value and a high value. The main aim of this paper is to introduce a new filter known as Adaptive Marginalized Particle Filter (AMPF) which exploits this property of noise thereby adapting the number of particles whenever possible and hence improving the estimation speed. The proposed filter is able to estimate the state parameters much faster than the existing filters. The performance of AMPF is compared with that of MPF and PF using a typical target tracking example. From the simulation, it can be concluded that AMPF is better than MPF and PF in terms of execution time, without much affecting its performance in terms of RMSE.

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Noise tolerance analysis of marginalized particle filter for target tracking

Cited by 2 publications

References 9 publications

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

A Novel Adaptive Marginalized Particle Filter for Mixed Linear / Nonlinear State-Space Models

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