PSO-PF target tracking in range-based Wireless Sensor Networks with distance-dependent measurement noise

Keshavarz-Mohammadiyan, Atiyeh; Khaloozadeh, Hamid

doi:10.1109/iraniancee.2015.7146341

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…The particle swarm optimization (PSO) proposed in [12] measures the distance between nodes, and the measurement error was contained in the observation function of the motion noise. As a result, the positions of objects were determined by a weighted aggregation and by maximizing the PSO.…”

Section: Related Workmentioning

confidence: 99%

“…Analogously, range-free positioning algorithms that rely on the connectivity between nodes are also difficult to use in a large-scale complex environment [5]- [7] because a high node density and a large communication overhead are required. Typical examples of range-free algorithms include Amorphous [8], Distance Vector-Hop(DV-Hop) [9], Multi Dimentional Scaling Map(MDS-MAP) [10], Distance Vector-short(DV-short) [11] and Approximate Point in Triangulation Test(APTT) [12].…”

Section: Introductionmentioning

confidence: 99%

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Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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“…Now we describe the observation model of all sensors in the following multivariate observation model (9) where, , , and .…”

Section: Modification Of Multiplicative Noisementioning

confidence: 99%

“…In this case, the computational effort to update these particles is wasted. Some authors have suggested using intelligent algorithms to solve this problem [9]- [11]. They have suggested using an intelligent algorithm in order to move samples towards regions of state space with higher likelihood.…”

Section: Introductionmentioning

confidence: 99%

Logarithm based adaptive Particle Filter for maneuvering target tracking in Wireless Sensor Networks with multiplicative noise

Keshavarz-Mohammadiyan

Khaloozadeh

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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Problem of maneuvering target tracking in a Wireless Sensor Network (WSN) with multiplicative noise is considered in this paper. To solve the problem of state dependent measurement noise of sensors, the multiplicative measurement model is adopted. Using natural logarithm, the observation model is turned into an equation with additive noise. The Probability Density Function (PDF) of this additive measurement noise is then obtained to construct the likelihood function and to weight the samples in Particle Filter (PF). To track the target with unknown maneuvers, Input Estimation (IE) technique is used. Moreover, the state transition prior PDF with adjusted covariance matrix is proposed as the importance density function to improve the estimation of target trajectory. Effectiveness of the proposed tracking approach is validated and compared with results of generic PF and Unscented Particle Filter (UPF) through Monte-Carlo simulations.

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“…PF uses a set of samples (particles) and a sequence of observations to estimate the posterior Probability Distribution Function (PDF) of system state variables recursively. Many researchers have proposed to apply PF or its modifications in target tracking problems [5]- [7]. However, despite wide application of PF, it suffers from some drawbacks such as degeneracy and sample impoverishment problem [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive IMMPF for bearing-only maneuvering target tracking in Wireless Sensor networks

Keshavarz-Mohammadiyan

Khaloozadeh

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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In this paper, the problem of maneuvering target tracking in a bearing-only Wireless Sensor network (WSN) is considered. To estimate the state variables of the moving target with nonlinear dynamics, Particle Filter (PF) is applied. Moreover, Interacting Multiple Model (IMM) algorithm is used to cope with target maneuvers. To reduce the computational cost of Interacting Multiple Model Particle Filter (IMMPF) algorithm, a novel adaptive sample set size is proposed based on the output estimation error. Besides, a new resampling method is suggested to deal with varying number of samples. The estimation results of the proposed adaptive IMMPF are compared with that of IMMPF with fixed number of samples, in terms of accuracy and computation time.

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PSO-PF target tracking in range-based Wireless Sensor Networks with distance-dependent measurement noise

Cited by 11 publications

References 14 publications

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Logarithm based adaptive Particle Filter for maneuvering target tracking in Wireless Sensor Networks with multiplicative noise

Adaptive IMMPF for bearing-only maneuvering target tracking in Wireless Sensor networks

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