A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics

Lim, Jaechan

doi:10.5370/jeet.2013.8.6.1520

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…The movement of the target is mainly driven by the state noise that is also the random acceleration of the target. The state and measurement variables are denoted by ψ and m, respectively, and the dynamic state function is described referring to Reference [36][37][38] as follows:…”

Section: Dynamic State Modelmentioning

confidence: 99%

Tracking by Risky Particle Filtering over Sensor Networks

Lim

Park

2020

Sensors

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The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.

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Section: Dynamic State Modelmentioning

confidence: 99%

Tracking by Risky Particle Filtering over Sensor Networks

Lim

Park

2020

Sensors

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“…The moving direction of the target is subject to the acceleration that is determined by the process noise in the state equation. We denote the state and measurement by θ and z , respectively, and the state equation is expressed as follows:

\underset{{bold-italicθ}_{k}}{\underset{⏟}{([], \begin{matrix} center \\ array r_{x, k} \\ array r_{y, k} \\ array v_{x, k} \\ array v_{y, k} \end{matrix})}} = \underset{{bold-italicA}_{1}}{\underset{⏟}{([], \begin{matrix} center center center center \\ array 1 & array 0 & array T & array 0 \\ array 0 & array 1 & array 0 & array T \\ array 0 & array 0 & array 1 & array 0 \\ array 0 & array 0 & array 0 & array 1 \end{matrix})}} \underset{{bold-italicθ}_{k - 1}}{\underset{⏟}{([], \begin{matrix} center \\ array r_{x, k - 1} \\ array r_{y, k - 1} \\ array v_{x, k - 1} \\ array v_{y, k - 1} \end{matrix})}} + {bold-italicA}_{2} {bold-italicu}_{k},

where

{bold-italicA}_{2} = ([], \begin{matrix} center center \\ array \frac{T^{2}}{2} & array 0 \\ array 0 & array \frac{T^{2}}{2} \\ array T & array 0 \\ array 0 & array T \end{matrix}), {bold-italicu}_{k} = ([], \begin{matrix} center \\ array u_{x, k} \\ array u_{y, k} \end{matrix}) .

r , v , u , and ( x , y ) denote the location, velocity, acceleration, and coordinates, respectively. T is the sampling peri...…”

Section: Problem Formulationmentioning

confidence: 99%

“…The moving direction of the target is subject to the acceleration that is determined by the process noise in the state equation. We denote the state and measurement by and z, respectively, and the state equation is expressed as follows [17][18][19] : where…”

Section: Problem Formulationmentioning

confidence: 99%

Minimax particle filtering for tracking a highly maneuvering target

Lim

Kim

Park

2019

Intl J Robust & Nonlinear

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Summary In this paper, we propose a new framework of particle filtering that adopts the minimax strategy. In the approach, we minimize a maximized risk, and the process of the risk maximization is reflected when computing the weights of particles. This scheme results in the significantly reduced variance of the weights of particles that enables the robustness against the degeneracy problem, and we can obtain improved quality of particles. The proposed approach is robust against environmentally adverse scenarios, particularly when the state of a target is highly maneuvering. Furthermore, we can reduce the computational complexity by avoiding the computation of a complex joint probability density function. We investigate the new method by comparing its performance to that of standard particle filtering and verify its effectiveness through experiments. The employed strategy can be adopted for any other variants of particle filtering to enhance tracking performance.

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“…1, we showed a realization of tracking the CFO by PF approaches by employing only ten particles in addition to EKF tracking. Except for cost-reference PF (CRPF) [10], all PF approaches seem to show PI problem (at a certain point, the estimated value maintains the same value till the end of the tracking, i.e. a straight line) including regularized PF.…”

Section: Computer Simulationsmentioning

confidence: 99%

Performance Degradation Due to Particle Impoverishment in Particle Filtering

Lim¹

2014

Journal of Electrical Engineering and Technology

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-Particle filtering (PF) has shown its outperforming results compared to that of classical Kalman filtering (KF), particularly for highly nonlinear problems. However, PF may not be universally superior to the extended KF (EKF) although the case (i.e. an example that the EKF outperforms PF) is seldom reported in the literature. Particularly, PF approaches show degraded performance for problems where the state noise is very small or zero. This is because particles become identical within a few iterations, which is so called particle impoverishment (PI) phenomenon; consequently, no matter how many particles are employed, we do not have particle diversity regardless of if the impoverished particle is close to the true state value or not. In this paper, we investigate this PI phenomenon, and show an example problem where a classical KF approach outperforms PF approaches in terms of mean squared error (MSE) criterion. Furthermore, we compare the processing speed of the EKF and PF approaches, and show the better speed performance of classical EKF approaches. Therefore, PF approaches may not be always better option than the classical EKF for nonlinear problems. Specifically, we show the outperforming result of unscented Kalman filter compared to that of PF approaches (which are shown in Fig. 7(c) for processing speed performance, and Fig. 6 for MSE performance in the paper).

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A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics

Cited by 6 publications

References 10 publications

Tracking by Risky Particle Filtering over Sensor Networks

Tracking by Risky Particle Filtering over Sensor Networks

Minimax particle filtering for tracking a highly maneuvering target

Performance Degradation Due to Particle Impoverishment in Particle Filtering

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