An improved strong tracking Kalman filter algorithm for real-time vehicle tracking

Alaaudeen, K.M.; Aruna, T.; Ananthi, G.

doi:10.1016/j.matpr.2022.02.507

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…A bias compensation PLKF (BCPLKF) estimator is presented to tackle the bias issue of PLKF, and an instrumental variable-based Kalman filter (IVKF) is presented to alleviate the bias by utilizing BCPLKF estimates instead of noisy measurements to compute the measurement matrix, in which the posterior Cramér-Rao lower bound (PCRLB) is derived for the nonlinear filtering problem. An improved strong tracking Kalman filter algorithm for tracking analysis is proposed in [15], in which a location is identified with the GPS and basic GSM with message setting. This KF-based scheme is outstanding for its real-time behavior.…”

Section: Related Workmentioning

confidence: 99%

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An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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Section: Related Workmentioning

confidence: 99%

“…Classical tracking algorithms exist in most applications, such as Bayesian algorithm [10][11][12][13], Kalman filter [14,15] and CLS algorithm [16]. Most of target tracking used in WSNs can basically be classified into two categories: clustering and predicting [17,18].…”

mentioning

confidence: 99%

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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show abstract

“…A new pseudolinear Kalman filter (PLKF) for target tracking in 2D-plane is presented in [10], using [11], in which a location is identified with the GPS and basic GSM with message setting.…”

Section: Related Workmentioning

confidence: 99%

“…Classical tracking algorithms exist in most applications, such as Bayesian algorithm [6][7][8][9], Kalman filter [10][11], and CLS algorithm [12] etc. Most of target tracking used in WSNs can basically be classified into two categories: clustering and predicting [13][14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Zhu

Zhang

2022

Preprint

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (Measurement Compensation based Mixture Population Monte Carlo) method which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee the sample diversity. And also, extra measurements or observations generating method is introduced to compensate missing predict locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, the HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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Online analysis method to correlate the mode shape for forced vibration in milling thin-walled workpieces

Guo

Mao

Peng

et al. 2022

Int J Adv Manuf Technol

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An improved strong tracking Kalman filter algorithm for real-time vehicle tracking

Cited by 5 publications

References 5 publications

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Online analysis method to correlate the mode shape for forced vibration in milling thin-walled workpieces

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