UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data

Uribe-Murcia, Karen; Shmaliy, Yuriy S.; Andrade-Lucio, José A.

doi:10.1155/2018/7456010

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…It should be noted that for time-stamped delays and missing data, the UFIR, Kalman, and game theory H ∞ filters were developed in [36] and [37], and for one-step Bernoullidistributed randomly delayed and missing data in [38]. However, no FIR solution has yet been addressed for tracking under multi-step random delays and multiple dropouts that motivate our present work.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

et al. 2023

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The unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory H ∞ filter are developed for vehicle tracking in wireless sensor networks (WSNs) with multi-step random delays and multiple dropouts. The problem with data delays is solved for given delay probabilities by converting a model with delays to another without delays. The dropouts are detected at the receiver by a data sensor, and lost data are compensated for by prediction. The filters developed are experimentally tested for accuracy and robustness using the Global Positioning System-based vehicle tracking database, where the measured coordinates are transmitted over WSN with delays. It is shown that the UFIR filter is the most robust to uncertain delays and the KF is the most accurate when the delay probabilities are known exactly. The game theory H ∞ filter is the most accurate under ideal conditions and prone to divergency otherwise.INDEX TERMS WSN, multi-step delay, multiple packet dropouts, UFIR filter, Kalman filter, game theory H ∞ filter.

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Section: Introductionmentioning

confidence: 99%

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

et al. 2023

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“…To note, UFIR is the most robust among the FIR variants as stated in (61,64,65). UFIR filters have been effectively applied in numerous engineering applications, including applications in global positioning systems (GPS)-based vehicle tracking over a wireless sensor network (WSN) (66), an electrocardiogram (ECG) data for features extraction (67,68), and state estimation of carbon monoxide concentration (69)(70)(71).…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

Rahman

Aziz

Ibrahim

et al. 2020

Preprint

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This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.

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“…In [27] an extended KF was modified to address the issue of missing measurement while in [28] a UFIR filter was developed as a robust estimator that neglects noise statistic. Regarding WSNs, in [29] a KF was modeled for intermittent observations and in [30] a UFIR alternative for missing and delayed data was developed. In this work, we further develop the results obtained in [19] by providing a mathematical expression for the consensus factor and also include a prediction step to overcome the issue of missing data.…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

2019

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Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.

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UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data

Cited by 13 publications

References 37 publications

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

Moving Vehicle Tracking under Measurement Uncertainties, Multi-step Random Delays, and Packet Dropouts

Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

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