An Alternative Look at the Constant-Gain Kalman Filter for State Estimation Over Erasure Channels

Silva, Eduardo I.; Solís, Miguel A.

doi:10.1109/tac.2013.2263647

Cited by 49 publications

(26 citation statements)

References 23 publications

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“…Another example is estimation over lossy networks where the measurement matrices are timevarying [65], [66]. On their routes to the gateway, sensor packets, possibly aggregated with measurements from several nodes, may become intermittent because of time-varying transmission intervals or delays [67], [68], packet dropouts [68]- [76], random message exchanges depending on the availability of appropriate network links [77], fading channels [78], and other communication constraints [79]. The measurement matrix H k is thus unknown until the sensor measurements are received at time k.…”

Section: Problem Formulationmentioning

confidence: 99%

Compressive Privacy for a Linear Dynamical System

Song

Wang

Tay

2020

IEEE Trans.Inform.Forensic Secur.

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We consider a linear dynamical system in which the state vector consists of both public and private states. One or more sensors make measurements of the state vector and sends information to a fusion center, which performs the final state estimation. To achieve an optimal tradeoff between the utility of estimating the public states and protection of the private states, the measurements at each time step are linearly compressed into a lower dimensional space. Under the centralized setting where all measurements are collected by a single sensor, we propose an optimization problem and an algorithm to find the best compression matrix. Under the decentralized setting where measurements are made separately at multiple sensors, each sensor optimizes its own local compression matrix. We propose methods to separate the overall optimization problem into multiple sub-problems that can be solved locally at each sensor. We consider the cases where there is no message exchange between the sensors; and where each sensor takes turns to transmit messages to the other sensors. Simulations and empirical experiments demonstrate the efficiency of our proposed approach in allowing the fusion center to estimate the public states with good accuracy while preventing it from estimating the private states accurately.

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Section: Problem Formulationmentioning

confidence: 99%

Compressive Privacy for a Linear Dynamical System

Song

Wang

Tay

2020

IEEE Trans.Inform.Forensic Secur.

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“…State augmentation [23] can handle fixed delay up to several sampling periods. In [7], [9], [22], and [24], estimation and fusion performance using Kalman filters (KFs) under variable packet loss rates has been considered. A dynamic selective fusion method based on information gain is proposed in [15] so that fusion is deferred until enough information has arrived at the fusion center.…”

Section: A Related Workmentioning

confidence: 99%

Effect of Retransmission and Retrodiction on Estimation and Fusion in Long-Haul Sensor Networks

Liu

Wang

Rao

et al. 2016

IEEE/ACM Trans. Networking

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In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this paper, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Moreover, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.Index Terms-Data association, long-haul sensor networks, mean-square-error (MSE) and root-mean-square-error (RMSE) performance, message retransmission, prediction and retrodiction, state estimation and fusion.

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“…In [6], [12], [28], [29], estimation and fusion performance using Kalman filters (KFs) under variable packet loss rates have been considered. Studies including [24], [35], [36] have addressed the so-called out-of-sequence-measurement (OOSM) issue -where an OOSM is defined as a measurement that has been generated earlier but arrives later -and their common goal is to update the current state estimate with an earlier measurement without reordering the measurements and recalculating the state estimator recursively.…”

mentioning

confidence: 99%

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

Liu

Brigham

Rao

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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Nageswara S. V. (2017) 'Estimation and fusion for tracking over long-haul links using articial neural networks.', IEEE transactions on signal and information processing over networks., 3 (4). pp. 760-770. Further information on publisher's website: Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics -such as the position -is expected to possess much improved quality. In this work, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). The joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.Index Terms-Long-haul sensor networks, state estimate fusion, artificial neural networks, estimation bias, error regularization, root-mean-square-error (RMSE) performance, reporting deadline.

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An Alternative Look at the Constant-Gain Kalman Filter for State Estimation Over Erasure Channels

Cited by 49 publications

References 23 publications

Compressive Privacy for a Linear Dynamical System

Compressive Privacy for a Linear Dynamical System

Effect of Retransmission and Retrodiction on Estimation and Fusion in Long-Haul Sensor Networks

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

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