Improved Kalman Filtering for Systems with Randomly Delayed and Lost Measurements

Rezaei, Hossein; Esfanjani, Reza Mahboobi; Sedaaghi, Mohammad Hossein

doi:10.1007/s00034-014-9740-6

Cited by 25 publications

(31 citation statements)

References 17 publications

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“…Combining Lemma 4, (53), (59) and (41)-(42), the condition (22) in Lemma 3 are satisfied. Then it can be obtained that…”

Section: Lemmamentioning

confidence: 72%

“…The H ∞ filtering has been reported in [32] for linear continuous-time systems with fixed multiple-timedelay measurements, and the state estimator has been designed in [13] for a class of discrete systems with distributed sensor delays as well as time-varying delays over a certain interval. Since the measurement delays often occur in a random way under realtime distributed decision-making and multiplexed data communication environment [30], the filtering problems with randomly occurring measurement delays have been considered in [20,22,31], where all channels share the same type of delay characteristics. As further extension, considering that the measurements are collected through multiple sensors with different physical properties, a series of mutually independent Bernoulli random variables are utilized in [18] to parameterize the randomly occurring measurement delays such that each sensor has individual delay rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gain-Constrained Extended Kalman Filtering with Stochastic Nonlinearities and Randomly Occurring Measurement Delays

Zhang

Zhao

et al. 2016

Circuits Syst Signal Process

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In this paper, the gain-constrained extended Kalman filtering problem is studied for discrete time-varying nonlinear system with stochastic nonlinearities and randomly occurring measurement delays. Both deterministic and stochastic nonlinearities are simultaneously present in the model, where the stochastic nonlinearities are described by first moment and can encompass several classes of well-studied stochastic nonlinear functions. A diagonal matrix composed of mutually independent Bernoulli random variables is introduced to reflect the phenomenon of randomly occurring measurement delays caused by unfavorable network conditions. The aim of the addressed filtering problem is to design a finite-horizon recursive filter such that, for all stochastic nonlinearities, randomly occurring measurement delays and gain constraint, the upper bound of the cost function involving filtering error is minimized at each sampling time. It is shown that the filter gain is obtained by solving matrix equations. A numerical simulation example is provided to illustrate the effectiveness of the proposed algorithm.

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“…Combining Lemma 4, (53), (59) and (41)-(42), the condition (22) in Lemma 3 are satisfied. Then it can be obtained that…”

Section: Lemmamentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Gain-Constrained Extended Kalman Filtering with Stochastic Nonlinearities and Randomly Occurring Measurement Delays

Zhang

Zhao

et al. 2016

Circuits Syst Signal Process

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“…Comparative simulation results are presented to demonstrate that the average RMSE of the estimation is drastically decreased by the proposed method compared to a rival one in the literature. Extending the developed filtering strategy for dynamical systems with bounded noises 28,29 and considering more imperfections in the communication medium, such as transmission delay, 30–32 define future research lines.…”

Section: Resultsmentioning

confidence: 99%

Event‐triggered distributed Kalman filter with consensus on estimation for state‐saturated systems

Rezaei

Esfanjani

Akbari

et al. 2020

Intl J Robust & Nonlinear

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This study investigates the problem of event-triggered distributed state estimation for discrete-time, nonlinear systems with state saturation. A Kalman-like filter is developed, and consensus is first achieved with respect to the prediction estimation. The accuracy of the computed estimation is then improved via two recursive equations. The filter gains are determined in each sensor node via utilization of only an upper bound for the common error covariance, thereby resulting in a lower computational burden. Finally, the boundedness of the estimation errors is analyzed, and a comparison of the simulation results demonstrates that the proposed filtering method outperforms a recent rival method. K E Y W O R D S distributed filter, event-triggered transmission, sensor networks, state-saturated system 1 INTRODUCTION An important aspect of multisensor systems is to design distributed filters, wherein the states of a dynamic process are cooperatively estimated based on noisy measurements obtained from several sensor nodes. In distributed filtering, information is exchanged between neighboring sensors to ensure that networked filters can arrive at an agreement (consensus) as a whole on the estimated value. Recently, besides investigating different aspects of networked systems, 1,2 the problem of distributed state estimation over sensor networks has received much attention because of its wide applications, including target tracking, 3,4 communication networks, 5 and industrial operations. 6,7 In general, approaches based on consensus strategy in distributed filtering can be divided into three main categories: (1) consensus on estimation, 8,9 in which the sum of weighted differences between the local and neighboring estimations was added to the estimator of each node; (2) consensus on measurement, 10 wherein consensus is obtained on local measurements and innovation covariance. However, it involves high communication costs and does not assure convergence and (3) consensus on information, 11 especially when local information used is uniform and average, such that the stability of the algorithm is provided at all times. It is clear that the challenges posed as a result of limited communication resources and energy supply should be considered while designing estimators for commonly used battery-operated sensors. Therefore, several studies have been published to reduce unnecessary operations for data transmission in multi-sensor systems by using an event-triggered communication protocol. 12-16 For instance, in the work of Song et al, 15 a distributed extended Kalman filtering problem was suggested for discrete-time nonlinear systems with multiple fading measurements, where an event-triggered communication scheme is used in both the sensor-to-estimator and estimator-to-estimator channels. In the paper

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“…Recursive estimation for systems with multiplicative noises, random delays, and multiple packet dropouts is proposed in Reference 28. Improved Kalman filtering for systems with random delay and lost measurements is presented based on an innovation approach in Reference 29, where the inversion of the innovation covariance matrix is avoided using the Moore‐Penrose inverse concept. Furthermore, an adaptive Kalman filter is developed in Reference 30 for systems with delay and dropout by introducing an adaptation factor in the objective function.…”

Section: Introductionmentioning

confidence: 99%

State estimation for a networked control system with packet delay, packet dropouts, and uncertain observation in S‐E and C‐A channels

Roy

Srinivasan

2020

Optim Control Appl Methods

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Summary In this work, a minimum variance estimator is designed for a networked system with inherent network imperfections in both sensor to estimator (S‐E) and controller to actuator (C‐A) channels simultaneously. The channels are affected by packet delays, dropouts, and uncertain observations. These effects are modeled using five Bernoulli distributed random variables. Correlation of noise at neighboring time caused by random delay is avoided by introducing two additional variables in the augmented stochastic model. The developed augmented stochastic model can handle network imperfections in both the S‐E and C‐A channels simultaneously. A minimum variance recursive linear estimator is designed using an innovation approach and projection theorem. Furthermore, sufficient condition is presented for the existence of steady state property of the proposed estimator. Simulation studies are carried out for the proposed estimator using a numerical example and a single link robot arm. Finally, performance comparison with other popular filters shows the effectiveness of the designed estimator.

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Improved Kalman Filtering for Systems with Randomly Delayed and Lost Measurements

Cited by 25 publications

References 17 publications

Gain-Constrained Extended Kalman Filtering with Stochastic Nonlinearities and Randomly Occurring Measurement Delays

Gain-Constrained Extended Kalman Filtering with Stochastic Nonlinearities and Randomly Occurring Measurement Delays

Event‐triggered distributed Kalman filter with consensus on estimation for state‐saturated systems

State estimation for a networked control system with packet delay, packet dropouts, and uncertain observation in S‐E and C‐A channels

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