Networked H∞ filtering for linear discrete-time systems

Song, H; Yu, Li; Zhang, Wen-An

doi:10.1016/j.ins.2010.10.012

Cited by 52 publications

(26 citation statements)

References 30 publications

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“…Similarly to that in Zhai, Hu, Yasuda, and Michel (2002) and Song, Yu, and Zhang (2011), we sum (24) from k ¼ k 0 þ 1 to k ¼ 1 and change the order of summation yielding…”

Section: Resultsmentioning

confidence: 99%

DistributedH_∞filtering for sensor networks with switching topology

Zhang

Song

Wang

2013

International Journal of Systems Science

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In this article, the distributed H 1 filtering problem is investigated for a class of sensor networks under topology switching. The main purpose is to design the distributed H 1 filter that allows one to regulate the sensor's working modes. Firstly, a switched system model is proposed to reflect the working mode change of the sensors. Then, a stochastic sequence is adopted to model the packet dropout phenomenon occurring in the channels from the plant to the networked sensors. By utilising the Lyapunov functional method and stochastic analysis, some sufficient conditions are established to ensure that the filtering error system is mean-square exponentially stable with a prescribed H 1 performance level. Furthermore, the filter parameters are determined by solving a set of linear matrix inequalities (LMIs). Our results relates the decay rate of the filtering error system to the switching frequency of the topology directly and shows the existence of such a distributed filter when the topology is not varying very frequently, which is helpful for the sensor state regulation. Finally, the effectiveness of the proposed design method is demonstrated by two numerical examples.

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“…Similarly to that in Zhai, Hu, Yasuda, and Michel (2002) and Song, Yu, and Zhang (2011), we sum (24) from k ¼ k 0 þ 1 to k ¼ 1 and change the order of summation yielding…”

Section: Resultsmentioning

confidence: 99%

DistributedH_∞filtering for sensor networks with switching topology

Zhang

Song

Wang

2013

International Journal of Systems Science

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“…Consider a linear continuous-time system described by (1) as follows The disturbance attenuation level is set as γ = 0.5. By solving the linear-matrix inequality (25), the desired filter parameters under different sensor failure rates can be designed as shown in Table 1.…”

Section: Illustrative Examplementioning

confidence: 99%

“…The difference between these two estimation structures is that the sensors in a centralised scheme are connected to a fusion centre (FC) and act like transfers, while the sensors in a distributed scheme can communicate with the neighbours and act like local FCs. To deal with the multiple measurements from the deployed sensors, many centralised and distributed filtering algorithms have been proposed by concerning the packet dropouts [1][2][3][4], stochastic sampled-data [5], sensor saturations [6,7], sensor failures [8][9][10] and stochastic non-linearities [4,11].…”

Section: Introductionmentioning

confidence: 99%

Multi‐sensor‐based H _∞ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures

Song

Zhang

2014

IET Control Theory & Applications

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This study investigates the multi-sensor-based centralised estimation problem in heterogeneous sensor networks with a common communication channel. Owing to the heterogeneity of the distributed sensors, it is usually impossible to package the measurements into one packet and transmit them to the fusion centre (FC) together, which implies that the measurements should be forwarded to the FC asynchronously. In view of this, a novel stochastic competitive transmission strategy is proposed to stagger the sensors' transmissions. By using the asynchronous sampled information from the sensors, an H ∞ filter is designed for the FC to periodically generate estimates. The filter parameters are determined by solving a linear-matrix inequality. An illustrative example is provided to demonstrate the effectiveness of the proposed theoretical results.

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“…In real networked systems however, one may not obtain a good estimation performance by using the deterministic sampling scheme due to the random network congestion [13]. In this scenario, one may resort to the stochastic sampling scheme, which can help to improve the estimation performance.…”

Section: Introductionmentioning

confidence: 99%

A Markovian system approach to distributed H∞ filtering for sensor networks with stochastic sampling

Ling

Zhang

2014

Journal of the Franklin Institute

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Networked H∞ filtering for linear discrete-time systems

Cited by 52 publications

References 30 publications

DistributedH_∞filtering for sensor networks with switching topology

DistributedH_∞filtering for sensor networks with switching topology

Multi‐sensor‐based H _∞ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures

A Markovian system approach to distributed H∞ filtering for sensor networks with stochastic sampling

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Networked H∞ filtering for linear discrete-time systems

Cited by 52 publications

References 30 publications

DistributedH∞filtering for sensor networks with switching topology

DistributedH∞filtering for sensor networks with switching topology

Multi‐sensor‐based H ∞ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures

A Markovian system approach to distributed H∞ filtering for sensor networks with stochastic sampling

Contact Info

Product

Resources

About

DistributedH_∞filtering for sensor networks with switching topology

DistributedH_∞filtering for sensor networks with switching topology

Multi‐sensor‐based H _∞ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures