Adaptive Event-triggered Robust Filtering over Sensor Networks with Markov Switching Topologies

Distributed H ∞ Consensus Filtering for Parabolic Distributed Parameter Systems with Multiple Missing Measurements: A Mobile Sensing Approach

“…Te disturbance attenuation level c > 0 and the flter parameter c i are given. Under assumptions (A1)-(A5) and inequalities (26), for the distributed parameter system (15), an H ∞ consensus flter (20) is then constructed so that the fltering error system (21) with w(t) � 0 is globally asymptotically stable in the mean square and also satisfes ( 22) under the zero initial condition for all non-zero w(t) if the mobile sensing device has a spatial distribution as (8), and its velocity law is as follows:…”

“…Te disturbance attenuation level c > 0 and the flter parameter c i and G i are given. For the distributed parameter system (15), an H ∞ consensus flter (61) is then constructed so that the fltering error system (62) with w(t) � 0 is globally asymptotically stable in the mean square and also satisfes ( 22) for all nonzero w(t) under the zero initial condition if assumptions (A1)-(A5), inequality (26), and the matrix inequality (47) hold.…”

“…In real-world engineering, sensor temporal failure or network transmission delay are the main causes of the missing measurement phenomenon in networked environments. Te frst study addressing the fltering problem with missing measurements defned by Bernoulli distributions appeared in [13], and works addressing other lumped parameter systems with probabilistic missing data followed in [14][15][16]. Te studies mentioned above brought up the fact that the observed output from fxed sensors was incomplete.…”