Abstract
This work investigates the remote vehicle tracking issue over constrained monitoring sensors and unreliable communication networks. A saturation function is used to describe the bounded time varying acceleration of the vehicle. A set of matrices are introduced to model the sensor monitoring conditions called captured states (CSs), and a Markov chain with time varying and partially unknown transition probability (TVPUTP) is proposed to analyze the conditions of the CSs. Then, a CS dependent nonfragile estimator is designed based on the measured unreliable vehicle information, and the estimation error system (EES) is derived. Two theorems are established to ensure that the EES satisfies the finite-horizon (FH) $H_\infty $ performance. Finally, an example is introduced to show the effectiveness of the results.
Highlights
Vehicle intelligence is a main trend in both automotive and transportation fields, and the research of intelligent vehicle mainly focuses on improving the safety, as well as providing excellent human-vehicle interface, and so on [1], [2]
The FH H∞ performance is analyzed for the estimation error system (EES) (17) with time varying and partially unknown transition probability (TVPUTP)
In terms of (30) and (31), considering the FH H∞ performance defined in Definition 1, we introduce the index J (N )
Summary
Vehicle intelligence is a main trend in both automotive and transportation fields, and the research of intelligent vehicle mainly focuses on improving the safety, as well as providing excellent human-vehicle interface, and so on [1], [2]. Remark 1: The vehicle may be missed or captured by the limited monitoring sensors, which was described by random Bernoulli process [15], Markov model [16], [17]. It needs to be emphasized that the vehicle is missing for the monitoring sensors and doesn’t need to transmit the zero data to remote estimator when θ(k) = 1, in this case, we define α1(k) satisfying E{α1(k) = 1} = 0. Definition 1 [36]: Given a scalar γ > 0 and a matrix Q > 0, if the inequality (19) holds for k ∈ [0, N ], the time varying EES (17) satisfies the FH H∞ performance
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