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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.

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks