Encoding–Decoding-Based Recursive State Estimation for Mobile Robot Localization: A Multiple Description Case

This article irons out the issue of recursive state estimation for mobile robot localization under a multiple description coding scheme. For the sake of optimizing the utilization of channel resources, a novel two-description coding scheme is first introduced to facilitate measurements transmission by encoding the data into two equally important descriptions. The raw data is then reconstructed according to the number of the descriptions received by the decoders. Meanwhile, two random variables with Bernoulli distribution are used to display the occurrences of the packet dropouts in both parallel independent channels from the encoders to the decoders. The primary objective of this article is to develop a desired estimator tailored to the mobile robot localization problem in the presence of the data encoding–decoding mechanism, where the upper bound on the estimation error covariance is first guaranteed by virtue of mathematical induction and then is minimized by designing the estimator gain appropriately. Furthermore, the estimation performance is analyzed through the implementation of a sufficient condition. Finally, experimental examples are employed to verify the applicability of the proposed encoding–decoding-based recursive state estimation scheme for mobile robot localization.