Abstract
This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example.
Highlights
Sensors have been long playing an essential role in various branches of science research and industrial engineering
The main difficulties of the addressed problem can be identified as follows: i) How to unify the asynchronous measurement data into a framework of identical time-scale and design the corresponding recursive form of the optimal estimate in the existence of both multiplicative and crosscorrelated noises? ii) How to design appropriate fault detection scheme as well as missing measurement compensation mechanism, to mitigate the effects from abnormal data on the estimation performance?. It is our objective in this paper to provide a systematic framework within which the fault-tolerant and packet dropouts compensation state estimation algorithm can be analyzed and designed for the addressed multi-sensor systems subject to both multiplicative and cross-correlated noises
1) The estimation issue is first studied for the multi-sensor systems whose measurement outputs are asynchronous and subject to both multiplicative and cross-correlated noises; and 2) a reliable state estimation is proposed where a fault detection mechanism and a packet dropouts compensation method are implemented to prevent the abnormal data from deteriorating the estimation performance and avoid the im
Summary
Sensors have been long playing an essential role in various branches of science research and industrial engineering. Since in many practical engineering such as the aforementioned integrated INS/GNSS navigation, the sensors are always deployed in very harsh sometimes even hostile environments (e.g., high speed, strong disturbance, extreme overload, etc) In such cases, the phenomena of sensor failures are inevitable which might yield abnormal data and degrade the estimation performance. To sum up, it is our objective in this paper to provide a systematic framework within which the fault-tolerant and packet dropouts compensation state estimation algorithm can be analyzed and designed for the addressed multi-sensor systems subject to both multiplicative and cross-correlated noises. 1) The estimation issue is first studied for the multi-sensor systems whose measurement outputs are asynchronous and subject to both multiplicative and cross-correlated noises; and 2) a reliable state estimation is proposed where a fault detection mechanism and a packet dropouts compensation method are implemented to prevent the abnormal data from deteriorating the estimation performance and avoid the im-.
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