Collaborative linear dynamical system identification by scarce relevant/irrelevant observations
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In the current paper, linear dynamical system identification by relevant and irrelevant multi-sensor observations is presented. In common system identification methods, it is presumed that observations are related to the system. However, the present study assumes that there are multi-sensor observations, whose sensors may give relevant information related to the system while some others do not. Furthermore, whether or not the sensor data is related or unrelated is unknown. Especially in large dimensions, the scarce observations of sensors pose a problem for estimating parameters. For this scenario, the current work will show that common methods are not appropriate. Therefore, to solve the problem of scarce relevant/irrelevant observations, the collaborative identification method is presented, in which relevant sensors collaborate with each other and, as a result, the estimation of parameters is more accurate. The results of synthetic and real dataset experiments indicate that the proposed model’s performance is superior to common methods.
KeywordsLinear dynamical system identification State-space model parameter estimation Multi-sensor multi-dimensional observations Unreliable observations Scarce observations Relevant/irrelevant observations
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