Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	甘叠博士后，中关村实验室
Inviter:	王颖博士后
Title:	Stability of FFLS algorithm based on compressed sensing
Language:	English
Time & Venue:	2023.06.18 08:30-10:00 南楼733
Abstract:	To identify the unknown sparse time-varying parameters of the stochastic dynamic system, we integrate compressive sensing theory with the traditional recursive least squares with forgetting factor (FFLS) algorithm, and propose a compressed adaptive filtering algorithm. Our algorithm is designed to first compress the original high-dimensional sparse regression vector by using the sensing matrix, and then apply the FFLS algorithm to estimate the compressed parameters. Subsequently, the original high-dimensional sparse parameters can be well recovered by a reconstruction technique. We introduce an excitation condition on the compressed stochastic regressors, under which the stability of the proposed algorithm (i.e., the upper bound of the estimation error) is established without assuming independence, stationarity or ergodicity of the system signals. The effectiveness of our theoretical results is demonstrated by a numerical example, which also shows that our proposed algorithm has better performance than both the compressed least mean squares algorithm and the uncompressed FFLS algorithm for tracking high-dimensional sparse parameters.