Data compression for subspace-based identification using periodic inputs
Tóm tắt
In this paper we develop data compression techniques for subspace methods to extend their ability to work with long data records. Satisfactory results are obtained when a subspace algorithm is used to identify the system based on averaged measurements with periodic input signals. We also discuss pitfalls of using multitonal input signals in combination with the proposed scheme.
Từ khóa
#Data compression #Signal processing #Recursive estimation #Frequency domain analysis #Time domain analysis #Signal generators #Frequency estimation #Random access memory #Parameter estimation #Least squares approximationTài liệu tham khảo
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