Exponential Decomposition and Hankel Matrix Franklin T. Luk Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, NT, Hong Kong.
[email protected] Sanzheng Qiao Department of Computing and Software, McMaster University, Hamilton, Ontario L8S 4L7 Canada.
[email protected] David Vandevoorde Edison Design Group, Belmont, CA 94002-3112 USA.
[email protected] 1 Introduction We consider an important problem in mathematical modeling: Exponential Decomposition. Given a finite sequence of signal values {s1,s2,...,sn}, determine a minimal positive integer r, complex coefficients {c1,c2,...,cr}, and distinct complex knots {z1,z2,...,zr}, so that the following exponential decomposition signal model is satisfied: r − s = c zk 1, for k =1,...,n. (1.1) k X i i i=1 We usually solve the above problem in three steps. First, determine the rank r of the signal sequence. Second, find the knots zi. Third, calculate the coefficients {ci} by solving an r × r Vandermonde system: W (r)c = s(r), (1.2) where 1 1 · · · 1 c1 s1 z1 z2 · · · zr c2 s2 (r) (r) W = . , c = . and s = . . . .. . . r−1 r−1 r−1 z1 z2 · · · zr cr sr A square Vandermonde system (1.2) can be solved efficiently and stably (see [1]). 275 276 Exponential Decomposition and Hankel Matrix There are two well known methods for solving our problem, namely, Prony [8] and Kung [6]. Vandevoorde [9] presented a single matrix pencil based framework that includes [8] and [6] as special cases. By adapting an implicit Lanczos pro- cess in our matrix pencil framework, we present an exponential decomposition algorithm which requires only O(n2) operations and O(n) storage, compared to O(n3) operations and O(n2) storage required by previous methods.