A TQR-ITERATION BASED ADAPTIVE SVD FOR REAL-TIME ANGLE AND FREQUENCY TRACKING

The transposed QR (TQR) iteration is a square root version of the symm etric QR iteration. The TQR algorithm converges directly to the singul ar value decomposition (SVD) of a matrix and was originally derived to provide a means to identify and reduce the effects of outliers for ro bust SVD computation. This paper extends the TQR algorithm to incorpor ate complex data and weighted norms, formulates a TQR-iteration based adaptive SVD algorithm, develops a real time systolic architecture, an d analyzes performance. The applications of high resolution angle and frequency tracking are developed and the updating scheme is so tailore d. A deflation mechanism reduces both the computational complexity of the algorithm and the hardware complexity of the systolic architecture , making the method ideal for real time applications. Simulation resul ts demonstrate the performance of the method and compare it to existin g SVD tracking schemes. The results show that the method is exceptiona l in terms of performance to cost ratio and systolic implementation.