Advances in reduced-rank adaptive beamforming

DOA estimation based on data level Multistage Nested Wiener Filter

“…In addition, the group of orthogonal weight vectors extracted by earlier several forward recursions spans the signal subspace, which ensures that the MSNWF algorithm is completed for the estimation of the direction of arrival and the reduced-rank adaptive filtering. In order to avoid the formation of blocking matrices required in the original algorithm, Zoltowski et al proposed a data level recursive MSNWF algorithm [23,24] as shown in Fig. 2, which effectively reduces the computational complexity.…”

“…1) Since the use of MSNWF in this technique realizes the subspace eigen decomposition, computation of inversion of the covariance matrix becomes unnecessary and thus reduces the complexity of computation; the MSNWF-DOA can be easily applied in hardware platform [23].…”

Direction of Arrival Estimation Based on the Multistage Nested Wiener Filter

“…This technique has clear advantage on the DOA estimation when interference exists, but it still needs the computation of matrix inversion which is not easy to be applied to a practical system. Based on this structure, a DOA estimation technique based on Multistage Nested Wiener Filter (MSNWF) [19][20][21][22][23][24][25] is proposed in [26]. In [26], Yu stated an original MSNWF algorithm [27] to estimate the DOAs, which used a filter and blocking matrix to avoid the calculation of covariance matrix inversion.…”