Simplified adaptive noise subspace algorithms for robust direction tracking

Abstract

The authors have developed two simplified adaptive eigen-subspace methods which robustly converge to the noise subspace only if the number of sources is less than the number of sensors. The first simplification, achieved by introducing an orthogonal factor, reduces the computational complexity and preserves the parallel structure of the inflation method (Yang and Kaveh, 1988). The convergence performances and initialisation behaviours perform better than other adaptive eigen-subspace algorithms when the number of sources is unknown. Further simplification is achieved using a unitary transformation approach (Huarng and Yeh, 1991). This leads to an adaptive real eigen-subspace algorithm which further reduces the computational complexity and also resolves the paired multipath problem. Simulations for evaluations of the proposed and the existing algorithms are also included in this paper.