A cumulant-based lattice algorithm for multichannel adaptive filtering is proposed in this paper. Proposed algorithm takes into account the advantages of higer-order statistics, that is, improvement of estimation accuracy, blindness to colored Gaussian noise and the possibility to estimate the nonminimum-phase system etc. Without invoking the Instrumental Variable () method as used in other papers [1], [2], the algorithm is derived directly from the recursive pseudo-inverse matrix. The behavior of the algorithm is illustrated by numerical examples.

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.

A high resolution iterative algorithm for estimating the direction-of-arrival of multiple wide band sources is proposed in this paper. For equally spaced array structure, two Unitary Transform based approaches are proposed in frequency domain for signal subspace processing in both coherent multipath and incoherent environment. Given a priori knowledge of the initial estimates of DOA, with proper spatial prefiltering to separate multiple groups of closely spaced sources, our proposed algorithm is shown to have high resolution capability even in coherent multipath environment without reducing the angular resolution, compared with the use of subarray. Compared with the conventional algorithm, the performance by the proposed algorithm is shown by the simulations to be improved under low Signal to Noise Ratio (SNR) while the performance is not degraded under high SNR. Moreover the computation burden involved in the eigencomputation is largely reduced by introducing the Pesudo-Hermitian matrix approximation.