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.

A double-layer Associative Memory System (AMS) based on the Cerebella Model Articulation Controller (CMAC) (CMAC-AMS), owing to its advantages of simple structures, fast searching procedures and strong mapping capability between multidimensional input/output vectors, has been successfully used in such applications as real-time intelligent control, signal processing and pattern recognition. However, it is still suffering from its requirement for a large memory size and relatively low precision. Furthermore, the hash code used in its addressing mechanism for memory size reduction can cause a data-collision problem. In this paper, a new high-order Associative Memory System based on the Newton's forward interpolation formula (NFI-AMS) is proposed. The NFI-AMS is capable of implementing high-precision approximation to multivariable functions with arbitrarily given sampling data. A learning algorithm and a convergence theorem of the NFI-AMS are proposed. The network structure and the scheme of its learning algorithm reveal that the NFI-AMS has advantages over the conventional CMAC-type AMS in terms of high precision of learning, much less required memory size without the data-collision problem, and also has advantages over the multilayer Back Propagation (BP) neural networks in terms of much less computational effort for learning and fast convergence rate. Numerical simulations verify these advantages. The proposed NFI-AMS, therefore, has potential in many application areas as a new kind of associative memory system.

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.