Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Isolation of resonance in acoustic backscatter from elastic targets using adaptive estimation schemes

Abstract: The problem of underwater target detection and classification from acoustic backscatter is the central focus of this paper. It has been shown that at certain frequencies the acoustic backscatter from elastic targets exhibits certain resonance behavior which closely relates to the physical properties of the target such as dimension, thickness, and composition. Several techniques in both the time domain and frequency domain have been developed to characterize the resonance phenomena in acoustic backscatter from spherical or cylindrical thin shells. The purpose of this paper is to develop an automated approach for identifying the presence of resonance in the acoustic backscatter from an unknown target by isolating the resonance part from the specular contribution. An adaptive transversal filter structure is used to estimate the specular part of the backscatter and consequently the error signal would provide an estimate of the resonance part. An important aspect of this scheme Lies in the fact that it does not require an underlying model for the elastic return. The adaptation rule is based upon fast Recursive Least Squares (RLS) learning. The approach taken in this paper is general in the sense that it can be applied to targets of unknown geometry and thickness and, further, does not require any a priori information about the target and/or the environment. Test results on acoustic data are presented which indicate the effectiveness of the proposed approach.

Total least mean squares algorithm

Abstract: Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm. The paper gives the statistical analysis for this algorithm, studies the global asymptotic convergence of this algorithm by an equivalent energy function, and evaluates the performances of this algorithm via computer simulations.

Optimal least squares time-domain synthesis of recursive digital filters

Abstract: A method for finding the coefficients of an nth-order linear recursive digital filter, which gives the best least squares approximation to a desired pulse response over a finite interval, is presented. A relationship is derived between the approximating error corresponding to an optimal set of numerator coefficients and the error produced by an overdetermined set of linear equations, which is a function of the denominator coefficients only. This relation provides a computational algorithm for calculating the optimal coefficients by iteratively solving weighted sets of linear equations in terms of the denominator coefficients only. Both theoretical and numerical results are presented. Also, bounds are found on the interval in which the norm of the optimum error must lie.