Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: The problems of input sensitivity, structure detection, model validation and input signal selection are discussed in the non-linear context.
Abstract: Least squares parameter estimation algorithms for non-linear systems are investigated based on a non-linear difference equation model. A modified extended least squares algorithm, an instrumental variable algorithm and a new suboptimal least squares algorithm are considered. The problems of input sensitivity, structure detection, model validation and input signal selection are also discussed in the non-linear context.
126 citations
16 Apr 1990
TL;DR: In this paper, an adaptive decision feedback equalizer (DFE) for application in the USA digital cellular radio telephone system was proposed. But the performance sensitivity to time delay spread, Doppler shift, and timing jitter was not evaluated.
Abstract: The authors study an adaptive decision feedback equalizer (DFE) for application in the USA digital cellular radio telephone system. A synchronous DFE and a fractionally spaced DFE are adaptive and use a fast recursive least squares algorithm to track rapid channel variations. Simulation results indicating the performance sensitivity to time delay spread, Doppler shift, and timing jitter are presented. A DFE using a complex fast-Kalman adaptation algorithm is presented, and its bit error rate performance evaluated. The fast Kalman equalizer is found to possess good tracking ability and can track channel variations at vehicle speeds of 50 mph (80 km/h). Sensitivity to sample timing jitter can be reduced by using a DFE with fractionally spaced feedforward taps. >
126 citations
18 Nov 1991
126 citations
01 Dec 1990
TL;DR: It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights.
Abstract: The conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions are delineated. The effects of signal bandwidth, input signal-to-noise ratio (SNR), noise correlation, and noise nonstationarity are explicitly considered. Analytical modeling, Monte Carlo simulations and experimental results obtained using a hardware implementation are utilized to provide performance bounds for specified input conditions. It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights. The stochastic properties of the filter weights ensure that the performance of the adaptive filter is bounded by that of the optimal matched filter for known stationary input conditions. >
126 citations
TL;DR: Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed, applicable over the whole time interval, including the transient, and the approximation error can be explicitly calculated.
Abstract: A general family of tracking algorithms for linear regression models is studied. It includes the familiar least mean square gradient approach, recursive least squares, and Kalman filter based estimators. The exact expressions for the quality of the obtained estimates are complicated. Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed. These are applicable over the whole time interval, including the transient, and the approximation error can be explicitly calculated. >
125 citations