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.

Online vehicle mass estimation using recursive least squares and supervisory data extraction

Abstract: This paper examines the online estimation of onroad vehicles' mass. It classifies existing estimators based on the dynamics they use for estimation and whether they are event-seeking or averaging. It then proposes an algorithm comparable to this literature in accuracy and speed, but unique in its minimal instrumentation needs and ability to provide conservative mass error estimates, in the 3sigma sense. The algorithm builds on the simple idea, inspired by perturbation theory, that inertial dynamics dominate vehicle motion over certain types of maneuvers. A supervisory algorithm searches for those maneuvers, and feeds the resulting filtered data into a recursive least squares-based mass estimator and conservative mass error estimator. Both simulation and field data demonstrate the viability of the resulting approach.

Recursive least squares constant modulus algorithm for blind adaptive array

Abstract: We consider the problem of blind adaptive signal separation with an antenna array, based on the constant modulus (CM) criterion. An approximation to the CM cost function is proposed, which allows the use of the recursive least squares (RLS) optimization technique. A novel RLS constant modulus algorithm (RLS-CMA) is derived, where the modulus power of the array output can take on arbitrary positive real values (i.e., fractional values allowed). Simulations are performed to compare the performance of the proposed RLS-CMA to other well-known algorithms for blind adaptive beamforming. Results indicate that the RLS-CMA has a significantly faster convergence rate and better tracking ability.

MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures

Abstract: We present the fundamentals of multiple-input, multiple-output (MIMO) signal processing for mode-division multiplexing (MDM) in multimode fiber (MMF). As an introduction, we review current long-haul optical transmission systems and how continued traffic growth motivates study of new methods to increase transmission capacity per fiber. We describe the key characteristics of MIMO channels in MMF, contrasting these with wireless MIMO channels. We review MMF channel models, the statistics derived from them, and their implications for MDM system performance and complexity. We show that optimizing performance and complexity requires management of channel parameters-particularly group delay (GD) spread and mode-dependent loss and gain-by design of transmission fibers and optical amplifiers, and by control of mode coupling along the link. We describe a family of fibers optimized for low GD spread, which decreases with an increasing number of modes. We compare the performance and complexity of candidate MIMO signal processing architectures in a representative long-haul system design, and show that programmable frequency-domain equalization (FDE) of chromatic dispersion (CD) and adaptive FDE of modal dispersion (MD) is an attractive combination. We review two major algorithms for adaptive FDE of MD-least mean squares (LMS) and recursive least squares (RLS)-and analyze their complexity, throughput efficiency, and convergence time. We demonstrate that, with careful physical link design and judicious choice of signal processing architectures, it is possible to overcome MIMO signal processing challenges in MDM systems.