scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: This work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation, and sees that the structure of a patient's respiratory motion trace has strong influence on the outcome of prediction.
Abstract: In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ?-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71?min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm?which is one of the algorithms currently used in the CyberKnife?is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient?s respiratory motion trace has strong influence on the outcome of prediction. Further work is needed to determine a priori the suitability of an individual?s respiratory behaviour to motion prediction.

73 citations

Journal ArticleDOI
TL;DR: A robust recursive least squares (RLS) approach to estimate time-varying parameters of a ZIP load model at the substation level, and a RLS with variable forgetting factors to capture the variations of model parameters under different situations, including continuous and sudden changes of parameters.
Abstract: Due to the increasing penetration of intermittent renewable energy and highly stochastic load behavior, it is challenging to effectively assess conservation voltage reduction (CVR) in power distribution systems. This paper proposes a robust time-varying load modeling technique to accurately identify load-to-voltage (LTV) dependence, yielding an improved CVR assessment scheme. In particular, we propose a robust recursive least squares (RLS) approach to estimate time-varying parameters of a ZIP load model at the substation level. Based on the identified load model, we are able to effectively evaluate LTV and analyze CVR factors. We propose a RLS with variable forgetting factors to capture the variations of model parameters under different situations, including continuous and sudden changes of parameters. To further enable RLS to suppress bad or missing measurements, we advocate to use the Huber M-estimator with a convex cost function. Finally, the robust RLS is solved by an iteratively reweighted technique. We demonstrate the effectiveness and the robustness of the proposed method using both simulations and field tests.

73 citations

Journal ArticleDOI
TL;DR: A one-dimensional residual Convolutional Neural Networks (1D-ResCNN) model for raw waveform-based EEG denoising is proposed to solve the above problem and can yield cleaner waveforms and achieve significant improvement in SNR and RMSE.

73 citations

Journal ArticleDOI
TL;DR: Analytical and simulation results are presented illustrating the convergence performance of the receiver when the tap weights are adjusted using either the least mean square (LMS) or recursive least squares (RLS) adaptive algorithms.
Abstract: This paper presents a novel receiver for direct sequence spread-spectrum signals over channels containing interference and multipath. The receiver employs an adaptive correlator that jointly detects the transmitted data, removes interference, and compensates for multipath. The optimum correlation vector is derived by determining the Wiener vector that minimizes the mean squared error (MSE) between the transmitted data bit and the correlator output. For an additive white Gaussian noise (AWGN) channel, the optimal correlation vector is the spreading sequence used by the transmitter. For interference and multipath channels, the optimal correlation vector will suppress the interference and combine the multipath while optimizing the detection of the transmitted data bit. The paper presents analytical and simulation results which illustrate the bit-error rate (BER) performance of the receiver in multipath and narrowband interference. Additionally, simulation results are presented illustrating the convergence performance of the receiver when the tap weights are adjusted using either the least mean square (LMS) or recursive least squares (RLS) adaptive algorithms.

72 citations

Proceedings ArticleDOI
19 Apr 2009
TL;DR: Application of the WL-RLS algorithm to adaptive beamforming of mixed BPSK and QPSK signal transmissions shows that the system can extract all of the transmitted signal outputs in certain overloaded scenarios, and it performs up to 3dB better than the conventional RLS beamformer when the array is not overloaded.
Abstract: Adaptive beamforming algorithms typically rely on a complex linear model between the sensor measurements and the desired signal output that does not enable the best performance from the data in some situations. In this paper, we present an extension of the well-known recursive least-squares algorithm for adaptive filters to widely-linear complex-valued signal and system modeling. The widely-linear RLS algorithm exploits a structured covariance matrix update that maintains information about the non-circularity of the input data to solve the widely-linear least-squares task at each snapshot. In addition, the WL-RLS algorithm can easily be switched between conventional and widely-linear complex modeling as needed. Application of the method to adaptive beamforming of mixed BPSK and QPSK signal transmissions shows that the system can extract all of the transmitted signal outputs in certain overloaded scenarios, and it performs up to 3dB better than the conventional RLS beamformer when the array is not overloaded.

72 citations


Network Information
Related Topics (5)
Control theory
299.6K papers, 3.1M citations
88% related
Optimization problem
96.4K papers, 2.1M citations
88% related
Wireless sensor network
142K papers, 2.4M citations
85% related
Wireless
133.4K papers, 1.9M citations
85% related
Feature extraction
111.8K papers, 2.1M citations
85% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237