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.
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17 May 2005
TL;DR: In this article, an active noise cancellation technique for recovering wearable biosensor signals corrupted by bodily motion is presented, where a finger mounted photoplethysmograph (PPG) ring sensor with a collocated MEMS accelerometer is considered.
Abstract: This paper presents an active noise cancellation technique for recovering wearable biosensor signals corrupted by bodily motion. A finger mounted photoplethysmograph (PPG) ring sensor with a collocated MEMS accelerometer is considered. The system by which finger acceleration disturbs PPG output is identified and a means of modeling this relationship is prescribed using either FIR or Laguerre models. This means of modeling motivates the use of a recursive least squares active noise cancellation technique using the MEMS accelerometer reading as an input for a FIR or Laguerre model. The model parameters are identified and tuned in real time to minimize the power of the recovered PPG signal. Experiments show that the active noise cancellation method can recover pulse information from PPG signals corrupted with up to 2G of acceleration with 85% improvement in mean squared error.
87 citations
TL;DR: In this paper, a method for finding physically reasonable parameters and confidence limits for parameters is described based on parameter scaling and diagonalization of the matrix of the normal equations, which is based on the assumption that the relationships provided by the observations are not really linearly independent when the random errors in the observations were considered.
87 citations
26 Sep 2004
TL;DR: Experimental results show that this generalization of the iterative closest point (ICP) algorithm for shape registration is superior to the least squares counterpart.
Abstract: This paper investigates the use of a total least squares approach in a generalization of the iterative closest point (ICP) algorithm for shape registration. A new Generalized Total Least Squares (GTLS) formulation of the minimization process is presented opposed to the traditional Least Squares (LS) technique. Accounting for uncertainty both in the target and in the source models will lead to a more robust estimation of the transformation. Robustness against outliers is guaranteed by an iterative scheme to update the noise covariances. Experimental results show that this generalization is superior to the least squares counterpart.
87 citations
TL;DR: This work deals with interference suppression in asynchronous direct-sequence code-division multiple-access (CDMA) systems employing binary phase-shift keying modulation and derives a new family of minimum mean-square-error detectors, which differ from their conventional counterparts in that they minimize a modified cost function.
Abstract: We deal with interference suppression in asynchronous direct-sequence code-division multiple-access (CDMA) systems employing binary phase-shift keying modulation. Such an interference may arise from other users of the network, from external low-rate systems, as well as from a CDMA network coexisting with the primary network to form a dual-rate network. We derive, for all of these cases, a new family of minimum mean-square-error detectors, which differ from their conventional counterparts in that they minimize a modified cost function. Since the resulting structure is not implementable with acceptable complexity, we also propose some suboptimum systems. The statistical analysis reveals that both the optimum and the suboptimum receivers are near-far resistant, not only with respect to the other users, but also with respect to the external interference. We also present a blind and a recursive least squares-based, decision-directed implementation of the receivers wherein only the signature and the timing of the user to be decoded and the signaling time and the frequency offset of the external interferer are assumed known. Finally, computer simulations show that the proposed adaptive algorithm outperforms the classical decision-directed RLS algorithm.
87 citations
TL;DR: This paper uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed.
Abstract: In this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real-time The method does not rely on the system power flow model Instead, it uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed Such a measurement-based approach is desirable since it is adaptive to changes in system operating point and topology We further improve the adaptability of the proposed approach to such changes by using weighted and recursive least-squares estimation Through numerical examples, we illustrate the advantages of our proposed DF estimation approach over the conventional model-based one in the context of contingency analysis and generation re-dispatch
86 citations