/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

https://www.cell.com/neuron/pdf/S0896-6273(15)01084-3.pdf

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the cameraâIMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.

http://robotics.usc.edu/publications/media/uploads/pubs/645.pdf

Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

Review of state of the art in smart rotor control research for wind turbines

Preface 1. Introduction 2. Linear algebra 3. Discrete-time signals and systems 4. Random variables and signals 5. Kalman filtering 6. Estimation of spectra and frequency response functions 7. Output-error parametric model estimation 8. Prediction-error parametric model estimation 9. Subspace model identification 10. The system identification cycle Notation and symbols List of abbreviations References Index.

/pdf/filtering-and-system-identification-a-least-squares-approach-4yakodifk9.pdf

Filtering and System Identification: A Least Squares Approach

Subspace identification of multivariable linear parameter-varying systems

Kernel methods for subspace identification of multivariable LPV and bilinear systems

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3656&context=eispapers

Wiener Model Identification and Predictive Control for Dual Composition Control of a Distillation Column

Subspace identification can be used to obtain models of piecewise linear state-space systems for which the switching is known. The models should not switch faster than the block size of the Hankel matrices used. The nonconsecutive parts of the input and output data that correspond to one of the local linear systems can be used to obtain the system matrices of that system up to a linear state transformation. The linear systems obtained in this way cannot be combined directly, because the state transformation is different for each of the local linear systems. The transitions between the local linear systems can be used to transform the models to the same state space basis. We show that the necessary transformations can be obtained from the data, if the data contains a sufficiently large number of transitions for which the states at the transition are linearly independent. An algorithm to determine the transformations is presented, and the sensitivity with respect to noise is investigated using a Monte-Carlo simulation.

/pdf/subspace-identification-of-piecewise-linear-systems-1xikzmd32w.pdf

Vincent Verdult

Papers

Filtering and System Identification: A Least Squares Approach

Subspace identification of multivariable linear parameter-varying systems

Kernel methods for subspace identification of multivariable LPV and bilinear systems

Wiener Model Identification and Predictive Control for Dual Composition Control of a Distillation Column

Subspace identification of piecewise linear systems