On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4}  years. We infer the component masses of the binary to be between 0.86 and 2.26  M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60  M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28  deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8}  Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

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GW170817: observation of gravitational waves from a binary neutron star inspiral

An introduction to the Kalman filter

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers & results.

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An Introduction to the Kalman Filter

The paper describes the engineering and design of a doubly fed induction generator (DFIG), using back-to-back PWM voltage-source converters in the rotor circuit. A vector-control scheme for the supply-side PWM converter results in independent control of active and reactive power drawn the supply, while ensuring sinusoidal supply currents. Vector control of the rotor-connected converter provides for wide speed-range operation; the vector scheme is embedded in control loops which enable optimal speed tracking for maximum energy capture from the wind. An experimental rig, which represents a 7.5 kW variable speed wind-energy generation system is described, and experimental results are given that illustrate the excellent performance characteristics of the system. The paper considers a grid-connected system; a further paper will describe a stand-alone system.

Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation

https://bura.brunel.ac.uk/bitstream/2438/14221/1/FullText.pdf

A survey of deep neural network architectures and their applications

Probability and Random Variables Mathematical Description of Random Signals Response of Linear Systems to Random Inputs Wiener Filtering The Discrete Kalman Filter Applications and Additional Topics on Discrete Kalman Filtering The Continuous Kalman Filter Discrete Smoothing and Prediction Linearization and Additional Topics on Applied Kalman Filtering The Global Positioning System: A Case Study.

introduction to random signals and applied kalman filtering

PART 1: RANDOM SIGNALS BACKGROUND Chapter 1 Probability and Random Variables: A Review Chapter 2 Mathematical Description of Random Signals Chapter 3 Linear Systems Response, State-space Modeling and Monte Carlo Simulation PART 2: KALMAN FILTERING AND APPLICATIONS Chapter 4 Discrete Kalman Filter Basics Chapter 5 Intermediate Topics on Kalman Filtering Chapter 6 Smoothing and Further Intermediate Topics Chapter 7 Linearization, Nonlinear Filtering and Sampling Bayesian Filters Chapter 8 the "Go-Free" Concept, Complementary Filter and Aided Inertial Examples Chapter 9 Kalman Filter Applications to the GPS and Other Navigation Systems APPENDIX A. Laplace and Fourier Transforms APPENDIX B. The Continuous Kalman Filter

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises

Probability and Random Variables: A Review. Mathematical Description of Random Signals. Response of Linear Systems to Random Inputs. Wiener Filtering. The Discrete Kalman Filter, State-Space Modeling, and Simulation. Prediction, Applications, and More Basics on Discrete Kalman Filtering. The Continuous Kalman Filter. Smoothing. Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering. More on Modeling: Integration of Noninertial Measurements Into INS. The Global Positioning System: A Case Study. Appendices. Index.

Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions

The notion of aiding GPS code-ranging information with carrier phase measurements has been around for quite some time. The integration of these two complementary forms of data can provide highly precise position solutions corrupted by very little high-frequency error, which is advantageous particularly for high- dynamical situations. This paper presents two schemes, based on the Kalman filter, for integrating GPS code and carrier measurements efficiently. One of these schemes involves a general combination of the two types of measurements, where the vehicle motion is described as a random process. In the other, the continuous carrier phase measurements provide a dynamically exact reference trajectory in much the same manner as in an inertial system, and the pseudorange data is then used to update this reference trajectory via a Kalman filter. This method of
integration looks especially attractive as a means of achieving good dynamical response while still retaining the benefits of filtering the pseudorange data.

GPS Navigation : Combining Pseudorange with Continuous Carrier Phase Using a Kalman Filter

The high stability of the GPS signals makes it possible to determine differential position over short baselines with an accuracy of the order of centimeters. This has been demonstrated using very long baseline interferometry (VLBI) methods of radio astronomy. This paper presents an alternative approach using Kalman filter methods. It is based on an adaptive filter concept developed by D. T. Magi11 in 1965.
The scheme employs parallel Kalman filters with each filter being modeled for a different integer wavelength assumption. As the phase measurement sequence progresses, the adaptive scheme “learns” which Kalman filter corresponds to the correct hypothesis, and thus it both resolves the wavelength ambiguity and estimates differential position simultaneously. Simulated results are presented which indicate convergence in about 10 minutes of satellite observation time.

Patrick Y. C. Hwang

Papers

introduction to random signals and applied kalman filtering

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises

Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions

GPS Navigation : Combining Pseudorange with Continuous Carrier Phase Using a Kalman Filter

A Kalman Filter Approach to Precision GPS Geodesy