Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Information Fusion Identification Method for the Multidimension ARMA Signal with Sensor Bias and Common Disturbance Noise

AbstractFor the multisensor multi-dimension autoregressive moving average(ARMA) signal system with a common disturbance measurement noise and sensor bias, when the model parameters, sensor bias and noise variances are all unknown, their consistent estimates are obtained by the multistage information fusion identification method. Firstly, by multi-dimension recursive extended least squares (RELS) algorithm, the estimates of the autoregressive parameters and sensor bias are obtained. Secondly, applying the correlation method, the estimates of the measurement noise variances are obtained. Finally, the fused estimates of the moving average(MA) parameters and the process noise variances are obtained by the Gevers-Wouters algorithm with a dead band. A simulation example verifies the consistency of unknown parameters estimates

Data-driven time-frequency analysis of multivariate data

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis

Distributed Kalman Filtering Using Weighted Averaging

This paper addresses the problem of distributed Kalman filtering, with focus on limiting the required communication bandwidth. By distributed we refer to a scenario when all nodes in the network desire an estimate of the full state of the observed system and there is no centralized computation center. Communication only takes place between neighbors and only a fixed number of times each sample. To reduce bandwidth requirements of individual nodes, estimates instead of measurements are communicated. A new estimate is then formed as a weighted average of the neighbouring estimates. The weights are optimized to yield a small estimation error covariance in stationarity. The minimization can be done off line thus allowing only estimates to be communicated. The advantage of communicating estimates instead of measurements becomes more evident when the number of nodes exceeds the size of the state vector to be estimated. The algorithm is applied to one simple second order system and temperature sensing network

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals

Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement