Doppler Frequency Estimation for a Maneuvering Target Being Tracked by Passive Radar Using Particle Filter

In this paper, we estimate Doppler frequency of a maneuvering target being tracked by passive radar using two types of particle filter, the first is “Maximum Likelihood Particle Filter” (MLPF) and the second is “Minimum Variance Particle filter” (MVPF). By simulating the passive radar system that has the bistatic geometry “Digital Video Broadcasting-Terrestrial (DVB-T) transmitter / receiver” with these two types, we can estimate the Doppler frequency of the maneuvering target and compare the simulation results for deciding which type gives better performanc

Modelling and robust controller design for an underactuated self-balancing robot with uncertain parameter estimation

A comprehensive literature review of self-balancing robot (SBR) provides an insight to the strengths and limitations of the available control techniques for different applications. Most of the researchers have not included the payload and its variations in their investigations. To address this problem comprehensively, it was realized that a rigorous mathematical model of the SBR will help to design an effective control for the targeted system. A robust control for a two-wheeled SBR with unknown payload parameters is considered in these investigations. Although, its mechanical design has the advantage of additional maneuverability, however, the robot's stability is affected by changes in the rider's mass and height, which affect the robot's center of gravity (COG). Conventionally, variations in these parameters impact the performance of the controller that are designed with the assumption to operate under nominal values of the rider's mass and height. The proposed solution includes an extended Kalman filter (EKF) based sliding mode controller (SMC) with an extensive mathematical model describing the dynamics of the robot itself and the payload. The rider's mass and height are estimated using EKF and this information is used to improve the control of SBR. Significance of the proposed method is demonstrated by comparing simulation results with the conventional SMC under different scenarios as well as with other techniques in literature. The proposed method shows zero steady state error and no overshoot. Performance of the conventional SMC is improved with controller parameter estimation. Moreover, the stability issue in the reaching phase of the controller is also solved with the availability of parameter estimates. The proposed method is suitable for a wide range of indoor applications with no disturbance. This investigation provides a comprehensive comparison of available techniques to contextualize the proposed method within the scope of self-balancing robots for indoor applications

Flow Forecasting for Leakage Burst Prediction in Water Distribution Systems using Long Short-Term Memory Neural Networks and Kalman Filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset for over 2,000 DMAs in Yorkshire, containing flow time series recorded at 15-minute intervals over one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are verified as corresponding to entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filter. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakage and disruptions by addressing detected and predicted burst events. The proposed FLUIDS framework is statistically assessed and compared against state-of-practice minimum night flow (MNF) methodology. Finally, it is concluded that the framework performs well on the unobserved test dataset for both regular and leakage water flows

Flow forecasting for leakage burst prediction in water distribution systems using long short-term memory neural networks and Kalman filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset of over 2,000 DMAs in North Yorkshire, UK, containing flow time series recorded at 15-minute intervals for a period of one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are cross referenced with entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in water Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filtering. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decision-making for any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakages and disruptions. The FLUIDS framework is statistically assessed and compared against the state-of-practice minimum night flow (MNF) methodology. Based on the statistical analyses, it is concluded that the proposed framework performs well on the unobserved test dataset for both regular and leakage water flows.</p

Coordinated tracking and interception of an acoustic target using autonomous surface vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.In today's highly advanced society, more industries are beginning to turn to autonomous vehicles to reduce costs and improve safety. One industry in particular is the defense industry. By using unmanned and autonomous vehicles, the military and intelligence communities are able to complete missions without putting personnel in harm's way. A particularly important area of research is in the use of marine vehicles to autonomously and adaptively track a target of interest in situ by passive sonar only. Environmental factors play a large role in how sound propagates in the ocean, and so the vehicle must be able to adapt based on its surrounding environment to optimize acoustic track on a contact. This thesis examines the use of autonomous surface vehicles (ASVs) to not only autonomously detect and localize a contact of interest, but also to conduct follow-on long-term tracking and interception of the target, by using anticipated environmental conditions to motivate its decisions regarding optimum tracking range and speed. This thesis contributes a simulated and theoretical approach to using an ASV to maximize signal-to-noise ratio (SNR) while tracking a contact autonomously. Additionally, this thesis demonstrates a theoretical approach to using information from a collaborating autonomous vehicle to assist in autonomously intercepting a target.The research completed in this thesis was funded by the U.S. Navy’s Civilian Institution Program with MIT/WHOI Joint Program

Aplicación de Matlab/Simulink al posicionamiento y control de drones en interiores

El objetivo principal de este trabajo es el desarrollo de un sistema de posicionamiento en interiores de drones para el seguimiento de trayectorias. El sistema diseñado utiliza la información captada por una cámara localizada en la parte inferior del dron y se encarga de detectar e identificar marcas fijas, con posición conocida, situadas en el suelo. Se calcula la posición absoluta del dron a partir de su posición relativa con respecto a la absoluta de la marca. Para el diseño, simulación e implementación se han utilizado las herramientas proporcionadas por Matlab/Simulink, realizando ensayos reales con un minidrón Parrot Mambo.The main purpose of this project is to develop a drone positioning system for trajectory tracking applications oriented to work in indoor environments. The positioning system uses the information from the onboard downfaced camera that detects and identifies fixed marks located on the ground and whose position is known. The positioning system calculates the absolute position of the drone from the relative position of the drone with respect to absolute position of the mark. For the demonstration of the proposal, Parrot Mambo minidrone is used. Matlab/Simulink tools and software are used for the design, implementation and simulation.Máster Universitario en Ingeniería Industrial (M141

Control system design using fuzzy gain scheduling of PD with Kalman filter for railway automatic train operation

The development of train control systems has progressed towards following the rapid growth of railway transport demands. To further increase the capacity of railway systems, Automatic Train Operation (ATO) systems have been widely adopted in metros and gradually applied to mainline railways to replace drivers in controlling the movement of trains with optimised running trajectories for punctuality and energy saving. Many controller design methods have been studied and applied in ATO systems. However, most researchers paid less attention to measurement noise in the development of ATO control system, whereas such noise indeed exists in every single instrumentation device and disturbs the process output of ATO. Thus, this thesis attempts to address such issues. In order to overcome measurement error, the author develops Fuzzy gain scheduling of PD (proportional and derivative) control assisted by a Kalman filter that is able to maintain the train speed within the specified trajectory and stability criteria in normal and noisy conditions due to measurement noise. Docklands Light Railway (DLR) in London is selected as a case study to implement the proposed idea. The MRes project work is summarised as follows: (1) analysing literature review, (2) modelling the train dynamics mathematically, (3) designing PD controller and Fuzzy gain scheduling, (4) adding a Gaussian white noise as measurement error, (5) implementing a Kalman filter to improve the controllers, (6) examining the entire system in an artificial trajectory and a real case study, i.e. the DLR, and (7) evaluating all based on strict objectives, i.e. a ±3% allowable error limit, a punctuality limit of no later and no earlier than 30 seconds, Integrated Absolute Error (IAE) and Integrated Squared Error (ISE) performances. The results show that Fuzzy gain scheduling of PD control can cope well with the examinations in normal situations. However, such discovery is not found in noisy conditions. Nevertheless, after the introduction to Kalman filter, all control objectives are then satisfied in not only normal but also noisy conditions. The case study implemented using DLR data including on the route from Stratford International to Woolwich Arsenal indicates a satisfactory performance of the designed controller for ATO systems