Optimal Control under Quantised Measurements - A Particle Filter and Reduced Horizon Approach

This thesis covers the optimal control of stochastic systems with coarsely quantised measurements. A particle filter approach is used both for the estimation and control problem. Three main families of particle filters are examined for state estimation, standard SIR filters, SIR filters with generalised sampling and auxiliary filters. A couple of different proposal distributions and weight functions were examined for the generalised SIR and auxiliary filter respectively. The choice of proposal distribution had the greatest impact on performance but the unrivalled best filter was achieved with a combination of generalised sampling and the auxiliary particle filter. For the problem of control the particle filter was used for cost-to-go evaluation by forward simulation in time. Simplifications of the full dynamic programming problem were done by reducing the time horizon resulting in M-measurement feedback policies and a new M-measurement cost feedback policy. One-measurement feedback and M-measurement cost feedback was examined for M 4 and although probing behaviour was observed none of the examined controllers managed to outperform a certainty equivalent controller

On particle filters in radar target tracking

The dissertation focused on the research, implementation, and evaluation of particle filters for radar target track filtering of a maneuvering target, through quantitative simulations and analysis thereof. Target track filtering, also called target track smoothing, aims to minimize the error between a radar target's predicted and actual position. From the literature it had been suggested that particle filters were more suitable for filtering in non-linear/non-Gaussian systems. Furthermore, it had been determined that particle filters were a relatively newer field of research relating to radar target track filtering for non-linear, non-Gaussian maneuvering target tracking problems, compared to the more traditional and widely known and implemented approaches and techniques. The objectives of the research project had been achieved through the development of a software radar target tracking filter simulator, which implemented a sequential importance re-sampling particle filter algorithm and suitable target and noise models. This particular particle filter had been identified from a review of the theory of particle filters. The theory of the more conventional tracking filters used in radar applications had also been reviewed and discussed. The performance of the sequential importance re-sampling particle filter for radar target track filtering had been evaluated through quantitative simulations and analysis thereof, using predefined metrics identified from the literature. These metrics had been the root mean squared error metric for accuracy, and the normalized processing time metric for computational complexity. It had been shown that the sequential importance re-sampling particle filter achieved improved accuracy performance in the track filtering of a maneuvering radar target in a non-Gaussian (Laplacian) noise environment, compared to a Gaussian noise environment. It had also been shown that the accuracy performance of the sequential importance re-sampling particle filter is a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The sequential importance re-sampling particle filter had also been compared to two conventional tracking filters, namely the alpha-beta filter and the Singer-Kalman filter, and had better accuracy performance in both cases. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be higher than that of both the alpha-beta filter and the Singer-Kalman filter. Analysis of the posterior Cramér-Rao lower bound of the sequential importance re-sampling particle filter had also been conducted and presented in the dissertation

Design, Evaluation, and Particle Size Characterization of an In-Duct Flat Media Particle Loading Test System for Nuclear-Grade Asme Ag-1 Hepa Filters

The design and performance evaluation of in-duct, isokinetic samplers capable of testing flat sheet, nuclear-grade High Efficiency Particulate Air (HEPA) filters simultaneously with a radial filter testing system is discussed in this study. Evaluations within this study utilize challenge aerosols of varying particle diameters and masses such as hydrated alumina, Arizona test dust, and flame-generated acetylene soot. Accumulated mass and pressure drop for each in-duct sampler is correlated to the full-scale radial filter accumulated mass from initial to 10 in w. c. of loading. SEM imaging of samples at 25%, 50%, 75% and 100% loading verifies particle sizes with instrumentation used, revealing filter clogging resulting from particle impaction and interception. The U.S Department of Energy requires prototype nuclear-grade HEPA filters to be qualified under ASME AG-1 standards. The data obtained can be used to determine baseline performance characteristics on pleated radial filter medium for increased loading integrity and lifecycle endurance

Computer Vision Tracking Using Particle Filters for 3D Position Estimation

This line of research seeks to increase knowledge of a tracked target using the particle filter, also known as Sequential Monte Carlo (SMC) methods. The target is tracked using vision based observations. These observations were simulated using both dual cameras and a single camera. If only a single camera tracks the target, depth cannot be determined directly and is considered an unobservable state. Filters can estimate this unobservable state using a dynamic model and data from the image. However the movement of the target is nonlinear which eliminated filters traditionally used to track motion such as the Kalman filter and its variants. The particle filter is an alternative that can track nonlinear motion, but was not feasible until recently due to its computational requirements. Simulations of nonlinear target movement, first in two dimensions, then three, evaluated the particle filter\u27s feasibility and performance. Subsequent simulations evaluated the particle filter\u27s ability to track a target using dual and single camera observations. Evaluation tests were devised to characterize the performance of each filter. Analysis metrics were produced to analyze the results of these tests. Linear and Kalman filters were also devised to serve as additional comparisons to the particle filter. Results for dual camera observations demonstrated the filter could track the target and determine unobservable states, however results for the single camera observations indicated the filter was problematic since it could not return accurate depth estimates and suffered from severe weight collapse

Assessment Methodology Of Backwash In Pressurized Sand Filters

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The objective of this study was to apply a methodology based on sampling sand from contaminated filter beds, to evaluate the performance of the backwash process in pressurized sand filters used in irrigation. Backwash trials were conducted in an experimental module with three new sand filters of the same commercial model. The evaluations were conducted after filtration processes performed using four filtration rates and three separate sand particle sizes (fine - 0.55 mm; medium - 0.77 mm, and coarse sand - 1.04 mm diameter), repeated in three subsequent cycles. After reaching a fixed expansion of 25% of the filter bed height, the backwash processes were performed for 15 min. Backwash cleaning efficiency was evaluated in all trials based on the mass of removed solids for different filter bed layers and throughout the entire filter bed. The backwash assessment methodology is effective and has potential to be a practical tool for farmers in the evaluation of the performance of pressurized sand filters used in irrigation.207600605Sao Paulo Research Support Foundation - FAPESPFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited – such as underwater, or in hostile regions – this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m

Physics-based prognostic modelling of filter clogging phenomena

In industry, contaminant filtration is a common process to achieve a desired level of purification, since contaminants in liquids such as fuel may lead to performance drop and rapid wear propagation. Generally, clogging of filter phenomena is the primary failure mode leading to the replacement or cleansing of filter. Cascading failures and weak performance of the system are the unfortunate outcomes due to a clogged filter. Even though filtration and clogging phenomena and their effects of several observable parameters have been studied for quite some time in the literature, progression of clogging and its use for prognostics purposes have not been addressed yet. In this work, a physics based clogging progression model is presented. The proposed model that bases on a well-known pressure drop equation is able to model three phases of the clogging phenomena, last of which has not been modelled in the literature yet. In addition, the presented model is integrated with particle filters to predict the future clogging levels and to estimate the remaining useful life of fuel filters. The presented model has been implemented on the data collected from an experimental rig in the lab environment. In the rig, pressure drop across the filter, flow rate, and filter mesh images are recorded throughout the accelerated degradation experiments. The presented physics based model has been applied to the data obtained from the rig. The remaining useful lives of the filters used in the experimental rig have been reported in the paper. The results show that the presented methodology provides significantly accurate and precise prognostic results