Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions

Multiple Target, Multiple Type Filtering in the RFS Framework

A Multiple Target, Multiple Type Filtering (MTMTF) algorithm is developed using Random Finite Set (RFS) theory. First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple types of targets, each with distinct detection properties, to develop a multiple target, multiple type filtering, N-type PHD filter, where $N\geq2$, for handling confusions among target types. In this approach, we assume that there will be confusions between detections, i.e. clutter arises not just from background false positives, but also from target confusions. Then, under the assumptions of Gaussianity and linearity, we extend the Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter. Furthermore, we analyze the results from simulations to track sixteen targets of four different types using a four-type (quad) GM-PHD filter as a typical example and compare it with four independent GM-PHD filters using the Optimal Subpattern Assignment (OSPA) metric. This shows the improved performance of our strategy that accounts for target confusions by efficiently discriminating them

Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach

Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be "compressed" to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size. Combined with off-the-shelf compression algorithms, the bound leads to state of the art generalization guarantees; in particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. As additional evidence connecting compression and generalization, we show that compressibility of models that tend to overfit is limited: We establish an absolute limit on expected compressibility as a function of expected generalization error, where the expectations are over the random choice of training examples. The bounds are complemented by empirical results that show an increase in overfitting implies an increase in the number of bits required to describe a trained network.Comment: 16 pages, 1 figure. Accepted at ICLR 201

Space-based relative multitarget tracking

Access to space has expanded dramatically over the past decade. The growing popularity of small satellites, specifically cubesats, and the following launch initiatives have resulted in exponentially growing launch numbers into low Earth orbit. This growing congestion in space has punctuated the need for local space monitoring and autonomous satellite inspection. This work describes the development of a framework for monitoring local space and tracking multiple objects concurrently in a satellite\u27s neighborhood. The development of this multitarget tracking systems has produced collateral developments in numerical methods, relative orbital mechanics, and initial relative orbit determination. This work belongs to a class of navigation known as angles-only navigation, in which angles representing the direction to the target are measured but no range measurements are available. A key difference between this work and traditional angles-only relative navigation research is that angle measurements are collected from two separate cameras simultaneously. Such measurements, when coupled with the known location and orientation of the stereo cameras, can be used to resolve the relative range component of a target\u27s position. This fact is exploited to form initial statistical representations of the targets\u27 relative states, which are subsequently refined in Bayesian single-target and multitarget frameworks --Abstract, page iii

A Bayesian Filtering Algorithm for Gaussian Mixture Models

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper

Multiple Space Object Tracking Using A Randomized Hypothesis Generation Technique

In order to protect assets and operations in space, it is critical to collect and maintain accurate information regarding Resident Space Objects (RSOs). This collection of information is typically known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information regarding the RSOs in the form of observations or measurement returns. However, the distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a measurement belongs to. These ambiguities are known as data association ambiguities. Coupled with uncertainty in RSO state and the vast number of objects in space, data association ambiguities can cause the multiple space object-tracking problem to become computationally intractable. Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with state space containing both continuous and discrete random variables. Using a Finite Set Statistics (FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object tracking recursions. These equations, known as the FISST multiple object tracking equations, are computationally intractable when solved in full. This computational intractability provokes the idea of the newly developed alternative hypothesis dependent derivation of the FISST equations. This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate approximation of the full FISST recursions while keeping the problem tractable. There are many other benefits to this new derivation. For example, it can be used to connect and compare the classical tracking methods to the modern FISST based approaches. This connection clearly defines the relationships between different approaches and shows that they result in the same formulation for scenarios with a fixed number of objects and are very similar in cases with a varying number of objects. Findings also show that the R-FISST technique is compatible with many powerful optimization tools and can be scaled to solve problems such as collisional cascading

Towards Probabilistic and Partially-Supervised Structural Health Monitoring

One of the most significant challenges for signal processing in data-based structural health monitoring (SHM) is a lack of comprehensive data; in particular, recording labels to describe what each of the measured signals represent. For example, consider an offshore wind-turbine, monitored by an SHM strategy. It is infeasible to artificially damage such a high-value asset to collect signals that might relate to the damaged structure in situ; additionally, signals that correspond to abnormal wave-loading, or unusually low-temperatures, could take several years to be recorded. Regular inspections of the turbine in operation, to describe (and label) what measured data represent, would also prove impracticable -- conventionally, it is only possible to check various components (such as the turbine blades) following manual inspection; this involves travelling to a remote, offshore location, which is a high-cost procedure. Therefore, the collection of labelled data is generally limited by some expense incurred when investigating the signals; this might include direct costs, or loss of income due to down-time. Conventionally, incomplete label information forces a dependence on unsupervised machine learning, limiting SHM strategies to damage (i.e. novelty) detection. However, while comprehensive and fully labelled data can be rare, it is often possible to provide labels for a limited subset of data, given a label budget. In this scenario, partially-supervised machine learning should become relevant. The associated algorithms offer an alternative approach to monitor measured data, as they can utilise both labelled and unlabelled signals, within a unifying training scheme. In consequence, this work introduces (and adapts) partially-supervised algorithms for SHM; specifically, semi-supervised and active learning methods. Through applications to experimental data, semi-supervised learning is shown to utilise information in the unlabelled signals, alongside a limited set of labelled data, to further update a predictive-model. On the other hand, active learning improves the predictive performance by querying specific signals to investigate, which are assumed the most informative. Both discriminative and generative methods are investigated, leading towards a novel, probabilistic framework, to classify, investigate, and label signals for online SHM. The findings indicate that, through partially-supervised learning, the cost associated with labelling data can be managed, as the information in a selected subset of labelled signals can be combined with larger sets of unlabelled data -- increasing the potential scope and predictive performance for data-driven SHM