We present a method of dynamic coalition formation (DCF) in sensor networks to achieve well-informed sensor-target allocations. Forecasts of target movements are incorporated when choosing sensor states, as is a memory of target observation. The algorithm can be run in a centralized or decentralized configuration; the latter relies on local message passing in the form of the max-sum algorithm. We show how the DCF algorithm has been applied to synthetic and real data.
We consider how the NHS COVID-19 application will initially calculate a risk score for an individual based on their recent contact with people who report that they have coronavirus symptoms.
Most target tracking approaches either assume that the number of targets is constant throughout the time horizon of interest, or that information about target existence (birth and death) is provided by some external source. Here we show how target existence can be integrated within the tracking framework in a rigorous way. The notion of existence is not new, and has been considered before in e.g. [D. Musicki et al., (1994), (2002)]. We provide here a general probabilistic treatment that impacts as little as possible on existing tracking algorithms so that legacy tracking software (and more generally target tracking architectures) can be reused. We first show how the notion of existence can be incorporated into a single target tracking framework (retaining algorithmic invariance). To place the probabilistic recursions into context we relate this single target tracking architecture to the probabilistic data association filter. We then extend the single target results to incorporate existence for multi-target tracking and relate this to an importance sampling implementation of the joint probabilistic data association (JPDA) framework. The treatment presented is entirely general and so facilitates implementation with Kalman filters, extended/unscented Kalman filters, particle filters, etc, i.e. the approach developed is invariant to the filtering and data association mechanisms used, and therein lies the novelty. We apply the proposed framework to the difficult problem of tracking football players in video sequences, where we adopt a mixture Kalman filter implementation.
Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for "Pillar 2" swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation now-casting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo.
This paper presents a novel distributed particle filter algorithm. To solve the problem of fusing the output of multiple particle filters, a joint space over multiple realisations of the same variable is used. This approach to using particle filters to perform distributed tracking of stealthy targets requires minimal modifications to the particle filters running at the sensor nodes and does not necessitate data to be transmitted to the fusion node.
We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2) algorithm, where N is the number of particles. We overcome this problem by integrating dual tree recursions and fast multipole techniques with forward-backward smoothers, a new generalized two-filter smoother and a maximum a posteriori (MAP) smoother. Our experiments show that these improvements can substantially increase the practicality of particle smoothing.
This paper discloses a novel algorithm for efficient inference in undirected graphical models using sequential Monte Carlo (SMC) based numerical approximation techniques. The methodology developed, titled "auxiliary particle belief propagation", extends the applicability of the much celebrated (Loopy) belief propagation (LBP) algorithm to non-linear, non-Gaussian models, whilst retaining a computational cost that is linear in the number of sample points (or particles). Furthermore, we provide an additional extension to this technique by analyzing temporally evolving graphical models, a problem which remains largely unexplored in the scientific literature. The work presented is thus a general framework that can be applied to a plethora of novel distributed fusion problems. In this paper, we apply our inference algorithm to the (sequential problem of) articulated object tracking.
Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for 'Pillar 2' swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo.
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