Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods

This paper proposes an efficient method for the simultaneous estimation of the state of a quantum system and the classical parameters that govern its evolution. This hybrid approach benefits from efficient numerical methods for the integration of stochastic master equations for the quantum system, and efficient parameter estimation methods from classical signal processing. The classical techniques use sequential Monte Carlo (SMC) methods, which aim to optimize the selection of points within the parameter space, conditioned by the measurement data obtained. We illustrate these methods using a specific example, an SMC sampler applied to a nonlinear system, the Duffing oscillator, where the evolution of the quantum state of the oscillator and three Hamiltonian parameters are estimated simultaneously

Parallel Approaches to Accelerate Bayesian Decision Trees

Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Existing work on Bayesian decision trees uses MCMC. Unfortunately, this can be slow, especially when considering large volumes of data. It is hard to parallelise the accept-reject component of the MCMC. None-the-less, we propose two methods for exploiting parallelism in the MCMC: in the first, we replace the MCMC with another numerical Bayesian approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing property that it is an inherently parallel algorithm; in the second, we consider data partitioning. Both methods use multi-core processing with a HighPerformance Computing (HPC) resource. We test the two methods in various study settings to determine which method is the most beneficial for each test case. Experiments show that data partitioning has limited utility in the settings we consider and that the use of the SMC sampler can improve run-time (compared to the sequential implementation) by up to a factor of 343

Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound

Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-uniform mediums, is challenging at best to select. In this letter, we propose a Bayesian approach to marginalize the influence of the SoS on beamformers for imaging. We adapt Bayesian direction-of-arrival estimation to an imaging setting and integrate a popular minimum variance beamformer over the posterior of the SoS. To solve the Bayesian integral efficiently, we use numerical Gauss quadrature. We apply our beamforming approach to shallow water sonar imaging where multipath and nonlinear propagation is abundant. We compare against the minimum variance distortionless response (MVDR) beamformer and demonstrate that its Bayesian counterpart achieves improved range and azimuthal resolution while effectively suppressing multipath artifacts

Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation

This paper is concerned with sensor management for target search and track using the generalised optimal subpattern assignment (GOSPA) metric. Utilising the GOSPA metric to predict future system performance is computationally challenging, because of the need to account for uncertainties within the scenario, notably the number of targets, the locations of targets, and the measurements generated by the targets subsequent to performing sensing actions. In this paper, efficient sample-based techniques are developed to calculate the predicted mean square GOSPA metric. These techniques allow for missed detections and false alarms, and thereby enable the metric to be exploited in scenarios more complex than those previously considered. Furthermore, the GOSPA methodology is extended to perform non-myopic (i.e. multi-step) sensor management via the development of a Bellman-type recursion that optimises a conditional GOSPA-based metric. Simulations for scenarios with missed detections, false alarms, and planning horizons of up to three time steps demonstrate the approach, in particular showing that optimal plans align with an intuitive understanding of how taking into account the opportunity to make future observations should influence the current action. It is concluded that the GOSPA-based, non-myopic search and track algorithm offers a powerful mechanism for sensor management.Comment: The paper has been submitted for publication in IEEE Transactions on Aerospace and Electronic Systems and is currently in revie

Vegetation database of Great Britain: Countryside Survey

This paper describes the vegetation database created as part of the Countryside Survey (CS) of Great Britain (GIVD ID EU-GB-003) which was established to monitor ecological and land use change in 1978 (http://www.countrysidesurvey.org.uk). The sample design is based on a series of stratified, randomly selected 1 km squares, which numbered 256 in the 1978 survey, 500 in the 1990 survey, 569 in the 1998 survey and 591 in the 2007 survey. Stratification of sample squares was based on predefined strata (called land classes) which have been derived from a classification of all 1 km squares in Britain based on their topographic, climatic and geological attributes obtained from published maps. A series of vegetation plots were located within each 1 km square using a restricted randomisation procedure designed to reduce aggregation. Linear features (road verges, watercourse banks, hedges, arable margins and field boundaries) and areal features (fields, unenclosed land and small semi-natural biotope patches) were sampled. Linear plots were 1 x 10 m laid out along a feature whilst unenclosed land and small biotopes were sampled using 2 m x 2 m plots. Larger randomly-placed plots were nested 14 m² plots with an inner nest of 2 m x 2 m. Within each 1 km Countryside Survey sample square the land cover and all landscape features were mapped and each parcel of land (and vegetation plot) has been assigned to a Broad Habitat/EUNIS habitat type. This database of vegetation plots is a very useful resource. The data is freely available from the website, however, there are restrictions on the release of the spatial location of the plots. There is now a considerable time-series of plots within the database going back to 1978 representing different habitat types and landscape features that can be analysed to determine changes in vegetation metrics (e.g. Ellenberg scores) and individual species. Vegetation changes can be linked to environmental drivers and the spatial scale (across GB) is sufficiently large to analyse gradients in most driving variables