Decision making under uncertainty with Bayesian filters

This work is concerned with exploiting Bayesian filters for decision making under uncertainty. The kind of decision making that is formally suitable for problems requiring finding optimal (non-sensing) actions as well as optimal answers/statements. Specifically, the focus will be on filters for spatial point processes which model nature as a population of indistinguishable objects. Previous works have been limited to translating the problem of point estimation into loss functions compatible with object populations. Whereas the present work systematically constructs a number of novel loss functions that give rise to a class of statistical problems beyond point estimation, which have not been appropriately formalized yet. We obtain closedform solutions to those problems (expressions computing optimal statements and corresponding minimized expected values of loss), and implement the solutions with a variety of approximate filters: the classical PHD filter, the Panjer PHD (PPHD) filter, and the Cardinalized PHD (CPHD) filter. We offer practical interpretations of the introduced problems, such as the estimation of risk value attached to an uncertain object population, and demonstrate selected implementations through numerical simulations. Overall, this work extends the variety of problems solvable using information from Bayesian filters, and reduces the amount of avoidable losses in such problems when compared to conventional approaches.James Watt Scholarship, Heriot-Watt Universit

Optimal Bernoulli point estimation with applications

This paper develops optimal procedures for point estimation with Bernoulli filters. These filters are of interest to radar and sonar surveillance because they are designed for stochastic targets that can enter and exit the surveillance region at random instances. Because of this property they are not served by the minimum mean square estimator, which is the most widely used approach to optimal point estimation. Instead of the squared error loss, this paper proposes an application-oriented loss function that is compatible with Bernoulli filters, and it develops two significant practical estimators: the minimum probability of error estimate (which is based on the rule of ideal observer), and the minimum mean operational loss estimate (which models a simple defence scenario)

A Formulation of the Adversarial Risk for Multiobject Filtering

This article is focused on estimating a quantity of interest in the context of military impact assessment that we shall call adversarial risk. We formulate the adversarial risk as a function of the multiobject state describing a group of weapons, and propose two approaches to estimate it using multiobject filters. The first, optimal, approach is tailored to filters for point processes, and produces the mean estimate of the adversarial risk and its variance. The second, naïve, approach is applicable to any filter producing point estimates of the multiobject state, yet it is not capable of equipping a risk estimate with an indicator of its quality. We develop an implementation of the optimal approach for a particular multiobject filter and compare it to the naïve approach

Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation

This paper examines the use of Poisson multi-Bernoulli mixture (PMBM) filters with realistic signal propagation models for tracking of targets with active sonar systems. In particular, the paper considers application of BELLHOP simulation to model the spatial dependence of the target probability of detection. The intention is to develop practical approaches to the problem of accurately representing sonar propagation within an advanced tracking filter

UAV-Derived Data Application for Environmental Monitoring of the Coastal Area of Lake Sevan, Armenia with a Changing Water Level

The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff’s distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling

Despite tremendous efforts, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. SARS-CoV-2 envelope is a key structural component of the virion that encapsulates viral RNA. It is composed of three structural proteins, spike, membrane (M), and envelope, which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns. These results are in good agreement with current experimental data, demonstrating a generic and versatile approach to model the structure of a virus de novo