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This paper explores application of the Probability Hypothesis Density (PHD) filter to the estimation of a threat level pertaining to an object population. Specifically, it develops explicit and compact expression for computation of its variance, a second-order statistical moment that quantifies the dispersion of the threat level around its mean value. The behaviour of the statistic is demonstrated...
This work analyzes the data output of laser ranging data collected from the SGF (Herstmonceux, UK), and proposes a bespoke likelihood function for its processing in the context of Bayesian filtering. It is then illustrated in a single-target Bayesian filter, performing successfully on simulated and real data, under a variety of noise profiles encountered in typical outputs of the sensor.
The necessity for maintaining surveillance in airborne environments is ever growing. Criminals and terrorists are finding new and elaborate means of attack, and small UAVs such as quadcopters and hexacopters could be a possible threat. Their small size and agile movement will make them difficult to detect. This work aims to determine whether or not these small UAVs can be detected at short range using...
We consider geographically distributed sensor platforms with limited field of views (FoVs) networked together in order to cover a larger surveillance region. Each sensor has a partially overlapping FoV with its neighbours, and, collects both target originated and spurious measurements. We are interested in estimating the locations of the sensors in a network coordinate system using only these measurements...
Motivated by object tracking applications with networked sensors, we consider multi sensor state space models. Estimation of latent parameters in these models requires centralisation because the parameter likelihood depend on the measurement histories of all of the sensors. Consequently, joint processing of multiple histories pose difficulties in scaling with the number of sensors. We propose an approximation...
In this work, we consider the front-end processing for an active sensor. We are interested in estimating signal amplitude and noise power based on the outputs from filters that match transmitted waveforms at different ranges and bearing angles. These parameters identify the distributions in, for example, likelihood ratio tests used by detection algorithms and characterise the probability of detection...
For many disciplines in natural sciences like biology, chemistry or medicine, the invention of optical microscopy in the late 1800's provided groundbreaking insight into biomedical mechanisms that were not observable before with the unaided eye. However, the diffraction limit of the microscope gives a natural constraint on the image resolution since objects which are smaller than half the wavelength...
The SC-PHD filter is an algorithm which was designed to solve a class of multiple object estimation problems where it is necessary to estimate the state of a single-target parent process, in addition to estimating the state of a multi-object population which is conditioned on it. The filtering process usually employs a number of particles to represent the parent process, coupled each with a conditional...
In this work, we consider a network of bearing only sensors in a surveillance scenario. The processing of target measurements follow a two-tier architecture: The first tier is composed of centralised processing clusters whereas in the second tier, cluster heads perform decentralised processing. We are interested in the first tier problem of locating peripheral sensors relative to their cluster head...
We consider geographically dispersed and networked sensors collecting measurements from multiple targets in a surveillance region. Each sensor node filters the set of cluttered, noisy target measurements it collects in a sensor centric coordinate system and with imperfect detection rates. The filtered multi-target information is, then, communicated to the nearest neighbours. We are interested in network...
Photoactivated Localization Microscopy (PALM) is a technique which allows the localization of particles smaller than the resolution of the microscope and can be used to analyze intracellular particle motion. Images acquired with this technique, however, are noisy, which complicates particle detection, and tracking the particles is complicated due to the presence of multiple objects at any given time...
The paper formulates the problem of sequential Bayesian estimation of a compound state consisting of a multi-object dynamic state and a multi-sensor bias. The compound state is modelled by a doubly stochastic point process, where the multi-object bias is a parent, whereas the multi-object state is the offspring point process. The prediction and the update steps for the first-order moment of the posterior...
In extended target tracking, targets potentially produce more than one measurement per time step. In recent random finite set (RFS) approaches, the set of measurements obtained from an extended target is modelled as a point process. In this paper, we expand on the RFS approach to extended target tracking by considering a hierarchical point process representation of multiple extended target, more specifically...
This paper introduces a general chain rule (GCR) for Gâteaux differentials/Gâteaux derivatives, and describes its consequences for multitarget detection and tracking. After describing the GCR and its specific form for functionals and functional derivatives, we use it to derive two new PHD filters: (1) a PHD filter for general models of target-generated measurements with general clutter processes;...
In many group target tracking scenarios, a collection of targets move in a correlated manner as part of a formation, such as a convoy of vehicles. The focus of this paper is in the estimation of the evolution over time of a single-group of targets, referred to as a single-cluster, based on a sequence of partial observation sets. Based on Finite Set Statistics, the paper presents a first-moment recursion...
Performance evaluation of multi-target tracking algorithms is of great practical importance in the design and comparison of tracking systems. Recently a consistent metric for performance evaluation of multi-object filters (referred to as OSPA metric) has been proposed. In this paper we describe how the OSPA metric can be adapted to evaluate the performance of multi-target tracking algorithms. The...
Distribution and decentralisation of fusion operations are key to network centric operations (NCOs) and distributed data fusion algorithms (DDF) have been developed to support them. These algorithms fuse data collected locally with state estimates propagated from other nodes. If the full advantages of NCOs are to be realised, these algorithms should exploit local information only: no single node,...
We consider the problem of distributed target tracking in a multi-object, multi-sensor scenario in which the structure of the joint distribution of the estimate between different nodes is unknown. In this paper we present a preliminary implementation of Generalised Covariance Intersection (GCI) fusion rule for multi-object posteriors through a Monte Carlo realisation. We discuss the subtleties in...
The Probability Hypothesis Density (PHD) filter is a multiple-target filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operated in environments with false alarms and missed detections. Two distinct algorithms implementations of this technique have been developed. The first of which, called the Particle PHD filter, requires...
We address the problem of identifying individual sinusoidal tracks from audio signals using multi-object stochastic filtering techniques. Attractive properties for audio analysis include that it is conceptually straightforward to distinguish between measurements that are generated by actual targets and those which are false alarms. Moreover, we can estimate target states when observations are missing...
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