Traditional trackingalgorithms for video use object extraction to generate point measurements on targets distributed over several pixels. A probabilistic multi-hypothesis tracker based algorithm is demonstrated that ...
Traditional trackingalgorithms for video use object extraction to generate point measurements on targets distributed over several pixels. A probabilistic multi-hypothesis tracker based algorithm is demonstrated that simply uses thresholded images and performs better than the extraction based approach.
Naval Weapon Closed Loop tracking systems require adequate sensor availability, suitable algorithms to provide d appropriate system response and a modelling capability that allows evaluation of candidate designs and a...
Naval Weapon Closed Loop tracking systems require adequate sensor availability, suitable algorithms to provide d appropriate system response and a modelling capability that allows evaluation of candidate designs and algorithmic techniques under representative conditions. This paper outlines some of the issues surrounding these capabilities and provides an overview of a system level modelling capability that allows the investigation of the performance of closed loop tracking systems for naval applications.
This paper presents a particle filtering algorithm for multiple object tracking. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which han...
This paper presents a particle filtering algorithm for multiple object tracking. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin.
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 ap...
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.
A multiple hypothesis track splitting filter has been developed for maintaining track on a target as it sheds extraneous objects. The filter includes full modelling of merged measurements from closely spaced objects a...
A multiple hypothesis track splitting filter has been developed for maintaining track on a target as it sheds extraneous objects. The filter includes full modelling of merged measurements from closely spaced objects and the transition to resolution of individual objects. This method is based on Kalman filtering of measurement streams (or tracks) defined by frame-to- frame associations which are assumed to be known.
This paper describes a self organising spatio-temporal radar CFAR system that uses multiple intelligent software agents to detect and adapt the processing to features in the environment. By combining both temporal and...
This paper describes a self organising spatio-temporal radar CFAR system that uses multiple intelligent software agents to detect and adapt the processing to features in the environment. By combining both temporal and spatial data gathering sufficient samples can be collected to allow both the first and second order moments of the clutter distribution to be approximated for each cell. By gathering higher order statistics to a useful accuracy, more stable thresholds may be produced.
This paper investigates the use of preview control for optimal trajectory tracking for air-to-surface missiles. An off-line reference trajectory is obtained by solving a trajectory optimisation problem that incorporat...
This paper investigates the use of preview control for optimal trajectory tracking for air-to-surface missiles. An off-line reference trajectory is obtained by solving a trajectory optimisation problem that incorporates the mission constraints. A trajectory following guidance scheme using a preview controller is used to generate the on-line control. An example of a terminal guidance trajectory with a bunt (climb and dive) manoeuvre and a look angle constraint is presented to demonstrate the method.
An approach that is common in the machine learning literature, known as active sensing, is applied to provide a method for managing agile sensors in a dynamic environment. We adopt an active sensing approach to schedu...
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An approach that is common in the machine learning literature, known as active sensing, is applied to provide a method for managing agile sensors in a dynamic environment. We adopt an active sensing approach to scheduling sensors for multiple targettrackingapplications that combines particle filtering, predictive density estimation, and relative entropy maximization. Specifically, the goal of the system is to learn the number and states of a group of moving targets occupying a surveillance region. At each time step, the system computes a sensing action to take, based on an entropy measure called the Renyi divergence. After the measurement is made, the system updates its probability density on the number and states of the targets. This procedure repeats at each time where a sensor is available for use. The algorithms developed here extend standard active sensing methodology to dynamically evolving objects and continuous state spaces of high dimension. It is shown using simulated measurements on real recorded target trajectories that this method of sensor management yields more than a ten fold gain in sensor efficiency when compared to periodic scanning. (C) 2004 Elsevier B.V. All rights reserved.
Clearly, the problem of deriving accurate algorithms for tracking of the dynamics of various kinds of targets has received considerable interest. This problem is central in many applications, such as radar and sonar. ...
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ISBN:
(纸本)078038881X
Clearly, the problem of deriving accurate algorithms for tracking of the dynamics of various kinds of targets has received considerable interest. This problem is central in many applications, such as radar and sonar. A large number of methods based on Kalman filter theory have been proposed. Design of a tracker based on a Kalman filter typically involves a trade-off between tracking performance and noise sensitivity. In particular, the Kalman filter depends on certain second-order statistics, namely the measurement noise variance and the variance of the (presumably random) target acceleration. Since these latter quantities can not be expected to be a priori known, a recursive least squares type algorithm which provides estimates thereof is suggested here. This method utilizes intermediate results obtained in the Kalman filter and hence, is evaluated in parallel. The usefulness of the proposed method is demonstrated by means of application to an extended Kalman filter, EKF. The considered EKF is based on a three-state filter model for tracking of the position and velocity of a moving target as well as estimation of possible nonlinearities in the measurements of the target position. Next, as an interesting alternative to the EKF, a recursive prediction error method, RPEM is proposed. As opposed to the extended Kalman filter, EKF, the suggested RPEM algorithm does not require knowledge of, or estimation of the statistics of the noise and the dynamics of the target motion. Instead, the proposed RPEM adjusts to changing target dynamics by means of on-line adjustment of a forgetting factor, which is calculated from filtered values of the prediction error. Hence, the resulting algorithm is less complex than the EKF. In addition, it is shown here how the EKF is related to the RPEM by means of specific choices of the time-varying estimates of the measurement noise variance and the covariance matrix of the system time variations. It is demonstrated by means of a numerical exa
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