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 adaptive beam scheduling algorithm based on covariance control strategy is proposed for an agile beam radar in multi-targettrackingapplications. The algorithm uses IMMKFs (Interacting Multiple Model Kalman Filter...
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ISBN:
(纸本)9780780395824
An adaptive beam scheduling algorithm based on covariance control strategy is proposed for an agile beam radar in multi-targettrackingapplications. The algorithm uses IMMKFs (Interacting Multiple Model Kalman Filters) to track maneuvering targets. The optimal beam scheduling model is built which can schedule radar beam based on the difference between the desired covariance matrix and that of the actual covariance of each target for maintaining the target's state estimate covariance near a desired level. Two kinds of beam scheduling algorithms, the minimum mean bias criterion and the minimax bias criterion, are proposed, respectively. Simulation results show that the algorithms based on covariance control can quickly achieve the desired tracking state and allocate sensor resources effectively.
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
Low cost and short range detection/localisation devices arouse a growing interest for civil and military applications such as automotive anti-collision radars or target detection devices used in active shielding. This...
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The proceedings contain 19 papers form the conference on ieetargettracking 2004: algorithms and applications. The topics discussed include: tracking with persistent, target-related, spurious measurements;practical f...
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The proceedings contain 19 papers form the conference on ieetargettracking 2004: algorithms and applications. The topics discussed include: tracking with persistent, target-related, spurious measurements;practical fusion of quantized measurements via particle filtering;an efficiency particle filter for jump markov nonlinear systems;tracking the manoeuvering targets using multiple scan joint probabilistic data association algorithm;an automatic method for eliminating spurious information from sensor networks;data fusion for several kalman filters tracking a single target;tracking seismic wave;and development of a generic multi-sensor tracking system for agile radars.
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