摘要 :
When tracking maneuvering targets with conventional algorithms, the process noise standard deviation used in the nearly constant velocity Kalman filter is selected vaguely in relation to the maximum acceleration of the target. The...
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When tracking maneuvering targets with conventional algorithms, the process noise standard deviation used in the nearly constant velocity Kalman filter is selected vaguely in relation to the maximum acceleration of the target. The deterministic tracking index is introduced and used to develop a relationship between the maximum acceleration and the process noise variance that either minimizes the maximum mean squared error (MMSE) in position or weighted sum of the noise variance plus maneuver bias. For each case, the process noise standard deviation is expressed in terms of the maximum acceleration and deterministic tracking index for both piecewise constant and discretized continuous acceleration error models. A lower bound on the process noise variance is also expressed in terms of the maximum acceleration and deterministic tracking index. With the use of Monte Carlo simulations, the method for choosing process noise variance for tracking maneuvering targets is demonstrated.
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摘要 :
A novel algorithm for multi-target track initiation in dense clutter environments is proposed based on approximating local maxima in the observation likelihood function. The algorithm implements a tree structure to search for loca...
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A novel algorithm for multi-target track initiation in dense clutter environments is proposed based on approximating local maxima in the observation likelihood function. The algorithm implements a tree structure to search for local maxima of the observation likelihood function by dividing the entire surveillance area into large subsets and narrowing the search inside each subset in which there is a high likelihood that a target is present. A rough Gaussian approximation technique is proposed to reduce complexity in calculating the observation likelihood function over a subset by avoiding integration. The proposed algorithm has been tested on a multi-target benchmark dataset and shows superior performance in terms of high target detection probability, low probability of false alarm, and low computational complexity.
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摘要 :
A novel algorithm for multi-target track initiation in dense clutter environments is proposed based on approximating local maxima in the observation likelihood function. The algorithm implements a tree structure to search for loca...
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A novel algorithm for multi-target track initiation in dense clutter environments is proposed based on approximating local maxima in the observation likelihood function. The algorithm implements a tree structure to search for local maxima of the observation likelihood function by dividing the entire surveillance area into large subsets and narrowing the search inside each subset in which there is a high likelihood that a target is present. A rough Gaussian approximation technique is proposed to reduce complexity in calculating the observation likelihood function over a subset by avoiding integration. The proposed algorithm has been tested on a multi-target benchmark dataset and shows superior performance in terms of high target detection probability, low probability of false alarm, and low computational complexity.
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Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targe...
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Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.
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摘要 :
Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targe...
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Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.
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In this paper we propose a method for selecting an appropriate subset of sensors with a view to minimize estimation error while tracking a target with sensors spread across in a 2-dimensional plane. In particular, we address the p...
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In this paper we propose a method for selecting an appropriate subset of sensors with a view to minimize estimation error while tracking a target with sensors spread across in a 2-dimensional plane. In particular, we address the problem of given N sensors, select n < N sensors to improve the given estimate of target location". Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes the bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the sensors be selected based on three measures viz. (a) collinearity, (b) spread and (c) proximity to the target. We use least square error estimation technique to compute the target location using distance measurements subject to multiplicative errors from multiple sensors.
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摘要 :
In this paper we propose a method for selecting an appropriate subset of sensors with a view to minimize estimation error while tracking a target with sensors spread across in a 2-dimensional plane. In particular, we address the p...
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In this paper we propose a method for selecting an appropriate subset of sensors with a view to minimize estimation error while tracking a target with sensors spread across in a 2-dimensional plane. In particular, we address the problem of given N sensors, select n < N sensors to improve the given estimate of target location". Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes the bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the sensors be selected based on three measures viz. (a) collinearity, (b) spread and (c) proximity to the target. We use least square error estimation technique to compute the target location using distance measurements subject to multiplicative errors from multiple sensors.
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This paper presents the improved performance of the IMMPDA (interacting multiple model with probabilistic data association) filter capable of detecting a maneuvering target. ^In order to detect a maneuvering of a target, an error ...
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This paper presents the improved performance of the IMMPDA (interacting multiple model with probabilistic data association) filter capable of detecting a maneuvering target. ^In order to detect a maneuvering of a target, an error monitoring and recovery method of a perception net is applied to the IMMPDA filter. ^Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. ^Thus the method is derived as an extension of previous results on tracking for a maneuvering target without clutter. ^The conventional filter (IMMPDA filter) which employs the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target and reduces the maximum values of estimation errors when maneuvering starts and finishes. ^The effectiveness of the proposed method is validated through simulations by comparing it with the conventional IMMPDA filter. ^(Author)
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This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its ki...
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This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHMs is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.
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摘要 :
This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its ki...
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This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHMs is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.
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