作者：
Gray, JEMcCabe, DHUSN
Ctr Surface Warfare Dahlgren Div Syst Res & Technol Dept Dahlgren VA 22448 USA
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the, same operation at the identical time), take measurem...
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ISBN:
(纸本)0819441872
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the, same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. This paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.
Covariance consistency is a critical element of a robust target tracking system. Target maneuvers and measurement origin uncertainty pose significant challenges to a tracking algorithm achieving covariance consistency...
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ISBN:
(纸本)0819441872
Covariance consistency is a critical element of a robust target tracking system. Target maneuvers and measurement origin uncertainty pose significant challenges to a tracking algorithm achieving covariance consistency. The Interacting Multiple Model (IMM) estimator is a nearly consistent estimator for tracking maneuvering targets. While the Probabilistic data Association Filter (PDAF) achieves covariance, consistency for a single target in presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open Issue. When using an unique assignment technique for associating measurements-to-tracks, the measurements are typically processed with the track filter as if the measurement-to-track association probabilities are unity for each measurement-track pair. This processing of the measurements results in poor covariance consistency for closely-spaced targets. In this paper, the use of approximate association probabilities for each measurement-to-track pair is proposed for the unique assignments and included in the track filter processing of the measurement to enhance the covariance consistency for closely-spaced targets.
This paper describes a new method of small target detection according to the features of small moving targets. This method bases on multi level threshold decision-making and sliding trajectory confidence testing techn...
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ISBN:
(纸本)0819441872
This paper describes a new method of small target detection according to the features of small moving targets. This method bases on multi level threshold decision-making and sliding trajectory confidence testing technology. Energy accumulating, multi-level thresholding, trajectory searching and matching, and trajectory confidence testing are combined in this method. The detection resolves the contrary requirement of real-time processing, large data throughput, high detection capability in small moving target detection in IR image sequences. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real time performance.
This paper describes an application of sequential Monte Carlo estimation (particle filtering) to the problem of tracking targets occasionally hidden in the blind Doppler zones of a radar. A particle filter which incor...
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ISBN:
(纸本)0819441872
This paper describes an application of sequential Monte Carlo estimation (particle filtering) to the problem of tracking targets occasionally hidden in the blind Doppler zones of a radar. A particle filter which incorporates the prior knowledge of the blind Doppler zone limits has been designed. The simulation results suggest significant improvement in track continuity over the standard Extended Kalman filter. As an operationally viable solution a hybrid tracker is envisaged which can switch between the EKF (with possibly built-in data association logic) and the particle filter, depending on the tracking conditions.
The aim of ground surveillance with airborne GMTI radar is the near real-time production of a dynamic ground picture. Since the sensors often record merely certain aspects of the situation of interest, information fus...
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ISBN:
(纸本)0819441872
The aim of ground surveillance with airborne GMTI radar is the near real-time production of a dynamic ground picture. Since the sensors often record merely certain aspects of the situation of interest, information fusion is of particular importance. In addition, even after platform motion compensation by signalprocessing techniques (STAP), ground moving targets can well be masked by the clutter notch of the sensor. This physical phenomenon directly results from the low-DOPPLER characteristics of the targets and causes interfering fading effects that seriously affect the tracking performance/continuity. In this context a GMTI sensor model provides significant performance improvements being relevant also to sensor data fusion. The Minimum Detectable Velocity (MDV) proves to be an important sensor parameter explicitly entering into GMTI tracking. In combination with road map information or sensor data fusion the refined model can in particular alleviate the recognition of stopping targets. A numerical example quantitatively illustrates the potential gain by exploiting GMTI-modeling, road-maps, and sensor fusion.
Modern target tracking systems perform different tasks among which are target detection, selection and tracking. in this study, we describe a target selection technique based on Utility Theory. The utility of each tar...
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ISBN:
(纸本)0819441872
Modern target tracking systems perform different tasks among which are target detection, selection and tracking. in this study, we describe a target selection technique based on Utility Theory. The utility of each target is assumed to be a linear combination of some basis functions that act on certain features of the target such as size, intensity, speed and direction. The unknown parameters of the basis functions and their weights are selected using a descent type optimization technique. Simulation results are presented.
Without range measurements, a sensor platform must execute a nontrivial motion if good target location estimates are to be generated with conventional tracking algorithms. This paper shows that even a stationary image...
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ISBN:
(纸本)0819441872
Without range measurements, a sensor platform must execute a nontrivial motion if good target location estimates are to be generated with conventional tracking algorithms. This paper shows that even a stationary image-based tracker can provide good location estimates when the target maneuvers. A tight cover region is generated with the proposed algorithm, and is compared with a more general bound.
In this paper, two modifications are made to the derivation of the PDAF: one replaces the Poisson distributed false alarms with a binomial distribution, the other involves the assumed distribution of the angular measu...
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ISBN:
(纸本)0819441872
In this paper, two modifications are made to the derivation of the PDAF: one replaces the Poisson distributed false alarms with a binomial distribution, the other involves the assumed distribution of the angular measurements associated with false alarms. The Binomial distribution better fits the kind of data typically seen in radar because the track gate typically, involves a small number of candidate range cells. The second modification is founded on the assumption that the angle-of-arrival estimates are produced with monopulse techniques. Previous work has modeled the false measurements as being uniformly distributed in the uncertainty volume of the track gate, while a more accurate approach recognizes that the angle components of the false alarms are better modeled as Gaussian perturbations about beam center.
In this paper a new method is presented to deal with multiple model filtering. The method is the so called Multiple Model Multiple Hypothesis Filter (MMMH filter). For each hypothesis a Kalman filter is running. This ...
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ISBN:
(纸本)0819441872
In this paper a new method is presented to deal with multiple model filtering. The method is the so called Multiple Model Multiple Hypothesis Filter (MMMH filter). For each hypothesis a Kalman filter is running. This hypothesis represents a specific model mode sequence history. The proposed method has a high level of genericity and is highly flexible. The main feature is that the number of hypotheses that are maintained varies with the 'difficulty' of a scenario. It is shown that the MMMH performs better than the widely used Interacting Multiple Model (IMM) filter.
The proceedings contains 53 papers from the conference on signal and dataprocessing of smalltargets 2001. The topics discussed include: radar target detection;object detection;video sequences;motion estimation;targe...
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The proceedings contains 53 papers from the conference on signal and dataprocessing of smalltargets 2001. The topics discussed include: radar target detection;object detection;video sequences;motion estimation;target cueing;hyperspectral images;adaptive antenna processing;surveillance radars;nonlinear detection filters;radar clutter;data association filters;data fusion;stereo vision;linear systems;multiple-model algorithms and dynamic programming algorithms.
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