The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric track-before-detect algorithm that has been shown to give good performance at a relatively low computation cost. Recent research has extend...
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ISBN:
(纸本)9780819490711
The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric track-before-detect algorithm that has been shown to give good performance at a relatively low computation cost. Recent research has extended the algorithm to allow it to estimate the signature of targets in the sensor image. This paper shows how this approach can be adapted to address the problem of group target tracking where the motion of several targets is correlated. The group structure is treated as the target signature, resulting in a two-tiered estimator for the group bulk-state and group element relative position.
We derive seven new particle flow algorithms for nonlinear filters based on the small curvature condition inspired by fluid dynamics. We find it extremely interesting that this physically motivated condition generaliz...
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ISBN:
(纸本)9780819490711
We derive seven new particle flow algorithms for nonlinear filters based on the small curvature condition inspired by fluid dynamics. We find it extremely interesting that this physically motivated condition generalizes two of our previous exact flow algorithms, namely incompressible flow and Gaussian flow. We derive a new algorithm to compute the inverse of the sum of two linear differential operators using a second homotopy, similar to Feynman's perturbation theory for quantum electrodynamics as well as Gromov's h-principle.
In this paper we present an approach for tracking in long range radar scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems, and that part...
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ISBN:
(纸本)9780819490711
In this paper we present an approach for tracking in long range radar scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems, and that particle filters may suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter and the Gaussian Mixture Sigma-Point Particle Filter are shown to avoid this diversity problem while producing consistent results.
We demonstrate that human skin biometrics in the visible to near infrared (VNIR) regime can be used as reliable features in a multistage human target tracking algorithm suite. We collected outdoor VNIR hyperspectral d...
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ISBN:
(纸本)9780819490711
We demonstrate that human skin biometrics in the visible to near infrared (VNIR) regime can be used as reliable features in a multistage human target tracking algorithm suite. We collected outdoor VNIR hyperspectral data of human skin, consisting of two human subjects of different skin types in the Fitzpatrick Scale (Type I [Very Fair] and Type III [White to Olive]), standing side by side at seven ranges (50 ft to 370 ft) in a suburban background. At some of these ranges, the subjects fall under the small target category. We propose a three-step approach: Step 1, reflectance retrieval;Step 2, exploitation of absorption wavelength line at 577 nanometers, due to oxygenated hemoglobin in blood near the surface of skin;and Step 3, matched filtering on candidate patches in the input imagery that successfully passed Step 2, using as input all of the available bands in a spectral average representation of human skin. Step-3 functionality is only applied to patches in the imagery showing evidence of human skin (Step 2 output). Regardless of the targets' kinematic states, the approach produced some excellent results locating the presence of human skin in the example dataset, yielding zero false alarms from potential confusers in the scene. The approach is expected to function as the focus of attention stage of a multistage algorithm suite for human target tracking.
Multi-target filtering for closely-spaced targets leads to degraded performance with respect to single-target filtering solutions, due to measurement provenance uncertainty. Soft data association approaches like the p...
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ISBN:
(纸本)9780819490711
Multi-target filtering for closely-spaced targets leads to degraded performance with respect to single-target filtering solutions, due to measurement provenance uncertainty. Soft data association approaches like the probabilistic data association filter (PDAF) suffer track coalescence. Conversely, hard data association approaches like multiple-hypothesis tracking (MHT) suffer track repulsion. We introduce the stochastic data association filter (SDAF) that utilizes the PDAF weights in a stochastic, hard data association update step. We find that the SDAF outperforms the PDAF, though it does not match the performance of the MHT solution. We compare as well to the recently-introduced equivalence-class MHT (ECMHT) that successfully counters the track repulsion effect. Simulation results are based on the steady-state form of the Ornstein-Uhlenbeck process, allowing for lengthy stochastic realizations with closely-spaced targets.
Effective multi-sensor, multi-target, distributed composite tracking requires the management of limited network bandwidth. In this paper we derive from first principles a value of information for measurements that can...
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ISBN:
(纸本)9780819490711
Effective multi-sensor, multi-target, distributed composite tracking requires the management of limited network bandwidth. In this paper we derive from first principles a value of information for measurements that can be used to sort the measurements in order from most to least valuable. We show the information metric must account for the models and filters used by the composite tracking system. We describe how this value of information can be used to optimize bandwidth utilization and illustrate its effectiveness using simulations that involve lossy and latent network models.
A novel algorithm for predicting target tracks through obscurations is introduced. This prediction method uses radar ground track indicators and the hidden transfer function (HTF) to predict future target locations. T...
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ISBN:
(纸本)9780819490711
A novel algorithm for predicting target tracks through obscurations is introduced. This prediction method uses radar ground track indicators and the hidden transfer function (HTF) to predict future target locations. The HTF method is described in detail, and results provided that quantify track accuracy, forecast accuracy, and the percentage of tracks exiting an obscuration occurring that occur within the forecasted region. Five different classifier methods are shown for labeling short segments of track history. Each classifier method is scored and significance testing used to determine that the data Model and SMART lookup table (LUT) were significantly better than the other classifier approaches.
Target tracking sensors and algorithms are usually evaluated using Monte Carlo simulations covering a large parameter space. We show a tracker for which the evaluation can be greatly simplified. We apply it to the one...
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ISBN:
(纸本)9780819490711
Target tracking sensors and algorithms are usually evaluated using Monte Carlo simulations covering a large parameter space. We show a tracker for which the evaluation can be greatly simplified. We apply it to the one dimensional crossing track problem (e. g. ground target tracking in a dense target environment, where targets are confined to a road), and estimate the probability that measurements and tracks are incorrectly associated. If only position is measured, we find the probability of a misassociation is a very simple analytic function of the relevant parameters: measurement standard deviation, measurement interval, target density, and target acceleration. For normally distributed target velocities, the average time between misassociations also has a simple form. We suggest roll-up metrics for tracking sensors and tracking problems.
Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their location information. However, traditionally radars are used as the primary source of surveillance and AIS...
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ISBN:
(纸本)9780819490711
Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their location information. However, traditionally radars are used as the primary source of surveillance and AIS is considered as a supplement with a little interaction between these data sets. The data from AIS is much more accurate than radar data with practically no false alarms. But unlike the radar data, the AIS measurements arrive unpredictably, depending on the type and behavior of a ship. The AIS data includes target IDs that can be associated to initialized tracks. In multitarget maritime surveillance environment, for some targets the revisit interval form the AIS could be very large. In addition, the revisit intervals for various targets can be different. In this paper, we proposed a joint probabilistic data association based tracking algorithm that addresses the aforementioned issues to fuse the radar measurements with AIS data. Multiple AIS IDs are assigned to a track, with probabilities updated by both AIS and radar measurements to resolve the ambiguity in the AIS ID source. Experimental results based on simulated data demonstrate the performance the proposed technique.
This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to de...
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ISBN:
(纸本)9780819490711
This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.
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