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Proceedings Volume 8393, including the Title Page, Copyright
information, Table of Contents, and the Conference Committee listing.
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Chemical and biological monitoring systems are faced with the challenge of detecting weak signals from contam-
inants of interest while at the same time maintaining extremely low false alarm rates. We present methods to
control the number of false alarms while maintaining power to detect; evaluating these methods on a fixed sensor
grid. Contaminants are detected using signals produced from underlying sensor-specific detection algorithms.
By learning from past data, an adaptive background model is constructed and used with a multi-hypothesis
testing method to control the false alarm rate.
Detection methods for chemical/biological releases often depend on specific models for release types and
missed detection rates at the sensors. This can be problematic in field situations where environment specific
effects can alter both a sensor's false alarm and missed detection characteristics. Using field data, the false
alarm statistics of a given sensor can be learned and used for inference; however the missed detection statistics
for a sensor are not observable while in the field. As a result, we pursue methods that do not rely on accurate
estimates of a sensor's missed detection rate. This leads to the development of the Adaptive Regions Method
that under certain assumptions is designed to conservatively control the expected rate of false alarms produced
by a fusion system over time, while maintaining power to detect.
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With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence,
Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational
awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance
video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to
identify moving objects with a classification algorithm based on both static and kinematic features of the objects.
Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction
methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up
Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the
video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies
characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates
and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the
acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of
five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and
the object of interest.
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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.
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The parametric Rao test for multichannel adaptive signal detection by the adaptive generalized detector (GD) constructed
based on the generalized approach to signal processing in noise is derived by modeling the disturbance signal as a multichannel
autoregressive process. The parametric Rao test takes a form identical to that of parametric GD for space-time
adaptive processing in airborne surveillance radar systems and other similar applications. The equivalence offers new insights
into the performance and implementation of the GD. Specifically, the Rao/GD is asymptotically (in the case of large
samples) a parametric generalized likelihood ratio test generalized detector (GLRT GD) due to an asymptotic equivalentce
between the Rao test and the GLRT/GD. The asymptotic distribution of the Rao/GD test statistic is obtained in closed
form, which follows an exponential distribution under the null hypothesis (the target return signal is absent) and, respectively,
a non-central Chi-squared distribution with two degrees of freedom under the alternative hypothesis (the target
return signal is present). The noncentrality parameter of the noncentral Chi-squared distribution is determined by the output
signal-to-interference-plus-noise ratio of a temporal whitening filter. Since the asymptotic distribution under the null
hypothesis is independent of the unknown parameters, the Rao/GD asymptotically achieves constant false alarm rate
(CFAR) GD. Numerical results show that these results are superior in predicting the performance of the parametric adaptive
matched filter detector even with moderate data support.
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Multiple Input Multiple Output- MIMO Radar is a fast growing research area. This paper will give a brief
introduction to the subject as well as derive an image formation scheme. The general problem of radar imaging
is to use some physical model for a transmitted signal, and measurements of the signal that is scattered back to
a receiver by a scene to attempt to derive information about the scene. The concept of communication involves
a message sender, a message receiver, and a channel. The sender sends a message through the channel to the
receiver. The receiver attempts to recover the original message. MIMO communication is just communication
that involves sending several messages to several recipients. The problem of Multiple Input Multiple Output
Radar Imaging is to use the corruption of transmitted messages to try and derive useful information about the
environment that the messages traveled through. The extra information gained with MIMO Radar can be used
to get rid of false targets, detect moving targets, and create a better resolution image. The plan for this research
is to culminate to an in-scene 3-d Image reconstruction algorithm. The model presented provides a context in
which to examine this problem.
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We present a theory and algorithm for detecting and classifying weak, distributed patterns in network data
that provide actionable information with quantiable measures of uncertainty. Our work demonstrates the
eectiveness of space-time inference on graphs, robust matrix completion, and second order analysis for the
detection of distributed patterns that are not discernible at the level of individual nodes. Motivated by the
importance of the problem, we are specically interested in detecting weak patterns in computer networks related
to Cyber Situational Awareness. Our focus is on scenarios where the nodes (terminals, routers, servers, etc.)
are sensors that provide measurements (of packet rates, user activity, central processing unit usage, etc.) that,
when viewed independently, cannot provide a denitive determination of the underlying pattern, but when fused
with data from across the network both spatially and temporally, the relevant patterns emerge. The approach
is applicable to many types of sensor networks including computer networks, wireless networks, mobile sensor
networks, and social networks, as well as in contexts such as databases and disease outbreaks.
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We derive five new particle flow algorithms for
nonlinear filters based on the small curvature
approximation inspired by fluid dynamics. We find it
extremely interesting that this physically motivated
approximation 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.
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Many modern agile sensor systems are capable of being adaptively tasked in response to an evolving environment.
This paper describes an algorithm developed in the framework of previous work by Casta~non, Wintenby and
Krishnamurthy. The goal is to schedule the time and dwell time for updates of targets under track using a phased
array radar. This problem is addressed using Lagrangian relaxation, decoupling the joint optimisation into a
series of single target problems. After discretising the single target decision state (i.e., the covariance matrix),
these single target problems are solved as Markov decision processes. An example of a method for selecting the
state space discretisation is outlined and the results used to generate a closed loop schedule for a set of track
states.
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Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However,
there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually
be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the
Singer maneuver model and worst-case correlation time, with piecewise constant white acceleration, and with
continuous white acceleration, and the reduced state filter proposed by Mookerjee and Reifler.1, 2
Performance evaluation of a tracking filter is significantly simplified by appropriate normalization. For the
Kalman filter with the Singer maneuver model, the steady-state RMS error immediately after an update depends
on only two dimensionless parameters.3 By assuming a worst case value of target acceleration correlation time,
we reduce this to a single parameter without significantly changing the filter performance (within a few percent
for air tracking).4
With this simplification, we find for all four filters that the RMS errors for the extrapolated state are functions
of only two dimensionless parameters. We provide simple analytic approximations in each case.
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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.
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Fusing data together for target tracking is a complex problem. There are two key steps. First, the raw observations must
be associated with existing tracks or used to form new tracks. Once the association has been done, then the tracks can be
updated and filtered with the new data. The updating and filtering is usually the easier of the two parts and it is the
association that can lead to most of the complexity in target tracking. When associating data (either measurements or
tracks or both) with existing tracks, the separation between the tracks is critical to how difficult the association decisions
will be. If the tracks are widely separated then the association decisions can be relatively easy. On the other hand, when
the tracks are closely spaced the association decisions can be very difficult or nearly impossible. When the tracks or
measurements are in three dimensions (such as with active sensors) the association can be accomplished in all three
dimension thus making an easier distinction of targets that may be very close in two dimensions, but distant in the third
dimension. However, when there are only two dimensions (as for passive sensors) observed by a sensor, targets that are
widely separated may appear to be very close or even unresolved. In this paper, we will discuss the issues involved with
applying the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm to fusing either measurements or tracks from
passive sensors.
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This paper presents an approach to attribute estimation incorporating data association ambiguity. In modern tracking
systems, time pressures often leave all but the most likely data association alternatives unexplored, possibly producing track
inaccuracies. Numerica's Bayesian Network Tracking Database, a key part of its Tracker Adjunct Processor, captures and
manages the data association ambiguity for further analysis and possible ambiguity reduction/resolution using subsequent
data.
Attributes are non-kinematic discrete sample space sensor data. They may be as distinctive as aircraft ID, or as broad as
friend or foe. Attribute data may provide improvements to data association by a process known as Attribute Aided Tracking
(AAT). Indeed, certain uniquely identifying attributes (e.g. aircraft ID), when continually reported, can be used to define
data association (tracks are the collections of observations with the same ID). However, attribute data arriving infrequently,
combined with erroneous choices from ambiguous data associations, can produce incorrect attribute and kinematic state
estimation.
Ambiguous data associations define the tracks that are entangled with each other. Attribute data observed on an entangled
track then modify the attribute estimates on all tracks entangled with it. For example, if a red track and a blue
track pass through a region of data association ambiguity, these tracks become entangled. Later red observations on one
entangled track make the other track more blue, and reduce the data association ambiguity. Methods for this analysis have
been derived and implemented for efficient forward filtering and forensic analysis.
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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.
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The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker is an algorithm that has been shown
to work well against low-SNR targets in an active multistatic framework with multiple transmitters and multiple
receivers. In this framework, measurements are usually received in time-bearing space. Prior work on
ML-PDA implemented the algorithm in Cartesian measurement space - this involved converting the measurements
and their associated covariances to (x, y) coordinates. The assumption was made that Gaussian measurement
error distributions in time-bearing space could be reasonably approximated by transformed Gaussian
error distributions in Cartesian space. However, for data with large measurement azimuthal uncertainties, this
becomes a poor assumption. This work compares results from a previous study that applied ML-PDA in a
Cartesian implementation to the Metron 2009 simulated dataset against ML-PDA applied to the same dataset
but with the algorithm implemented in time-bearing space. In addition to the Metron dataset, a multistatic
Monte Carlo simulator is used to create data with properties similar to that in the Metron dataset to statistically
quantify the performance difference of ML-PDA operating in Cartesian measurement space against that
of ML-PDA operating in time-bearing space.
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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.
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Target tracking techniques have usually been applied to physical systems via radar, sonar or imaging modalities.
But the same techniques - filtering, association, classification, track management - can be applied to nontraditional
data such as one might find in other fields such as economics, business and national defense. In this
paper we explore a particular data set. The measurements are time series collected at various sites; but other
than that little is known about it. We shall refer to as the data as representing the Megawatt hour (MWH)
output of various power plants located in Afghanistan. We pose such questions as: 1. Which power plants seem to have a common model?
2. Do any power plants change their models with time?
3. Can power plant behavior be predicted, and if so, how far to the future?
4. Are some of the power plants stochastically linked? That is, do we observed a lack of power demand at
one power plant as implying a surfeit of demand elsewhere?
The observations seem well modeled as hidden Markov. This HMM modeling is compared to other approaches;
and tests are continued to other (albeit self-generated) data sets with similar characteristics.
Keywords: Time-series analysis, hidden Markov models, statistical similarity, clustering weighted
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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.
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This paper presents algorithms for prediction, tracking, and retrodiction for targets whose motion is constrained
by external conditions (e.g., shipping lanes, roads). The targets are moving along a path, defined by way-points
and segments. Measurements are obtained by sensors at low revisit rates (e.g., spaceborne). Existing tracking
algorithms assume that the targets follow the same motion model between successive measurements, but in a
low revisit rate scenario targets may change the motion model between successive measurements. The proposed
prediction algorithm addresses this issue by considering possible motion model whenever targets move to a
different segment. Further, when a target approaches a junction, it has the possibility to travel into one of
the multiple segments connected to that junction. To predict the probable locations, multiple hypotheses for
segments are introduced and a probability is calculated for each segment hypothesis. When measurements become
available, segment hypothesis probability is updated based on a combined mode likelihood and a sequential
probability ratio test is carried out to reject the hypotheses. Retrodiction for path constrained targets is also
considered, because in some scenarios it is desirable to find out the target's exact location at some previous time
(e.g., at the time of an oil leakage). A retrodiction algorithm is also developed for path constrained targets so
as to facilitate motion forensic analysis. Simulation results are presented to validate the proposed algorithm.
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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.
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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 multiplehypothesis
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 recentlyintroduced
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.
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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.
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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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In this paper we present a new algorithm for approximating the target-measurement association
probabilities of the Joint Probabilistic Data Association Filter (JPDAF). This algorithm is designed to
robustify the JPDAF against track coalescence which can greatly degrade the performance of the JPDAF
and other approximate algorithms. It is based on the works of Roecker and the JPDAF* of Blom and
Bloem. We compare our new algorithm with the two it is based on, as well as the "cheap JPDAF" and
the Set JPDAF, and show that it offers a significant improvement in computational complexity over the
JPDAF*, and improvement in tracking error over the Roecker algorithm. We compare their performance
with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in scenarios involving several
closely-spaced targets. A consistency comparison of the various algorithms considered is also presented.
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This paper is Part VIc of a comprehensive survey of maneuvering target tracking without addressing the so-called
measurement-origin uncertainty. It provides an in-depth coverage of various approximate density-based nonlinear filters
in discrete time developed particularly for handling the uncertainties induced by potential target maneuvers as well as nonlinearities
in the dynamical systems commonly encountered in target tracking. An emphasis is given to more recent results,
especially those with good potential for tracking applications.
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