Over the last two decades, many solutions have arisen to combine target tracking estimation with classification methods.
Target tracking includes developments from linear to non-linear and Gaussian to non-Gaussian processing. Pattern
recognition includes detection, classification, recognition, and identification methods. Integrating tracking and pattern
recognition has resulted in numerous approaches and this paper seeks to organize the various approaches. We discuss the
terminology so as to have a common framework for various standards such as the NATO STANAG 4162 - Identification
Data Combining Process. In a use case, we provide a comparative example highlighting that location information (as an
example) with additional mission objectives from geographical, human, social, cultural, and behavioral modeling is
needed to determine identification as classification alone does not allow determining identification or intent.
There are hosts of target tracking algorithm approaches, each valued with respect to the scenario operating conditions (e.g.
sensors, targets, and environments). Due to the application complexity, no algorithm is general enough to be widely
applicable, nor is a tailored algorithm able to meet variations in specific scenarios. Thus, to meet real world goals,
multitarget tracking (MTT) algorithms need to undergo performance assessment for (a) bounding performance over
various operating conditions, (b) managing expectations and applicability for user acceptance, and (c) understanding the
constraints and supporting information for reliable and robust performance. To meet these challenges, performance
assessment should strive for three goals: (1) challenge problem scenarios with a rich variety of operating conditions, (2) a
standard, but robust, set of metrics for evaluation, and (3) design of experiments for sensitivity analysis over parameter
variation of models, uncertainties, and measurements.
KEYWORDS: Information security, Quantization, Signal to noise ratio, Transmitters, Receivers, Data modeling, Binary data, Distance measurement, Sensors, Signal processing
Geo-security service, which refers to the authorization of persons or facilities based on their distinctive location
information, is an application of the fields of position, navigation and time (PNT). Location features from radio
navigation signals are mapped into a precise verification tag or geotag to block or allow certain action or access. A
device that integrates a location sensor and geotag generation algorithm is tamper-resistant, that is, one cannot spoof the
device to bypass the location validation. This paper develops a theoretical framework of the geotag-based positioning
and security systems, and evaluates the system performance analytically and experimentally by using Loran signals as a
case study.
KEYWORDS: Sensors, Probability theory, Target detection, Information theory, Target recognition, Error analysis, Distance measurement, Switches, Sensing systems, Control systems
In this paper, we compare the information-theoretic metrics of the Kullback-Leibler (K-L) and Renyi (α) divergence
formulations for sensor management. Information-theoretic metrics have been well suited for sensor management as they
afford comparisons between distributions resulting from different types of sensors under different actions. The difference
in distributions can also be measured as entropy formulations to discern the communication channel capacity (i.e.,
Shannon limit). In this paper, we formulate a sensor management scenario for target tracking and compare various
metrics for performance evaluation as a function of the design parameter (α) so as to determine which measures might
be appropriate for sensor management given the dynamics of the scenario and design parameter.
In surveillance and reconnaissance applications, dynamic objects are dynamically followed by track filters with
sequential measurements. There are two popular implementations of tracking filters: one is the covariance or Kalman
filter and the other is the information filter. Evaluation of tracking filters is important in performance optimization not
only for tracking filter design but also for resource management. Typically, the information matrix is the inverse of the
covariance matrix. The covariance filter-based approaches attempt to minimize the covariance matrix-based scalar
indexes whereas the information filter-based methods aim at maximizing the information matrix-based scalar indexes.
Such scalar performance measures include the trace, determinant, norms (1-norm, 2-norm, infinite-norm, and Forbenius
norm), and eigenstructure of the covariance matrix or the information matrix and their variants. One natural question to
ask is if the scalar track filter performance measures applied to the covariance matrix are equivalent to those applied to
the information matrix? In this paper we show most of the scalar performance indexes are equivalent yet some are not.
As a result, the indexes if used improperly would provide an "optimized" solution but in the wrong sense relative to
track accuracy. The simulation indicated that all the seven indexes were successful when applied to the covariance
matrix. However, the failed indexes for the information filter include the trace and the four norms (as defined in
MATLAB) of the information matrix. Nevertheless, the determinant and the properly selected eigenvalue of the
information matrix were successful to select the optimal sensor update configuration. The evaluation analysis of track
measures can serve as a guideline to determine the suitability of performance measures for tracking filter design and
resource management.
This paper develops and evaluates a game-theoretic approach to distributed sensor-network management for target
tracking via sensor-based negotiation. We present a distributed sensor-based negotiation game model for sensor
management for multi-sensor multi-target tacking situations. In our negotiation framework, each negotiation agent
represents a sensor and each sensor maximizes their utility using a game approach. The greediness of each sensor is
limited by the fact that the sensor-to-target assignment efficiency will decrease if too many sensor resources are assigned
to a same target. It is similar to the market concept in real world, such as agreements between buyers and sellers in an
auction market. Sensors are willing to switch targets so that they can obtain their highest utility and the most efficient
way of applying their resources. Our sub-game perfect equilibrium-based negotiation strategies dynamically and
distributedly assign sensors to targets. Numerical simulations are performed to demonstrate our sensor-based negotiation
approach for distributed sensor management.
To enhance target-tracking accuracy during maneuvers, we develop (1) a post-update compensation (PUC) method to
contain the maneuvering errors and (2) a maneuver indicator to signify the start and end of a maneuver. Tracking of a
maneuvering target is formulated as post-update compensation (PUC), in which a non-maneuvering tracker such as the
α-β filter is allowed to propagate and update its estimates based on the innovations (defined as the difference between a
measurement and its prediction/a priori) without maneuver consideration. Maneuver-induced errors are then removed
from the state updates/a posteriori, yielding compensated estimates based on the residuals (defined as the difference
between a measurement and the one generated from the state update). This post-update compensation (PUC) scheme is
equivalent to Dale Blair's [3] two-stage estimator but simpler in formulation. Simulation results are presented to
illustrate the PUC scheme with error analysis as well as implications of the enhanced tracking methods to increase track
life, reduce location errors of maneuvering targets, and techniques for sensor management as to when to schedule an
observation for target identification.
Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by
managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a
crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor
management (DSM) scheme is increasingly important in modern multi-sensor systems. DSM is afforded in modern
systems through increased bandwidth, wireless communication, and enhanced power. However, protocols for system
control are needed to management device access. As game theory offers learning models for distributed allocations of
surveillance resources and provides mechanisms to handle the uncertainty of surveillance area, we propose an agent-based
negotiable game theoretic approach for decentralized sensor management (ANGADS). With the decentralized
sensor management scheme, sensor assignment occurs locally, and there is no central node and thus reduces the risk of
whole-system failure. Simulation results for a multi-sensor target-tracking scenario demonstrate the applicability of the
proposed approach.
This paper presents a simple method of target maneuver indication (TMI) from high range resolution (HRR) radar
measurements. The HRR TMI (HTMI) relates the slope of a target's range-Doppler image to the underlying turn rate
when the target undergoes a turn maneuver. As an intermediate product of range profile formation process of an HRR
radar, this approach provides an easy and quick indication of target maneuverability and, under favorable conditions, an
estimate of such a maneuver (the turn rate and turn radius). The target maneuver indication can be incorporated into a
target tracker to determine whether the target is decelerating or accelerating and to estimate the curvature of a turn so as
to improve tracking accuracy. In this paper, we first formulate the target maneuver indicator from HRR radar
measurements. Various methods for slope and slope rate estimation are then presented. The simulation environment is
described together with the software tools used to generate target RF signatures. Simulation results are presented to show
the operation and performance of the simple maneuver indicator for various encounter scenarios. Finally, the use of the
HMTI method for maneuvering target tracking is discussed with performance metrics of timeliness, tracking sensitivity,
and track accuracy as well as target identity confidence when HRR range profiles are also used for identification.
Mutual-aided target tracking and target identification schemes are described by exploiting the couplings between the target tracking and target identification systems, which are typically implemented in a separate manner. A hybrid state space approach is formulated to deal with continuous-valued kinematics, discrete-valued target type, and discrete-valued target pose (inherently continuous but quantized). We identify and analyze ten possible mutual aiding mechanisms with different complexity in different levels. The coupled tracker design is illustrated within the context of JointSTARS using GMTI and HRRR measurements as well as digital terrain and elevation data (DTED) and road map among others. The resulting coupled tracking and identification system is expected to outperform the separately designed systems particularly during target maneuvers, for recovering from temporary data dropout, and in a dense target environment.
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