For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE)
and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes
increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for
decision support systems. In this paper, we overview some information fusion methods in decision support and address
the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion
model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an
information fusion system. Decision support implies that information methods augment user decision making as opposed
to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that
facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain,
and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy,
confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality
(time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and
robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

/pdf/information-fusion-measures-of-effectiveness-moe-for-2djlrw5t6d.pdf

Information Fusion Measures of Effectiveness (MOE) for Decision Support

In an urban environment, one of the key challenges remains to be the reliable estimation of the other traffic participants' motion state. Due to the highly nonlinear motions in city traffic, an instant and precise estimation of heading direction, velocity, and, particularly, yaw rate is required. Radar sensors are well suited for this task due to their robustness to environmental influences and direct measurement of the radial (Doppler) velocity. High-resolution radars receive multiple reflections from an extended object. In comparison to state-of-the-art approaches, not only is the Doppler velocity of a single reference point taken into account, but also is the distribution of the Doppler velocity across the vehicle analyzed. The velocity profile is derived with characteristic features and a corresponding sample covariance. These are fused into an unscented Kalman filter, resulting in a significant accuracy improvement and a reduction in the latency of the filter to almost zero during a change in motion or initialization. This yields a great improvement in determining the trajectories of potential critical objects, increasing the time to avoid collisions. Furthermore, the approach enables simultaneous identification of the rotation center of the object, which is essential for the tracking of highly dynamic maneuvers. All approaches were implemented and evaluated on a large experimental data set using highly precise reference systems as ground truth. The results show an impressive improvement in the accuracy of the yaw rate estimation of a factor of 3–4 compared with state-of-the-art approaches in a dynamic scenario.

Tracking of Extended Objects with High-Resolution Doppler Radar

Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be characterized by 1-D High Range Resolution (HRR) Radar profiles with sufficient Signal-to-Noise Ratio (SNR). The amplitude feature information for each range bin of the HRR profile is used to discern one target from another to help maintain track or to identify a vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter, but a portion of the target signature. Enhanced clutter suppression can be achieved using a Multi-channel Signal Subspace (MSS) algorithm, which preserves target features. In this paper, we (1) exploit extra feature information from enhanced clutter suppression for Automatic Target Recognition (ATR), (2) present a Decision-Level Fusion (DLF) gain comparison using Displaced Phase Center Antenna (DPCA) and MSS clutter suppressed HRR data; and (3) develop a confusion-matrix identity fusion result for Simultaneous Tracking and Identification (STID). The results show that more channels forMSS increase identification over DPCA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation. The paper contributions include extending a two-channel MSS clutter cancellation technique to three channels, verifying the MSS is superior to the DPCA technique for target identification, and a comparison of these techniques in a novel multi-look confusion matrix decision-level fusion process.

/pdf/decision-level-fusion-performance-improvement-from-enhanced-1pkgk3dcmk.pdf

Decision-Level Fusion Performance Improvement From Enhanced HRR Radar Clutter Suppression

Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be characterized by 1-D High Range Resolution (HRR) Radar profiles with sufficient Signal-to-Noise Ratio (SNR). The amplitude feature information for each range bin of the HRR profile is used to discern one target from another to help maintain track or to identify a vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter, but a portion of the target signature. Enhanced clutter suppression can be achieved using a Multi-channel Signal Subspace (MSS) algorithm, which preserves target features. In this paper, we (1) exploit extra feature information from enhanced clutter suppression for Automatic Target Recognition (ATR), (2) present a Decision-Level Fusion (DLF) gain comparison using Displaced Phase Center Antenna (DPCA) and MSS clutter suppressed HRR data; and (3) develop a confusion-matrix identity fusion result for Simultaneous Tracking and Identification (STID). The results show that more channels for MSS increase identification over DPCA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation. The paper contributions include extending a two-channel MSS clutter cancellation technique to three channels, verifying the MSS is superior to the DPCA technique for target identification, and a comparison of these techniques in a novel multi-look confusion matrix decision-level fusion process.

Joint data management (JDM) includes the hardware (e.g. sensors/targets), software (e.g. processing/algorithms), and operations (environments) of data exchange that enable persistent surveillance in the context of a data-to-decision (D2D) information fusion enterprise. Key attributes of an information system require pragmatic assessment of data and information management, distributed communications, knowledge representation, human-systems interaction, as well as a balanced sensor mix, algorithm choice, and life-cycle data management. Throughout the paper, we seek to describe the current technology, research approaches, and metrics that influence a realizable JDM product. We develop JDM methods for structured and unstructured data to determine an accurate target track and identification as a moving intelligence (MOVINT) capability. We examine classification methods of unstructured data using seismic, acoustic, and combined fusion methods for data analysis and information management.

/pdf/joint-data-management-for-movint-data-to-decision-making-48790wiqav.pdf

Joint data management for MOVINT data-to-decision making

Tracking maneuvering targets presents a great challenge to airborne surveillance radar signal processing and sensor systems management systems. Smears caused by an uncompensated maneuver (either translational or rotational) affect target identification (ID) with distorted target images. An unexpected maneuver introduces large position estimation errors to a tracker and in the worst case loss of track. On the other hand, a sensor manager relies upon an expected performance of a tracker to schedule its resources so as to maintain target ID/tracker performance. To aid a sensor management cost function, we present a simple target maneuver indicator (TMI) specifically for the operational condition of target maneuverability. It relates the slope of a target's range-Doppler image to the underlying turn rate, if the target undergoes a maneuver. As an intermediate product of the range profile formation process, this approach provides an easy and quick indication of target maneuverability and, under favorable conditions, an estimate of such a maneuver (e.g., the turn rate), which can be incorporated into the tracking algorithm of the tracker.

A simple maneuver indicator from target’s range-doppler image

Maintaining track continuity is a challenging task for large area persistent surveillance with high target density, rapid target maneuvering, and low target-to-clutter ratio When the sensor revisit interval is long, measurement-to-track association becomes rather difficult Although maneuvering targets can be discriminated via Doppler processing, clutter still masks moving targets at certain aspect angles This creates a blind zone for GMTI (ground moving target indicator) radar, which may extend as large as 25% of the azimuth angles For high-value targets that may undergo evasive maneuvers, a net track solution coordinating multiple looks from a netted set of sensors is set forth in this paper to ensure continuous monitoring Target visibility therefore needs to be factored into netted radar platform coordination and sensor systems management For ground targets, their heading is mostly aligned with the longitudinal axis The target pose thus carries kinematic information about the direction of velocity vector that can be used to assist tracking of maneuvering targets In this paper, the range profiles of high range resolution (HRR) radar are used for pose angular estimation of maneuvering targets in clutter A pose-sensitive classifier provides matching scores that are converted into likelihoods and updated over time in a probabilistic manner Pose angular tracking from individual sensors in the network is fused to cover the blind zone encountered by individual sensors A software simulation environment that produces range profiles based on the RF signatures of moving targets is used in the study Simulation results for pose angular tracking in clutter are presented to illustrate the net track solution concept and its performance

W. Garber

Papers

A simple maneuver indicator from target’s range-doppler image

A Net Track Solution to Pose-Angular Tracking of Maneuvering Targets in Clutter with HRR Radar