Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker; (ii) modify multi-target trackers to take a set of tracks as a set of measurements; and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.
The Interface Launcher (iLauncher) technology automates the submission of HPC jobs and provides a mechanism for rapidly prototyping web interfaces from the user’s desktop to powerful capabilities running on back-end high performance computing (HPC) resources, including Amazon Web Services (AWS) GovCloud, distributed clusters of heterogeneous nodes with multiple graphics processing units (GPUs) per node running the Slurm batch queuing software, and Department of Defense (DoD) supercomputers running the Portable Batch Scheduling (PBS) software. We present some of the latest advancements in iLauncher plugin development, particularly in the use of channels to make deployment of plugins easier for groups of users. We also describe our latest plugin for using PostgreSQL with the TimescaleDB and PostGIS extensions in a Singularity container with the pgAdmin and Jupyter Notebook web interfaces for use on these HPC resources.
Sensor data fusion has significant potential for advancing discovery, processing, and inspection of engineering materials. The paper reviews recent developments in data fusion with respect to materials inspection, highlights potential areas for materials growth, and shows results from application of matching component analysis (MCA). The main contributions of the paper include analysis of current fusion methods to uncover challenges and opportunities with respect to two inspection modalities (scanning acoustic microscopy and eddy current testing); and presenting an extension of MCA which has previously developed for other image modalities. Presenting MCA highlights the benefits towards a baseline method of SAM-EC fusion using the Multi-Scale Mixed Modality Microstructure Titanium Assessment Characterization (M4TAC) challenge dataset. Example results are presented with current motivations of enhancements.
This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.
Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is wellsuited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.
In radar target tracking, knowledge of the true dynamics of target motion is paramount for accurate state estimates. In this paper, we propose a method of target maneuver detection utilizing symbolic dynamics. We demonstrate its ability to compete with other commonly used maneuver detectors. This is done through simulations performing target maneuver detection.
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