In contemporary defense training and operations, users regularly encounter complicated and dynamic environments that generate large amounts of knowledge derived from locally acquired data. In order to facilitate collaborative decision making, users need to effectively share and distribute locally learned knowledge in a timely manner. This paper presents a semantic-based knowledge and information sharing system (S-KISS): a forum application for efficient peer-to-peer knowledge sharing. S-KISS enables simple and casual peer-to-peer information exchange, while retaining the quality of widely disseminated content for judicious knowledge consumption. Based on advanced semantic analysis technologies, S-KISS also supports effective semantic-based knowledge searching and semi-automated knowledge management with two knowledge management methods: (1) knowledge similarity searching based on WordNet and BERTScore, and (2) semantic similarity-based knowledge graph construction and knowledge grouping. The searching method focused on the semantics of text instead of word spans. Meanwhile, the grouping method constructs a knowledge graph where each node represents a posting and the links between nodes along with their semantic similarities. Postings can be grouped into multiple clusters of similar topics using Markov clustering algorithm, which allows users to look up related content quickly and effectively. The feasibility and effectiveness of S-KISS is demonstrated via a web-based prototype using practical scenarios and a real-world benchmark dataset curated from the sub-Reddit online forum ‘r/newtothenavy’. With broad and generic language models, the capabilities developed in S-KISS are applicable for knowledge information management in any space, air, sea, marine, and cyber domains. S-KISS can be utilized in other relevant software applications such as collaborative communication platforms and e-training discussion forums.
This paper provides the results of a proposed methodology for removing sensor bias from a space-based infrared (IR)
tracking system through the use of stars detected in the background field of the tracking sensor. The tracking system
consists of two satellites flying in a lead-follower formation tracking a ballistic target. Each satellite is equipped with a
narrow-view IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult
due to a constant, non-varying or slowly varying bias error present in each sensor's line of sight measurements. As
known stars are detected during the target tracking process, the instantaneous sensor pointing error can be calculated as
the difference between star detection reading and the known position of the star. The system then utilizes a separate
bias filter to estimate the bias value based on these detections and correct the target line of sight measurements to
improve the target state vector. The target state vector is estimated through a Linearized Kalman Filter (LKF) for the
highly non-linear problem of tracking a ballistic missile. Scenarios are created using Satellite Toolkit(C) for trajectories
with associated sensor observations. Mean Square Error results are given for tracking during the period when the target
is in view of the satellite IR sensors. The results of this research provide a potential solution to bias correction while
simultaneously tracking a target.
This paper examines the effect of sensor bias error on the tracking quality of a space-based infrared (IR) tracking system
that utilizes a Linearized Kalman Filter (LKF) for the highly non-linear problem of tracking a ballistic missile. The
tracking system consists of two satellites flying in a lead-follower formation tracking a ballistic target. Each satellite is
equipped with an IR sensor that provides azimuth and bearing to the target. The tracking problem is made more difficult
due to a constant, non-varying or slowly varying bias error present in each sensor's line of sight measurements. The
effect of this error on the state vector estimation is explored using different values for sensor accuracy and various
degrees of uncertainty of the target and platform dynamic. Scenarios are created using Satellite Toolkit for trajectories
with associated sensor observations. Mean Square Error results are given for tracking during the period when the target
is in view of the satellite IR sensors. The results of this research provide insight into the accuracy requirements of the
sensors and the suitability of the LKF estimator.
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