Pattern-of-life extraction and anomaly detection using GMTI data

Abstract

Ground Moving Target Indicator (GMTI) uses the concept of airborne surveillance of moving ground objects to observe and take actions accordingly. This concept was established in the late 20th century and was put to test during the Gulf War to observe enemy movement on the other side of the mountain. During the war, due to limitations of technology, information such as enemy movement were usually observed through human readings. With the improvement of surveillance technology, tracking individual target became possible, which allows the extraction of useful features for advance usage. Such features, known as tracks, are the results of GMTI tracking. Although the quality of the tracker plays a crucial role in the system performance of this paper, the development of the tracker is not discussed in this paper. The developed system will use simulated ideal GMTI tracks as input dataset. This paper presents an end-to-end system that includes Anomaly GMTI (AGMTI) track simulation, Pattern of Life (PoL) extraction and Anomaly Detection System (ADS). All the subsystems (AGMTI, PoL and ADS) are independent of each other, so they can either be replaced or disabled to resemble different real-world scenarios. The results from AGMTI will provide inputs for the rest of the subsystems. The results from PoL extraction will be used to improve the performance of ADS. The proposed ADS is a semi-supervised learning detection system in which the system takes prior information to support and improve detection performance, but will still operate without prior information. The AGMTI tracks simulator will be simulated with an open-sourced software called Simulation of Urban Traffic (SUMO). The AGMTI tracks simulator subsystem will make use of SUMO's API to generate normal and anomaly GMTI tracks. The PoL extraction will be accomplished by using various clustering algorithms and statistical functions. The ADS will use combination of various anomaly detection algorithms for different anomaly events including statistical approach using Gaussian Mixture Model Expectation Maximization (GMM-EM), Hidden Markov Model (HMM), graphical approach using Weiler-Atherton Polygon Clipping (WAPC) and various clustering algorithms such as K-means clustering, Spectral clustering and DBSCAN. Finally, as extensions to the proposed system, this paper also presents Contextual Pattern of Life (CPoL) and Grouped Anomaly Detection. The CPoL is an extension to the PoL to enhance the quality and robustness of the extraction. The Grouped Anomaly is extension to both AGMTI track simulator and ADS to diversify the possible scenarios. The results from the ADS will be evaluated. Details of implementation will be provided so the system can be replicated.