Tracking fishing vessels individually plays a pivotal role in fisheries monitoring, control, and surveillance. Fishing trip is the most appropriate granularity level to study routine fishing activity patterns. Since self-reported information about fishing vessel trips is notoriously unreliable, we propose here TripTracker, an unsupervised learning approach to partition raw trajectories of ships and boats engaging in fishing into trips and identify trip types. TripTracker first partitions a fishing trip into micro-activities, then uses cluster analysis to confirm the microactivity type. Next, it employs multiple Hidden Markov Models to partition the trip into segments, each of which representing a routine activity. Finally, TripTracker utilizes maritime contextual information to differentiate various fishing trip types, revealing actionable knowledge about vessel activities and their operations. Our experimental evaluation on a large real-world fishing vessel trajectory dataset, confirms TripTracker’s practicability and effectiveness for enhancing maritime domain awareness.

The impact of marine life on the oceans of our planet is undeniable and overfishing is a serious threat to marine ecosystems worldwide. Maritime domain awareness calls for continuous monitoring and tracking of fisheries using data from maritime intelligence sources to detect illegal fishing activities. Marine traffic data from vessel tracking services is a promising source for identifying, locating, and capturing vessel information. Given the volume of such data, manual processing is impossible, raising an immediate need for autonomous and smart systems to follow the footprints of vessels and detect their activity types in near real-time. To achieve this goal, we propose FishNET, a simple yet effective convolutional neural network (CNN) model for vessel trajectory classification. The model is trained using a set of invariant spatiotemporal feature sequences extracted from the behavioral characteristics of vessel movements.

In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets from vessel tracking services. Dark fishing refers to stealthy fishing operations by vessels trying to hide their illicit activities related to various forms of illegal fishing-one of the most serious threats to world fisheries and fish populations worldwide as well as to global food security. Our approach builds on profiling and ranking fishing vessels by analyzing their routine operations over extended time periods to uncover abnormal activity patterns associated with dark fishing. The focus is on vessel movement patterns rendered as a trajectory with defined starting and endpoints such as ports and known anchorage locations. Specifically, we analyze scenarios where the fishing pattern, with the fishing gear in the water, is obscured in a vessel's reported trip data. Our experimental evaluation, using a large dataset of fishing vessel trajectories from coastal waters of North America, shows the effectiveness and efficiency of the proposed method in differentiating between suspicious and normal fishing vessels irrespective of the vessel type.

Automated vessel anomaly detection is immensely important for preventing and reducing illegal activities (e.g., drug dealing, human trafficking, etc.) and for effective emergency response and rescue in a country's territorial waters. A major limitation of previously proposed vessel anomaly detection techniques is the high rate of false alarms as these methods mainly consider vessel kinematic information which is generally obtained from AIS data. In many cases, an anomalous vessel in terms of kinematic data can be completely normal and legitimate if the "context" at the location and time (e.g., weather and sea conditions) of the vessel is factored in. In this paper, we propose a novel anomalous vessel detection framework that utilizes such contextual information to reduce false alarms through "contextual verification". We evaluate our proposed framework for vessel anomaly detection using massive amount of real-life AIS data sets obtained from U.S. Coast Guard. Though our study and developed prototype is based on the maritime domain the basic idea of using contextual information through "contextual verification" to filter false alarms can be applied to other domains as well.

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures like oil and gas rigs and other types of critical infrastructure against common threats and illegal activities. Typical examples range from smuggling of drugs and weapons, human trafficking and piracy all the way to terror attacks. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis approach to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We consider such scenarios as probabilistic processes and analyze complex vessel trajectories using machine learning to model common patterns. Specifically, we represent patterns as left-to-right Hidden Markov Models and classify them using Support Vector Machines. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information, including kinematic features, geospatial features, contextual information and maritime domain knowledge. Our experimental evaluation shows the effectiveness of the proposed approach using comprehensive real-world vessel tracking data from coastal waters of North America.

In this paper, a goal-driven net-enabled distributed data fusion system is described for CanCoastWatch (CCW) project. Multiple sensors are deployed and managed to achieve the goals of situation assessment using a net-enabled architecture. The local tracks reported by multiple sensors are first integrated into global tracks. Decision making is then performed on basic sub-goals that can be directly derived from the fused global tracks. Finally, a goal-driven rule-based expert system uses the basic sub-goal decisions for goal reasoning.

MacDonald Dettwiler is leading a PRECARN partnership project to develop an advanced simulation testbed for the evaluation of the effectiveness of Network Enabled Operations in a coastal large volume surveillance situation. The main focus of this testbed is to study concepts like distributed information fusion, dynamic resources and networks configuration management, and self synchronising units and agents. This article presents the system architecture with an emphasis on our approach for distributed information fusion.