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In this work, we demonstrate a system that enhances radio frequency (RF) fingerprints of individual transmitters via waveform modification to uniquely identify them amidst an ensemble of identical transmitters. This has the potential to enable secure identification, even in the presence of stolen and retransmitted unique device identifiers that are present in the transmitted waveforms, and ensures robust communications. This approach also lends itself to steganography as the waveform modifications can themselves encode information. Our system uses Bayesian program learning to learn specific characteristics of a set of emitters, and integrates the learned programs into a reinforcement learning architecture to build a policy for actions applied to the digital waveform before transmission. This allows the system to learn how to modify waveforms that leverage and emphasize inherent differences within RF front-ends to enhance their distinct characteristics while maintaining robust communications. In this ongoing research, we demonstrate our system in a small population, and provide a road map to expand it to larger populations that are expected in today’s interconnected spaces.

In this work, we demonstrate an initial application of Bayesian program learning (BPL) to learn models for individual radio frequency (RF) transmitters based on a single training signal for each transmitter. Once learned, these models are used to classify individual RF transmitters based on one signal observation. BPL improves upon other machine learning techniques by learning and classifying effectively from small amounts of training data. BPL programs represent concepts as probabilistic generative models expressed as structured procedures in an abstract description language. These models explicitly account for both concept-specific and context-dependent mechanisms, allowing them to perform well under dynamic environmental conditions. In this ongoing research, we demonstrate our system using signals from a small population of software-defined radios (SDRs) with known signal encodings in a laboratory environment, and provide a path forward for expanding it to larger populations, more signal types, and challenging transmission environments.

A novel adaptive mixture-based neural network is presented for exploiting track data to learn normal patterns of motion behavior and detect deviations from normalcy. We have extended our prior approach by introducing multidimensional probability density components to represent class density using an adaptive mixture of such components. The number of components in the adaptive mixture algorithm, as well as the values of the parameters of the density components, is estimated from the data. The network utilizes a recursive version of the Expectation Maximization (EM) algorithm to minimize the Kullback-Leibler information metric by means of stochastic approximation combined with a rule for creation of new components. Learning occurs incrementally in order to allow the system to take advantage of increasing amounts of data without having to take the system offline periodically to update models. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. In addition, the adaptive mixtures neural network classifies streaming track data as normal or deviant. These capabilities contribute to higher-level fusion situational awareness and assessment objectives by enabling a shift of operator focus from sensor monitoring and activity detection to assessment and response. Our overall motion pattern learning approach learns behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce massive amounts of track data to a rich set of information regarding operator field of regard that supports rapid decision-making and timely response initiation.

To date, our neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior have operated at fixed spatial scales. Although these models continuously adapted to incoming track data through incremental learning in order to adjust to evolving situations, the fundamental spatial scale of the learned models did not change over time. This constraint necessitates a trade-off between model maturation rate and deviation detection or behavior prediction performance. This paper describes updates to our approach that enable data-driven model scale adaptation. Anomaly detection is based on coarse resolution models during early learning stages and progressively switches to finer resolution models as sufficient data are received. This approach increases speed of model maturation with small amounts of data, while improving model fidelity and anomaly detection sensitivity as increasing amounts of data are received. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to assessment and response. Our learning algorithms learn behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding their field of regard that supports decision-making and timely response initiation.

We have developed a suite of neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to assessment and response. Our learning algorithms learn behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding their field of regard that supports decision-making and timely response initiation. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. Deviations from normalcy result in reports being published directly to operator displays or to other reasoning components within a larger system. Deviation tolerance levels are user settable during system operation to tune alerting sensitivity. Operator (or other system component) responses to anomaly alerts can be fed back into the algorithms to further enhance and refine learned models. These algorithms have been successfully demonstrated to learn vessel behaviors across the maritime domain and to learn vehicle and dismount behavior in land-based settings.

An improved neurobiologically inspired algorithm for situation awareness in the maritime domain is presented, which takes real-time tracking information and learns motion pattern models on-the- fly, enabling the models to adapt well to evolving situations while maintaining high levels of performance. The constantly refined models, resulting from concurrent incremental learning, are used to evaluate the behavior patterns of vessels based on their present motion states. Improvement to the associative learning law for learning temporal associations between vessel events enables conditional probabilities between events to be learned incrementally and locally. This allows weights in the learned model to be interpreted more readily, enabling better location prediction performance. Improvement in prediction performance is achieved by using multiple spatial scales to represent position, enabling the most relevant spatial scale to be used for local vessel behavior. Features and performance of these updates to the learning system using recorded data are described.

SeeCoast is a prototype US Coast Guard (USCG) port surveillance system that provides automated scene understanding support for watchstanders. A major SeeCoast objective is to reduce operator workload while maintaining optimal domain awareness by shifting operators' focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities. Analyst-defined vessel activities are recognized from pre-scripted patterns and anomalous vessel activities are detected using machine learning techniques. The baseline SeeCoast system interfaces to the USCG Hawkeye prototype and uses (a) machine vision technology to produce target tracks from streaming video data; (b) multi-INT fusion technology to correlate radar, Automatic Identification System (AIS), and/or video track data into a single coherent track picture; (c) vessel activity analysis and learning technology to provide alerts for events of interest according to user-defined criteria; and (d) visualization of those alerts embedded within the common operating picture. The video processing component tasks and controls Hawkeye cameras to detect vessels in motion and generates vessel track and classification (based on vessel length) information. SeeCoast detects unsafe, illegal, and threatening vessel activities using a rule-based pattern recognizer and detects anomalous vessel activities on the basis of automatically learned behavior normalcy models. Operators can optionally guide the learning system in the form of examples and counter-examples of activities of interest, and refine the performance of the learning system by confirming alerts or indicating examples of false alarms. This paper focuses on the learning-based activity analysis capabilities of SeeCoast.

Neurobiologically inspired algorithms have been developed to continuously learn behavioral patterns at a variety of conceptual, spatial, and temporal levels. In this paper, we outline our use of these algorithms for situation awareness in the maritime domain. Our algorithms take real-time tracking information and learn motion pattern models on-the-fly, enabling the models to adapt well to evolving situations while maintaining high levels of performance. The constantly refined models, resulting from concurrent incremental learning, are used to evaluate the behavior patterns of vessels based on their present motion states. At the event level, learning provides the capability to detect (and alert) upon anomalous behavior. At a higher (inter-event) level, learning enables predictions, over pre-defined time horizons, to be made about future vessel location. Predictions can also be used to alert on anomalous behavior. Learning is context-specific and occurs at multiple levels: for example, for individual vessels as well as classes of vessels. Features and performance of our learning system using recorded data are described.

This paper addresses maritime situation awareness by using cognitively inspired algorithms to learn behavioral patterns at a variety of conceptual, spatial, and temporal levels. The algorithms form the basis for a system that takes real-time tracking information and uses continuous on-the-fly learning that enables concurrent recognition of patterns of current motion states of single vessels in local vicinity. Learned patterns include routine behaviors as well as illegal, unsafe, threatening, and anomalous behaviors. Continuous learning enables the models to adapt well to evolving situations while maintaining high levels of performance. The learning combines two components: an unsupervised clustering algorithm, and a supervised mapping and labeling algorithm. Operator input can guide system learning. Event-level features of our learning system using simulated and recorded data are described

We have continued development of a system for multisensor image fision and interactive mining based on neural models of color vision processing, learning and pattern recognition. We pioneered this work while at MIT Lincoln Laboratory, initially for colorfirsed night vision (low-light visible and uncooled thermal imagery) and'later extended it to multispectral IR and 30 ladar. We also developed a proof-of-concept system for EO, IR, SAR firsion and mining. Over the last year we have generalized this approach and developed a user- Ji-iendly system integrated into a COTS exploitation environment known as ERDAS Imagine. In this paper, we will summarize the approach and the neural networh used, and demonstrate fusion and interactive mining (i.e., target learning and search) of low-light visible/S WIRhWINL WIR night imagery, and IKONOS multispectral and high-resolution panchromatic imagery. Zn addition, we will demonstrate how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This will be illustrated for the detection of small boats in coastal waters using firsed visible/MW.IIRL WIR imagery.