Apparatus and methods for detecting an anomaly in a dataset by using two or more anomaly detection algorithms, as well as to corresponding computer program products, are described. The results obtained by using the two or more anomaly detection algorithms are combined in accordance with a certain rule of combination, thereby providing an improved accuracy of anomaly detection.

There are provided mechanisms for determining an uncertainty measure of a mixture-model based parametric classifier. A method is performed by a classification device. The method includes obtaining a short-term frequency representation of a multimedia signal. The short-term frequency representation defines an input sequence. The method includes classifying the input sequence to belong to one class of at least two available classes using the parametric classifier. The parametric classifier has been trained with a training sequence. The method includes determining an uncertainty measure of the classified input sequence based on a relation between posterior probabilities of the input sequence and posterior probabilities of the training sequence.

A method for observing a predetermined monitoring area, wherein one or more sensing platform nodes are employed to observe a predetermined number of sub-areas of the monitoring area, includes observing the sub-areas of the monitoring area using the sensing platform nodes so as to collect measuring data for the sub-areas. A prediction model is provided for analyzing predictability of measuring data for the sub-areas based on the collected measuring data. Future measuring data is calculated for the sub-areas and uncertainty of the future measuring data over time is calculated using the prediction model. The sensing platform nodes are scheduled for observation of the sub-areas according to a scheduling mechanism. The scheduling of the sensing platform nodes is dependent on the calculated uncertainty of the future measuring data predicted for the sub-areas.

The present invention encompasses a physical or virtual, computational, analysis, fusion and correlation system that can automatically, systematically and independently analyze collected sensor data (upstream) aboard or streaming from aerial vehicles and/or other fixed or mobile single or multi-sensor platforms. The resultant data is fused and presented locally, remotely or at ground stations in near real time, as it is collected from local and/or remote sensors. The invention improves detection and reduces false detections compared to existing systems using portable apparatus or cloud based computation and capabilities designed to reduce the role of the human operator in the review, fusion and analysis of cross modality sensor data collected from ISR (Intelligence, Surveillance and Reconnaissance) aerial vehicles or other fixed and mobile ISR platforms. The invention replaces human sensor data analysts with hardware and software providing two significant advantages over the current manual methods.

There are provided mechanisms for determining an uncertainty measure of a mixture-model based parametric classifier. A method is performed by a classification device. The method includes obtaining a short-term frequency representation of a multimedia signal. The short-term frequency representation defines an input sequence. The method includes classifying the input sequence to belong to one class of at least two available classes using the parametric classifier. The parametric classifier has been trained with a training sequence. The method includes determining an uncertainty measure of the classified input sequence based on a relation between posterior probabilities of the input sequence and posterior probabilities of the training sequence.

The present invention encompasses a physical or virtual, computational, analysis, fusion and correlation system that can automatically, systematically and independently analyze collected sensor data (upstream) aboard or streaming from aerial vehicles and/or other fixed or mobile single or multi-sensor platforms. The resultant data is fused and presented locally, remotely or at ground stations in near real time, as it is collected from local and/or remote sensors. The invention improves detection and reduces false detections compared to existing systems using portable apparatus or cloud based computation and capabilities designed to reduce the role of the human operator in the review, fusion and analysis of cross modality sensor data collected from ISR (Intelligence, Surveillance and Reconnaissance) aerial vehicles or other fixed and mobile ISR platforms. The invention replaces human sensor data analysts with hardware and software providing two significant advantages over the current manual methods.

A method for observing a predetermined monitoring area, wherein one or more sensing platform nodes are employed to observe a predetermined number of sub-areas of the monitoring area, includes observing the sub-areas of the monitoring area using the sensing platform nodes so as to collect measuring data for the sub-areas. A prediction model is provided for analyzing predictability of measuring data for the sub-areas based on the collected measuring data. Future measuring data is calculated for the sub-areas and uncertainty of the future measuring data over time is calculated using the prediction model. The sensing platform nodes are scheduled for observation of the sub-areas according to a scheduling mechanism. The scheduling of the sensing platform nodes is dependent on the calculated uncertainty of the future measuring data predicted for the sub-areas.