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Time filter

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Monroeville, PA, United States

Coraluppi S.,Compunetix Inc
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

Fusion of passive electronic support measures (ESM) with active radar data enables tracking and identification of platforms in air, ground, and maritime domains. An effective multi-sensor fusion architecture adopts hierarchical real-time multi-stage processing. This paper focuses on the recursive filtering challenges. The first challenge is to achieve effective platform identification based on noisy emitter type measurements; we show that while optimal processing is computationally infeasible, a good suboptimal solution is available via a sequential measurement processing approach. The second challenge is to process waveform feature measurements that enable disambiguation in multi-target scenarios where targets may be using the same emitters. We show that an approach that explicitly considers the Markov jump process outperforms the traditional Kalman filtering solution. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). Source


Katsilieris F.,Center for Maritime | Braca P.,Center for Maritime | Coraluppi S.,Compunetix Inc
Proceedings of the 16th International Conference on Information Fusion, FUSION 2013 | Year: 2013

The Automatic Identification System (AIS) is an automatic tracking system based on reports provided by the vessels carrying an AIS transponder. The reports contain information on the vessel position, velocity etc. and typically have high accuracy. Given that the AIS is a self-reporting system, the trustworthiness of positional information depends on data being reported by the vessel, rather than measured by a sensor. Any self-reporting system is prone to 'spoofing' or the intentional reporting on incorrect information. This paper addresses the inference problem of whether the received AIS data are trustworthy with the help of radar measurements and information from the tracking system. This problem can be treated in the hypothesis testing framework where the null hypothesis is that the AIS data are trustworthy and the alternative hypothesis is that the data are spoofed. The proposed solution, the generalized version of the sequential log-likelihood ratio test, is compared to the ideally optimal solution using real and simulated data. © 2013 ISIF ( Intl Society of Information Fusi. Source


Trademark
Compunetix Inc. | Date: 2016-06-18

Telecommunications Media Processor for real-time communications (RTC) applications, including conferencing of 3 or more locations simultaneously (i.e. conferencing bridges); or other RTC applications such as those to support multipoint for WebRTC, IMS, High Definition (HD), and secure communications solutions.


Coraluppi S.,Compunetix Inc | Carthel C.,Compunetix Inc
IEEE Aerospace Conference Proceedings | Year: 2014

This paper studies the counting-targets limit of the multiple-hypothesis tracking (MHT) and cardinalized probability hypothesis density (CPHD) solutions to the multi-target tracking (MTT) problem. The solutions are compared with a direct Kalman filtering (KF) solution to the counting-targets MTT problem, whereby we assume a continuous state (number of objects) and assume linear Gaussian measurements. While the enhanced MHT solution - the cardinalized MHT (CMHT) - performs well, it does not match the performance of the KF and of the CPHD. In future work, we will assess these solutions with an RMSE performance metric and examine whether there is any sub-optimality in the CPHD solution to this problem. © 2014 IEEE. Source


Chenouard N.,Institute Pasteur Paris | Chenouard N.,Ecole Polytechnique Federale de Lausanne | Chenouard N.,New York University | Smal I.,Erasmus Medical Center | And 37 more authors.
Nature Methods | Year: 2014

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers. © 2014 Nature America, Inc. Source

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