Multi-sensor monitoring relies on smart sensors and smart algorithms. In addition to a long history of working with advanced computing and signal processing algorithms Sensigent has contributed to recent state of the art funded R&D programs that focused on multi-sensor monitoring and data fusion under the most difficult conditions. When signals are weak or masked by other agents Sensigent detection, location and identification algorithms have enabled significant performance improvements. Using new Sequential Monte Carlo Methods as well as more established techniques such as Support Vector Method and Principal Components Analysis Sensigent technology is able to meet the requirement to discriminate, interpret and display actionable information.

Sensigent staff members are experts in the application of advanced signal processing technology, e.g., Bayesian belief networks and state-of-art extensions to naïve Bayes classifiers, to increase the effectiveness of sensors used for homeland security and other applications. Our advanced signal processing and sensor data fusion algorithms have improved the effectiveness of COTS sensors by up to 40% as proven in rigorous long-term operational assessments at National Laboratories and elsewhere.