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Robin Ritter
Senior Managing Officer
Mr. Ritter is a controls and systems engineer with over two decades of experience in end-to-end modeling and simulation of sensor system performance. He currently supports customers analyzing stages of kill chain performance, including sensor coverage, mean time to access, and command and control of Positive ID (PID) systems. He has extensive experience with physics-based synthetic scene generation, developing and using models for active and passive EO/IR signatures, including polarization-maintaining and temperature-dependent BRDF. Mr. Ritter has designed algorithms to track satellites with active (laser-illuminated) systems, processed visible/SWIR/MWIR tactical images of aircraft and ground vehicles, and won multiple Phase I, II and III DoD SBIR contracts for physics-based scene generation using active, passive, and LIDAR/LADAR systems. Mr. Ritter received his B.S. in Mechanical Engineering, Magna Cum Laude, from the University of California at Davis and his M.S. in Mechanical Engineering from MIT.
Nicole Romanoski
Space Modeling and Simulation Engineer
Ms. Romanoski is an experienced engineer with expertise in modeling, simulation and analysis of ground, air and space domains. Her work includes developing and using models for active and passive EO/IR signatures, generating radiometrically-realistic images using Government-owned rendering tools, and analyzing large scale multi-domain simulations. Ms. Romanoski received her B.S. in Physics with an Astrophysics Option, with honors, from the New Mexico Institute of Mining and Technology
Dr. Nick Malone
Senior Scientist
Dr. Nick Malone is a computer scientist with a strong background in sensor data processing, feature extraction, target signature simulation and machine learning. He is the author of an adaptive terrain compositing approach used for 3D rendering. His recent work consists of Machine Learning applied to Computer Vision problems for star streak detection, 3D model reconstruction from monocular camera systems of satellites, UAVs and other aircraft, and 3D rendering of radiometrically correct environments. These Machine Learning approaches are based on Deep Convolutional Neural Networks and Bayesian convergence estimates combined with feature detection. Other projects include multi swarm robotic simulation based on biologically inspired algorithms and kinematically accurate path planning toolsets for simulation environments such as AFSIM. He holds a PhD in computer science. His PhD work was in path planning for robotics under uncertainty with noisy sensors measurement, moving obstacles and reinforcement learning. Dr. Malone received his B.S. and M.S in computer science from the University of Tulsa and received his PhD in computer science at the University of New Mexico.
Publications
Nick Malone et al. “Hybrid dynamic moving obstacle avoidance using a stochastic reachable set-based potential field”. In: IEEE Transactions on Robotics 33.5 (2017), pp. 1124–1138.
Nick Malone et al. “Stochastic reachability based motion planning for multiple moving obstacle avoidance”. In: Proceedings of the 17th international conference on Hybrid systems: computation and control. 2014, pp. 51–60.
Aleksandra Faust, Nick Malone, and Lydia Tapia. “Preference-balancing motion planning under stochastic disturbances”. In:2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE. 2015, 3555–3562.
Luis Varela and Laura E. Boucheron, Nick Malone and Nicholas Spurlock, “Streak detection in wide field of view images using Convolutional Neural Networks (CNNs)”, In AMOS September 2019.
Knight Scientific Systems was founded in 2022 and employs seasoned professionals with experience across multiple scientific and engineering domains.
As long-time providers of scientific and engineering services to the US Government, our team are experts at operations analysis, developing software, and prototyping hardware. We balance reuse of industry standard tools with pioneering new capabilities.
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