Coulter D.,Modern Technology Solutions, Inc.
AUVSI Unmanned Systems 2013 | Year: 2013
Integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) continues to face many hurdles. While the current focus has shifted somewhat to the privacy issue, substantiating the safety of UAS sense and avoid (SAA) systems continues to be a major issue that must be resolved. From a technological point of view, making the case for SAA system safety should be no different from the application of well-established certification processes used for systems on manned aircraft. In fact, many of those processes do have direct applicability and can be used to substantiate many of the SAA system properties; but even after all those safety assessments are complete there remains one big question facing unmanned aircraft integration: "What is the expected midair collision level of safety provided by the SAA system?" Because SAA is intended to function as a safety-critical system providing an alternate means of compliance with Part 91 see-and-avoid requirements, the question is valid but there is no equivalent safety substantiation methodology or performance standard against which to evaluate the result. This paper identifies the differences between manned and unmanned aircraft safety substantiation as they apply to SAA and offers a proposed methodology for determining the midair collision risk for a UAS using an SAA system implementation. Although the goal of a system developer is to establish SAA system safety requirements, the methodology looks at the NAS from a systems-of-systems perspective to calculate an overall midair collision rate considering the contributions to safety provided by the UAS SAA system, intruder aircraft pilot and systems, air traffic control instructions, and airspace procedures. This result can then be used as a basis for allocation of risk to each mitigator as a step in determining required SAA system performance. Source
Modern Technology Solutions, Inc. | Date: 2012-12-28
A visual inspection system includes a database storing a wireframe model of an object and a portable electronic device equipped with an imaging device and a display. The portable electronic device is in communication with the database. The portable electronic device is configured to show on the display the wireframe model as an overlay to an image of the object taken by the imaging device. The display is configured to accept input of a trace of a defect on the display, and displays the trace on the image. A method of transmitting electronic data from an unsecure device to a secure database is also described.
Downing B.H.,Modern Technology Solutions, Inc.
Institute of Navigation International Technical Meeting 2016, ITM 2016 | Year: 2016
The landscape of Global Navigation Satellite Systems (GNSS) signals is undergoing fundamental change. The parallel development of new, more robust and versatile signals along with two new GNSS systems with global reach is having a dramatic impact on the development of consumer and professional grade GNSS receivers. Receiver manufacturers are busily developing and implementing unique signal acquisition and tracking algorithms, advanced integrity monitoring algorithms, advanced multipath mitigation algorithms and a host of other enhancements in an effort to improve the performance of GNSS receivers and make their products stand out in a crowded field. The objective of this research is to develop a methodology for comparing the signal tracking performance of GNSS receivers as a way to evaluate the effectiveness of their unique designs and to compare the performance of various receivers. This research focuses mainly on the design, performance and evaluation of a GNSS receiver's pseudorandom noise (PRN) code tracking loops. Source
Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase II | Award Amount: 649.99K | Year: 2008
Accurate collateral damage assessment is of critical importance for missile defense because determining the likelihood of a successful intercept kill is required for efficient interceptor tasking. This is especially critical for first generation missile defense systems that are likely to only have a limited number of interceptors available to address a very wide spectrum of threats. The primary goal of kill assessment is mapping the observed sensor signature to the likely post-impact physical state of the target and all of the meaningful debris (fragments) generated from the collision. The Missile Defense Agency (MDA) has a requirement to develop advanced numerical simulation codes and related expertise to accurately predict fragmentation and thermofluid dynamics from kill vehicle hypervelocity impacts on reentry vehicles, which can be compared with sensor signature data collected during MDA intercept experiments. The SBIR Phase 2 research proposed here will conduct a two year computational investigation expanding the SBIR Phase 1 efforts to optimize model accuracy, conduct parametric study, establish mathematical relationship between fragmentation state (spatial and thermal) and impact parameters, utilize more than one computer code to verify accuracy and reliability, and compare simulation results with observed sensor data from MDA intercept experiments.
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 729.97K | Year: 2008
The objective of this proposal is to extend and build on our Phase 1 research to create a hierarchical Aided Target Recognition (AiTR) capability utilizing High Range Resolution (HRR) RADAR profiles. We will continue to refine our utilization of Hidden Markov Models (HMMs) as the basis of the hierarchical AiTR software, but will devote a portion of the program resources to explore alternative models, features and algorithms that may provide superior performance. The strategic vision is to develop a principled and conservative capability that is fully understood, methodically refined and can be depended on for a good level of performance, while pursuing promising but more speculative approaches that could yield significantly superior performance. The HMM-based system is the benchmark against which any alternative methodology will be compared. At all times, alternative methods will be compared against this benchmark and will only be adopted if significantly greater performance is demonstrated while taking into consideration any differences in complexity or required computational power.