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Accession Number ADA562500
Title All Source Sensor Integration Using an Extended Kalman Filter.
Publication Date Mar 2012
Media Count 151p
Personal Author T. R. Penn
Abstract The global positioning system (GPS) has become an ubiquitous source for navigation in the modern age, especially since the removal of selective availability at the beginning of this century. The utility of the GPS is unmatched, however GPS is not available in all environments. Heavy reliance on GPS for navigation makes the warfighter increasingly vulnerability as modern warfare continues to evolve. This research provides a method for incorporating measurements from a massive variety of sensors to mitigate GPS dependence. The result is the integration of sensor sets that encompass those examined in recent literature as well as some custom navigation devices. A full-state extended Kalman filter is developed and implemented, accommodating the requirements of the varied sensor sets and scenarios. Some 19 types of sensors are used in multiple quantities including inertial measurement units, single cameras and stereo pairs, 2D and 3D laser scanners, altimeters, 3-axis magnetometers, heading sensors, inclinometers, a stop sign sensor, an odometer, a step sensor, a ranging device, a signal of opportunity sensor, global navigation satellite system sensors, an air data computer, and radio frequency identification devices. Simulation data for all sensors was generated to test filter performance. Additionally, real data was collected and processed from an aircraft, ground vehicles, and a pedestrian. Measurement equations are developed to relate sensor measurements to the navigation states. Each sensor measurement is incorporated into the filter using the Kalman filter measurement update equations. Measurement types are segregated based on whether they observe instantaneous or accumulated state information. Accumulated state measurements are incorporated using delayed-state update equations. All other measurements are incorporated using the numerically robust UD update equations. Simulation results show the expected performance of improved navigation state estimation over time w.
Keywords Detectors
Global positioning system
Ground vehicles
Identification systems
Inertial measurement units
Kalman filtering
Navigational aids
Three dimensional

Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
76D - Navigation Systems
Corporate Author Air Force Inst. of Tech., Wright-Patterson AFB, OH. Graduate School of Engineering and Management.
Document Type Thesis
Title Note Master's thesis.
NTIS Issue Number 1225
Contract Number N/A

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