Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Andrew J. Terzuoli, PhD.


Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to locate the most likely location of a signal. When an object does not emit a classical RF signal, Angle of Arrival (AOA) measurements become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of camera. When location and attitude knowledge of the sensor passive objects can be tracked. A Non-Linear Optimization (NLO) method for calculating the most likely estimate from AOA measurements has been created in previous work. This thesis, modifies that algorithm to automatically correct AOA measurement errors by estimating the inherent bias and timedrift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. Two methods are created to correct the sensor bias. One method corrects the sensor bias in post processing while treating the previous NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm. These methods appear to improve performance by 10 − 60% depending on the data.

AFIT Designator


DTIC Accession Number