Abstract
Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the 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.
Highlights
1.1 Angle of Arrival (AOA) LocalizationMany localization methods incorporate the use of multiple transmitters or receivers
The Bias Drift NLO (BDNLO) was determined to be the most accurate but most likely to diverge while the Bias Drift Modular NLO (BDMod) method showed marginal but regular and stable improvement to the KMAVNLOCI
The BDMod converged in 98.6% of the trials and improved the performance of the KMAVNLOCI for 95% of the trials at an average 20.8% increase in accuracy
Summary
RSS and TDOA methods use multiple measurements from different sensing or transmitting configurations to calculate the location of an object. While these methods have been proven to be an effective method of localization, they require the object to be emitting some sort of RF signal. It calculates the most likely position from the smallest Euclidean distance error from the LOS vectors and not the smallest amount of AOA measurement error This leaves the algorithm prone to increased error when the sensor configuration is bad or if one sensor has more error than another. Most triangulation algorithms do not consider the effect of non-time coincidental measurements on a fast moving object
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