NON LINEAR OPTIMIZATION APPLIED TO ANGLE-OF-ARRIVAL SATELLITE BASED GEO-LOCALIZATION FOR BIASED AND TIME-DRIFTING SENSORS

被引：0

作者：

Levy, Daniel ^{[1
]}

Roos, Jason ^{[1
]}

Robinson, Jace ^{[1
]}

Carpenter, William ^{[1
]}

Martin, Richard ^{[1
]}

Taylor, Clark ^{[1
]}

Sugrue, Joseph ^{[1
]}

Terzuoli, Andrew ^{[1
]}

机构：

[1] Inst Elect & Elect Engineers, Piscataway, NJ 08854 USA

来源：

XXIII ISPRS CONGRESS, COMMISSION II | 2016年 / 41卷 / B2期

关键词：

Angle of Arrival; Line of Sight; Non-Linear Optimization; Bias; Time-Drift; Geo-location; Passive Tracking;

D O I：

10.5194/isprsarchives-XLI-B2-319-2016

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

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.

引用

页码：319 / 325

页数：7

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