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Accuracy Improvement of Closed-Form TDOA Location Methods Using IMM Algorithm

For target location and tracking in wireless communication systems, mobile target positioning and tracking play an important role.
Since multi-sensor system can be used as an efficient solution to target positioning process, more accurate target location estimation and tracking results can be obtained.
However, both the deployment of designed multi-sensor and location algorithm may affect the overall performance of position location.
In this thesis, based on the time difference of arrival (TDOA), two closed-form least-square location methods, spherical-interpolation (SI) method
and spherical-intersection (SX) method are used to estimate the target location. The two location methods are different from the usual process of
iterative and nonlinear minimization.
The locations of the target and the designed multiple sensors may yield geometric effects on location performance.
The constraints and performance of the two location methods will first be introduced.
To achieve real-time target tracking, the Kalman filtering structures are used to combine the SI and SX methods.
Because these two positioning and tracking systems have different and complementary performance inside and outside the multi-sensor array, we consider using data fusion to improve location estimation results by using interacting multiple model (IMM) based estimator, in which internal filters running in parallel are designed as the SX-KF1 and the SI-KF2. However, due to the time-varying characteristics of measurement noises, we propose an adjusting scheme for measurement noise variance assignment in the Kalman filters to obtain improved location estimation results. Simulation results are obtained by running Matlab program.
In three-dimensional multi-sensor array scenarios, the
results of moving target location estimation shows that the IMM-based estimators effectively improve the position performance.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0831110-194331
Date31 August 2010
CreatorsChen, Guan-Ru
ContributorsShiunn-jang Chen, Chin-Der Wann, King-chu Hung, Hsin-hsyong Yang
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0831110-194331
Rightsnot_available, Copyright information available at source archive

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