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Source localization and calibration using TDOA and FDOA measurements in the presence of sensor location uncertainty

Science China(Information Sciences) 2014年第4期57卷 156-167页

作者： LI JinZhou GUO FuCheng JIANG WenLi School of Electronics Science and Engineering National University of Defense Technology

Source localization accuracy is very sensitive to sensor location *** paper performs analysis and develops a solution for locating a moving source using time difference of arrival(TDOA)and frequency difference of arrival(FDOA)measurements with the use of a calibration *** a Gaussian random signal model,we first derive the Cram′er-Rao lower bound(CRLB)for source location estimate in this *** we analyze the differential calibration technique which is commonly used in Global Positioning *** is indicated that the differential calibration cannot attain the CRLB accuracy in most cases.A closed-form solution is then proposed which takes a calibration emitter into account to reduce sensor location *** is shown analytically that under some mild approximations,our approach is able to reach the CRLB *** simulations are included to corroborate the theoretical developments.

关键词： source localization calibration time difference of arrival(TDOA) frequency difference of arrival(FDOA) sensor location uncertainty Cram′er-Rao lower bound(CRLB)

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Asynchronous Multi-sensor Bias Estimation with sensor location uncertainty

Asynchronous Multi-sensor Bias Estimation with Sensor Locati...

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2009中国控制与决策会议

作者： Suo Xiaofeng,Chen Li,Sheng Andong Automation School,Nanjing University of Science & Technology,Nanjing 210094

＜正＞In multi-sensor systems,a practical problem is that the target data reported by the sensors are usually not time-coincident or synchronous due to the different data *** addition,for mobile sensors,their location might not be perfectly *** paper presents a new algorithm for multisensor bias estimation in asynchronous sensors with sensor location *** algorithm is based on a Kalman filter combined with pseudo-measurement and equivalent bias to estimate both the range and azimuth *** Simulation results show the Cramer-Rao Lower Bound（CRLB） is *** means the proposed estimation algorithm is statistically efficient.

关键词： asynchronous sensors sensor location uncertainty bias estimate pseudo-measurement equivalent bias Kalman filter.

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