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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Suo Xiaofeng Chen Li Sheng Andong |
| Copyright Year | 2009 |
| Description | Author affiliation: Automation School, Nanjing University of Science & Technology, 210094, China (Suo Xiaofeng; Chen Li; Sheng Andong) |
| Abstract | 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 rates. In addition, for mobile sensors, their location might not be perfectly known. This paper presents a new algorithm for multisensor bias estimation in asynchronous sensors with sensor location uncertainty. This algorithm is based on a Kalman filter combined with pseudo-measurement and equivalent bias to estimate both the range and azimuth biases. The Simulation results show the Cramer-Rao Lower Bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient. |
| Starting Page | 4317 |
| Ending Page | 4322 |
| File Size | 168396 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427222 |
| DOI | 10.1109/CCDC.2009.5194689 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Uncertainty Target tracking Automation Kalman filter bias estimate Sensor fusion asynchronous sensors Sensor systems Computational complexity Coordinate measuring machines Azimuth equivalent bias sensor location uncertainty pseudo-measurement Error correction State estimation |
| Content Type | Text |
| Resource Type | Article |
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