Use of measurement noise correlations for an improved SONAR model
Using SONAR as the primary range finding sensor has largely been abandoned due to problems such as limited range, large bearing errors and large beam widths. However, SONAR is used conjunction with other sensors such as LIDARs, RADARs and vision sensors for ranging and obstacle avoidance in many aut...
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Published in | 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy) pp. 1 - 6 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.12.2017
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Subjects | |
Online Access | Get full text |
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Summary: | Using SONAR as the primary range finding sensor has largely been abandoned due to problems such as limited range, large bearing errors and large beam widths. However, SONAR is used conjunction with other sensors such as LIDARs, RADARs and vision sensors for ranging and obstacle avoidance in many autonomous vehicle applications. In this paper, we propose a solution to reduce the range and bearing error significantly, and thus improve the performance of the SONAR. Using the results from the Gaussian Correlation Inequality, we derive probabilistic transformations that can improve the range and bearing measurement of the SONAR, thus reducing the sensor error. We are also presenting simulation study, to place bounds on the types and characteristics of the SONARs within which our model's performance is optimal. |
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DOI: | 10.1109/TAPENERGY.2017.8397253 |