Use of measurement noise correlations for an improved SONAR model

• Published in: 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy) pp. 1 - 6
• Main Authors: Sekar, Ramanan, Shankar, N Sai, Shankar, B Shiva, Manivannan, P V
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Correlation; Covariance matrices; Gaussian Correlation Inequality; Maximum Likelihood Estimation algorithm; Pressure sensors; Robot sensing systems; Sonar measurements; SONAR sensor
• DOI: 10.1109/TAPENERGY.2017.8397253

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.