ICMLC 2017 : 19th International Conference on Machine Learning and Computing Vancouver, Canada

1. Design and Test a Robust Bearing-Only Target Motion Analysis
Algorithm Based on Modified Gain Extended Kalman Filter
Mohammad Tarek Al Muallim
Ozhan Duzenli
Ceyhun Ilguy
ICMLC 2017 : 19th International Conference on Machine Learning and Computing
Vancouver, Canada

2. Problem Description and Challenges
▪ Multi-beam passive sonar.
▪ Line of sight angle (bearing) is the only available information.
▪ Measured bearing is very noisy.
▪ No effective method that could be used to track an unknown target.
▪ A need for a fast and reliable target motion analysis algorithm.
▪ A need for an online confidence level measurement.
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3. Method
▪ Design a Total Bearing-only Target Motion Analysis System including:
▪ The prediction model.
▪ The tracking algorithm: Modified Gain Extended Kalman Filter.
▪ Optimal observer maneuver.
▪ Online performance indicators and confidence level measurement.
▪ Maneuvering target detector.
▪ Tests done through simulation:
▪ The simulator model a discrete scenario for an observer and a target.
▪ Takes in consideration all the practical aspects of the problem such as:
▪ Smooth transition in the speed.
▪ Circular turn of the ship.
▪ Noisy measurements.
▪ Quantized bearing measurement came for multi-beam sonar.
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4. Results
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5. Results
▪ Full tracking within 10 minutes, with very little error.
▪ Accumulated estimated bearing error less than 0.1 degree.
▪ Real bearing error less than 0.5 degree.
▪ Range absolute error percent less than 5%.
▪ Speed absolute error percent less than 5%.
▪ Heading error less than 2 degree.
▪ Online performance estimator is aligned with the real performance.
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