Building an AI System to Improve Tracking Accuracy in Air Surveillance

Abstract

Finding or tracking the location of an object with precision and accuracy is a crucial problem in defence applications, robotics and computer vision. Radars fall into the spectrum of high-end defence sensors or systems upon which the security and surveillance of the entire world depends. There has been a lot of focus on Multi Sensor Tracking (MST) in recent years, with radars as the sensors. The Indian Air Force (IAF) uses a MST system to detect flights pan India, developed and supported by Bharat Electronics Limited (BEL), a defence agency that we are working with. In this thesis, we describe the Machine Learning approaches, which are built on top of the existing system the Air force uses currently. We have used 3 Machine Learning approaches, the first on a smaller dataset and the second and third on a bigger dataset. Each subsequent approach does a better job of reducing the errors faced by the system. The final approach trained our models on about 11 million anonymized real Multi Sensor tracking data points provided by radars performing tracking activity across the Indian air space. This approach has shown an increase in tracking accuracy by 5.5% from 91.5% to 97%. The model and the corresponding code were transitioned to BEL, which have been tested in their simulation environment with a plan to take forward for ground testing. Our final approach comprises of 3 steps: (a) We train a Neural Network and a CatBoost model and ensemble them using a Logistic Regression model to predict one type of error, namely Splitting error, which can help to improve the accuracy of tracking. (b) We again train a Neural Network and a CatBoost model and ensemble them using a different Logistic Regression model to predict the second type of error, namely Merging error, which can further improve the tracking accuracy. (c) We use cosine similarity to find the nearest neighbour and correct the data points predicted to have Splitting/Merging errors by predicting the original global track of these data points.