Learning of a tracker model from multi-radar data for performance prediction of air surveillance system

Ruotsalainen, Marja; Jylhä, Juha

doi:10.1109/cec.2017.7969562

Cited by 4 publications

(1 citation statement)

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“…Tracking of objects during air surveillance is typically performed using radars across most countries of the world since they work best in long distances and all kinds of weather.A radar (Ruotsalainen and Jylhä 2017) is an electromagnetic system that is useful for large scale surveillance. It can be viewed as an active sensor that can detect targets by emitting large power electromagnetic signals and also determines the targets' angle, range or velocity.…”

Section: Introductionmentioning

confidence: 99%

CB+NN Ensemble to Improve Tracking Accuracy in Air Surveillance

Dasika

Paruchuri

2022

AAAI

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Finding or tracking the location of an object accurately is a crucial problem in defense applications, robotics and computer vision. Radars fall into the spectrum of high-end defense sensors or systems upon which the security and surveillance of the entire world depends. There has been a lot of focus on the topic of Multi Sensor Tracking in recent years, with radars as the sensors. The Indian Air Force uses a Multi Sensor Tracking (MST) system to detect flights pan India, developed and supported by BEL(Bharat Electronics Limited), a defense agency we are working with. In this paper, we describe our Machine Learning approach, which is built on top of the existing system, the Air force uses. For purposes of this work, we trained our models on about 13 million anonymized real Multi Sensor tracking data points provided by radars performing tracking activity across the Indian air space. The approach has shown an increase in the accuracy of tracking by 5 percent from 91 to 96. The model and the corresponding code were transitioned to BEL, which has been tested in their simulation environment with a plan to take forward for ground testing. Our approach comprises of 3 steps: (a) We train a Neural Network model 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 model 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 accuracy of tracking. (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.

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Section: Introductionmentioning

confidence: 99%