Drones have undergone tremendous development in recent years. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber-attacks to infrastructure, private/public property, and people. Their great agility commences new threa
Automated drone detection and targeting system
Drones have undergone tremendous development in recent years. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber-attacks to infrastructure, private/public property, and people. Their great agility commences new threats in security and public privacy. The small size makes it effortless to perform challenging missions very precisely. The radar system provides good capabilities at long ranges but the limitation to these systems is their inability to sense smaller and slower aircrafts flying at lower altitudes. Therefore, the aim of this project is to develop an integrated real-time drone detection system that can detect an Unmanned Aerial Vehicle(UAV) based on Real-time frequency monitoring for all frequencies and coordinates ( Heading, Height) at the same time and interpret the images of it in terms of polar coordinates. Initially, neural networks will be used to train the detection and tracking algorithm. As a result the trained algorithms will be tested and validated for real-time drones infiltration scenarios The proposed system develops a model which detects a UAV that attempts to trespass the secured territory at low altitude, providing its precise location, calculating the distance and targeting the UAV.
The core objectives of this project include:
The internal architecture of the proposed model will work on these four basic step
This phase of the project handles the capturing of image through several cameras. Once we have the image we will only detect the aerial objects.
2. Identification
Once we have detected the aerial objects in an image, we will further proceed towards the identification of the UAV’s from other flying objects.
3. Tracking
As we have distinguished the UAV from other aerial objects we can then track the movement of the target trespassing the premises.
4. Targeting
The last stage includes the targeting of the UAV through a mock laser system.
Benefitis of the project are as folllows
Final deliverable consists of a trained model which can detect UAV in low range, which will be displayed on real time monitoring screen. This will help the user to identify and target the threat with the help of this system. Key deliverable are as follows:
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Cameras | Equipment | 4 | 5000 | 20000 |
| Acoustic Sensors | Equipment | 10 | 200 | 2000 |
| GPU unit | Equipment | 1 | 35000 | 35000 |
| Screen | Equipment | 1 | 4000 | 4000 |
| Stands | Equipment | 4 | 1500 | 6000 |
| Cables/wires | Equipment | 20 | 100 | 2000 |
| Miscellaneous | Miscellaneous | 1 | 2000 | 2000 |
| Total in (Rs) | 71000 |
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