Yu. V. Koltzov1

1 Nizhegorodskiy Research Institute (Nizhny Novgorod, Russia)
1 koltzovyv@mail.ru

In connection with the widespread use of unmanned aerial vehicles (UAVs), there is a need to optimize the size, weight and consumption of modern UAVs location systems with an emphasis on transceiver modules using integrated radar solutions in the form of systems-on-a-chip, using the enormous capabilities of artificial intelligence (AI).

The development of location systems has led to the emergence of high-resolution four-dimensional radar-on-a-chip (RCC) systems that provide measurements of range, speed, azimuth and elevation (altitude). Several RCCs are used, each of which performs its own functions, and all of them are combined into a location system network (networks-on-a-chip). Such networks promise enhanced object detection capabilities due to network diversity and better angular resolution, particularly if the network covers an aperture much larger than that of a single radar.

It is important to note that silicon RCCs, due to their minimal size, weight and consumption, can be used in UAV location systems.
AI has become ubiquitous in radars and is becoming an integral part of the entire computing infrastructure, creating demand for more computing power and significantly expanding the capabilities of UAVs.

The latest 4D RCCs provide azimuth, altitude, range and relative velocity measurements, identification of nearby objects, 2D velocity estimation and 360° visibility, simultaneous tracking of multiple targets, and also implement mapping, creating a 3D shape of objects, tracking them and classifying them, localizing the media on the map. The number of applications is constantly growing.

4D radars have increased the angular resolution required for target separation by an order of magnitude. As a result, the resolution reached 0.1 degrees for horizontal and vertical angles due to MIMO scaling. Increasing the number of transmitting and receiving antennas makes it possible to obtain a larger aperture of the radar virtual antenna array.

Improving the radar architecture, as well as the use of AI and machine learning based on powerful processing systems using highly efficient algorithms, ensures the autonomy of UAVs, their operation in unknown conditions, and movement in a group while avoiding obstacles, which guarantees high secrecy of actions.

To provide all-round visibility, several radars are used, each of which performs its own functions and has its own viewing angle, and all radars are combined into a network.

The implementation of new capabilities of UAVs required a significant increase in the computing power of the radar. The use of AI requires even more computing power and memory resources. Cloud computing also increases energy consumption dramatically. The solution may be to centralize computing.

The use of software-defined systems for UAVs is increasing because it allows for architectural flexibility and the best results in different operating modes.

Koltzov Yu.V. Unmanned aerial vehicle location systems. Breakthrough Transceiver and Signal Processing Solutions. Nanotechnology: development and applications – XXI century. 2025. V. 17. № 4. P. 51–64. DOI: https://doi.org/10.18127/ j22250980-202504-06 (in Russian)

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