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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 08 | Aug 2026

p-ISSN: 2395-0072

www.irjet.net

Development of a Passive RF Spectrum Intelligence Platform for Drone Signal Detection and Analysis using HackRF One and Python Maj Gagandeep Jaswal

Military College of Telecommunication Engineering (MCTE), Mhow ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - The increasing use of unmanned aerial systems

programmable software implementations. Instead of requiring separate hardware receivers for different communication standards, SDR platforms digitise the received spectrum and implement signal processing algorithms in software. This flexibility enables rapid development of customised RF analysis tools and supports experimentation across multiple communication bands.

(UAS) in military, commercial and civilian applications has significantly increased the importance of passive radio frequency (RF) monitoring for spectrum awareness and authorised signal analysis. Conventional drone detection techniques rely primarily on radar, electro-optical sensors or active RF interrogation, which may not always be suitable for laboratory research, training environments or passive spectrum observation. Software Defined Radio (SDR) offers a flexible and cost-effective approach for analysing RF activity without transmitting energy into the electromagnetic spectrum. This paper presents the design of a Python-based Passive RF Spectrum Intelligence Platform using HackRF One for real-time spectrum monitoring, RF signal visualisation and candidate signal analysis. The proposed software continuously scans user-defined frequency ranges, performs Fast Fourier Transform (FFT)-based spectrum analysis, generates waterfall visualisation and records candidate RF events for subsequent analysis. The platform incorporates a graphical user interface developed using PyQt6 together with SQLite-based event management to organise signal metadata and recorded IQ data. The modular software architecture enables future integration of advanced signal classification algorithms while maintaining separation between passive RF observation and protocol-specific decoding. The proposed platform is intended for authorised spectrum monitoring, education and software defined radio research.

HackRF One is a widely used SDR platform capable of receiving signals over a broad frequency range extending from approximately 1 MHz to 6 GHz. Combined with Python and modern scientific computing libraries, HackRF One provides an accessible platform for implementing spectrum analysis, RF signal visualisation and data acquisition systems. Python libraries such as NumPy, SciPy, PyQt6 and PyQtGraph enable development of sophisticated graphical interfaces together with real-time digital signal processing algorithms.

Keywords: Software Defined Radio, HackRF One, Passive RF Monitoring, Spectrum Intelligence, FFT, Waterfall Display, IQ Recording, Python, Electromagnetic Spectrum Operations.

The objective of this work is to design a passive RF spectrum intelligence platform capable of continuously monitoring selected frequency bands, visualising spectrum occupancy, identifying candidate RF transmissions and maintaining a searchable database of observed events. Rather than attempting universal decoding of proprietary or encrypted communication protocols, the proposed platform concentrates on passive spectrum observation, RF signal characterisation and recording of IQ samples for authorised offline analysis. This design philosophy provides a flexible foundation for future enhancement while maintaining compatibility with laboratory research and educational applications.

1. INTRODUCTION

2. Literature Review

The electromagnetic spectrum has become one of the most valuable operational resources supporting communication, navigation, surveillance, intelligence gathering and command-and-control functions. Modern wireless systems occupy increasingly congested portions of the radio spectrum, making efficient spectrum monitoring essential for both research and operational applications. Among these wireless platforms, unmanned aerial systems (UAS) represent one of the fastest-growing technologies and employ a variety of RF communication techniques for control, telemetry and payload transmission.

Drone detection technologies have traditionally relied upon radar, acoustic sensors, electro-optical imaging and RF monitoring systems. Radar systems provide long-range detection capabilities but often encounter challenges when observing small unmanned aerial vehicles possessing limited radar cross sections. Electro-optical and infrared sensors offer high target identification accuracy under favourable environmental conditions but their effectiveness may degrade during adverse weather, poor illumination or lineof-sight obstruction. Passive RF monitoring has emerged as an important complementary technology because it does not require transmission of electromagnetic energy. Instead, passive systems analyse naturally occurring RF emissions generated

Software Defined Radio (SDR) has transformed RF research by replacing dedicated hardware processing with

Impact Factor value: 8.315

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