The problems caused by commercial drones in air traffic, airports, and vital and military installations have increased the demand for drone detection and tracking systems. An acoustic beamforming system that tracks audio sources using 256 microphones in real-time was extended to detect and track drones. This thesis studied software-defined, multi-channel, real-time filtering solutions to improve the systems' drone detection and tracking capabilities. The sound frequencies of drone sound and disturbance noise were analyzed to create a suitable filter. Methods for implementing this filter on all channels while still operating in real-time were studied. SIMD intrinsics were used to create a few candidate algorithms, and a GPU algorithm was created as well. These algorithms were compared to each other based on execution time metrics, and the system was also analyzed for performance degradation and placement of the filtering algorithms. The results of the isolated execution time of the filtering algorithms showed the best SIMD algorithm to be operating at 0.41 milliseconds and the GPU algorithm at 0.12 milliseconds when filtering 256 samples from all 256 channels. The real-time constraint was around 5.2 milliseconds, meaning both solutions operated well below the limit. The results of the system's drone detection and tracking capabilities, when placed outdoors in a windy environment, showed the system clearly finding the drone 48% of the time without any filtering and 89% of the time with filtering. The signal-to-noise ratio was also improved by 21dB by using this filter. The results show that a software-defined multi-channel, real-time filter operating on a large data stream is a viable solution to real-time DSP applications. When specializing a beamforming application in tracking a desired frequency, filtering was revealed to be a good solution.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-531286 |
| Date | January 2024 |
| Creators | Teglund, Jonas |
| Publisher | Uppsala universitet, Datorteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC IT, 1401-5749 ; 24006 |
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