Abstract
The multi-source fusion positioning, which utilize kinds of signals in 2.4 G ISM band, requires a variety of special sensors to identify the signal type. This leads to low utilization rate of equipment resources, high equipment cost and the difficulty of optimizing the location signal source. In this paper, an ISM band multi-source signal sensing method is proposed, we use Short-time Fourier transform (STFT) to transform signal data into time-frequency images dimensional. Based on the SVM image classification method, the time-frequency image features of each signal are extracted, then the classifier is designed to recognize the unknown positioning signals within 100 MHz bandwidth. The method has been tested by Simulink simulation in a Gaussian noise environment. The results show that compared to traditional single time/frequency domain signal detection, this approach maintains recognition performance no need to equip sensors for each type of signal.
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Acknowledgements
This research was supported by the National Key Research and Development Program of China (Grant: 2016YFB0502001). This research is also received support from the Provincial Science and Technology Plan (2017B090908005).
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Deng, Z., Qi, H., Hu, E., Liu, Y. (2021). ISM Band Multi-source Signal Perception Based on Time-Frequency Image Feature Analysis. In: Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC 2021) Proceedings. Lecture Notes in Electrical Engineering, vol 773. Springer, Singapore. https://doi.org/10.1007/978-981-16-3142-9_57
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DOI: https://doi.org/10.1007/978-981-16-3142-9_57
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