Modeling and Simulation of Real Time RF Based Drone Detection and Tracking
Implementation plan:
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Step 1: Initially, we load the DroneRF Dataset for the Drone Signal Data.
Step 2: Next, we preprocess the Data and extract the features based on the signals from the dataset by using the STFT Algorithm.
Step 3: Next, we train the Drone data to classify Drone vs. Non-drone signals by using CNN-LSTM Hybrid Algorithm.
Step 4: Next, we implement the Real-Time RF Signal by means of Software Defined Radio and Detect the Signals by using FFT with CNN Algorithm.
Step 5: Finally, we plot the performance metrics such as
5.1) Frequency (Hz) Vs. Signal Strength (dB)
5.2) Number of Epochs Vs. Accuracy (%)
5.3) Number of Epochs Vs. Precision (%)
5.4) Signal length (ms) Vs. Processing Time (ms)
Software Requirements:
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1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows-10 (64-bit) or above
Dataset:
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Link: DroneRF dataset: A dataset of drones for RF-based detection, classification, and identification – Mendeley Data
Note:
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[1] If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
[2] Please note that this implementation plan does not include any further steps after it is put into implementation.
[3] Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
[4] If the above plan satisfies your requirement please confirm with us.
[5] Project based on Simulation only, not a real time project.
