A method for estimating signal parameters of UAV remote controller in different frequency scenarios
The rapid development of drones not only brings help and convenience to all walks of life,but also brings serious impacts and threats to civil aviation safety,social security and even the ecological environment.Therefore,the control and countermeasures of drones have become urgent,and the estimation of the parameters of the remote controller signal of drones is an important technical means to control and counter drones.To solve this problem,a signal parameter estimation method of unmanned aerial vehicle(UAV)remote control in different frequency scenarios is proposed.In this method,the signal is transformed into time-frequency graph by using spectral graph transformation,and the frequency spectrum of each time slot is interpolated to improve the resolution of frequency domain.The threshold adaptive denoising algorithm and K-means algorithm are combined to suppress the noise components,and the parameters such as take-off time,frequency hopping period,center frequency and total bandwidth are estimated.Based on the measured data,the parameter estimation performance of the proposed method is compared with that of the two traditional methods.The experimental results show that the proposed method has obvious advantages in the performance of the above parameter estimation.
unmanned aerial vehicle(UAV)frequency hopping(FH)parameter estimationfully connected neural networkdifferent frequency
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