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Micro-Doppler methods of detecting and classifying small UAVs are limited in range due to the weak radar returns from their plastic propellers. Smaller windows of data instead of longer windows are used for detection as stationarity assumptions often fail for longer windows. Traditional non-parametric methods may be inadequate as they have limited spectral resolution with smaller windows and may provide false detection when radar returns are weak. A rotary drone detector using the number of Helicopter Rotation Modulation (HERM) lines is considered in this paper. Two parametric methods for estimating the number of HERM lines, Minimum Description Length (MDL) and Akaike Information Criterion (AIC), are considered for detection purposes. Experiments using real data acquired using a micro-helicopter drone and a commercial ultra-wide band radar reveal that MDL performs significantly better than AIC and the traditional Fourier-based non-parametric estimation methods.

Energy Internet is a hot spot of research and practice in the field of energy at home and abroad. DC power distribution technology is an important element of energy Internet. How to plan the type and capacity of energy storage system (ESS) is a problem worthy of attention when designing the optimal energy conversion path in the energy Internet, especially in the DC distribution network. The proper type and capacity of ESS can effectively suppress DC voltage flicker and improve the system operation stability and fault crossing ability. In this paper, the hybrid energy storage scheme of energy storage battery and super capacitor is adopted in DC distribution network, and the discrete Fourier spectrum analysis of power demand sample data is carried out to obtain the basic power in low frequency and fluctuating power in high frequency. The energy storage battery with slow dynamic response speed is used to match the power demand of low-frequency segment, and the super capacitor with slow dynamic response speed is used to match the power demand of high-frequency segment. The influence index of SOC over limit times of energy storage system is introduced to seek the critical value of ESS capacity planning. Finally, the proposed hybrid energy storage planning method is verified by the case studying.

Most research on drone detection and classification focus on using features from micro-Doppler signatures with blade flashes. However, these methods are limited in range and require radars with high pulse repetition frequency (PRF)–at least twice the maximum tip velocity. A different method to detect and classify drones at longer ranges using a low PRF radar is desired. In the literature, the cepstrum method was shown to be able to estimate the rotation rate when the PRF is insufficient. An alternative way of analyzing micro-Doppler is by using a long windowed Short-time Fourier transform (STFT) to generate HElicopter Rotation Modulation (HERM) lines. HERM lines exhibit similar behavior to a cepstrogram, with spectral lines separated in frequency by a value related to the rotation rate. In this paper, the separation frequency of HERM lines was estimated using a log harmonic summation algorithm. The proposed algorithm was tested on a simple HERM line model and also on real data obtained from two blade single rotor micro-helicopter drone. The algorithm was shown to be more resilient than cepstrum under Gaussian noise.

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.