KEYWORDS: Radar signal processing, Radar sensor technology, Signal generators, Microwave photonics, Unmanned aerial vehicles, Radar, Digital signal processing, Antennas, Signal processing, Receivers
This paper presents the experimental demonstrations of LFMCW radar system based on microwave photonics generated radar signal and electronic receiving link. In the transmitting section of the radar, the seed signal given by DDS with relative low frequency and relative narrow bandwidth is designed with tunable signal wave forms, which can afford the system with different time-width and bandwidth. After the microwave photonics electro-optic modulation and photoelectric transformation system, the seed signal will be transformed into the radar signal by 4 times with the frequency carried and instantaneous bandwidth. In the receiving part of the radar, the electronic dechirping method is carried out to down convert the radar echo signal instead of microwave photonics approach. After electronic ADC, the ranging data is processed. The experiments towards to the cooperative UAV target are carried out in urban background. The true data about the longitude, latitude and the flight height of the UAV is sent to the radar from the UAV, and the data is used to guide the radar to detect the UAV’s range. A typical radar wave forms 4GHz bandwidth and 20ms pulse width is operated through the system. The experimental demonstration by protocol type has been shown, and the experimental results show that the range of frequency modulated continuous wave radar based on this system can reach 1 km for target with RCS 0.01m2 (DJI).
KEYWORDS: Radar signal processing, Radar sensor technology, Signal generators, Microwave photonics, Antennas, Digital signal processing, Receivers, Signal detection, Radar, Frequency modulation
Demonstration of LFMCW radar system by hardware-in-the-loop simulation based on tunable microwave photonics generated radar signal and electric receiving link is presented. The seed signal is given by Direct Digital Synthesis (DDS) with tunable signal wave forms, which can afford the system with different time-width and bandwidth. The microwave photonics electro-optic modulation and photoelectric transformation system turns the seed signal with low-frequency and narrow-bandwidth into the signal by 4 times at frequency carried on the laser through the dualparallel electro-optic modulator, and obtain the high-frequency and broadband radar signal after the photoelectric detector, and then transmit the radar signal into the input port of the hardware-in-the-loop simulator with time delay function. The radar signal is set as different delay corresponding to different transmission distance, such as 1km, 2km, and other distances. After the hardware-in-the-loop simulator with some distance delay, the signal is transmitted from the output ort of the hardware-in-the-loop simulator into the receiver. In the receiving link, the electric de-chirping method is carried out to down convert the radar echo signal. After electric ADC, the ranging data is processed. Two typical wave forms, such as the wave form with 1GHz bandwidth and 2ms pulse width, and the other wave form with 2GHz bandwidth and 8ms are operated through the system respectively. The demonstration by hardware-in-the-loop simulation has been given, and the experimental results show that the range of frequency modulated continuous wave radar based on this system can reach 5 km.
The rapid development of the drone industry brings security risks to low-altitude airspace in important places. In order to effectively defense unmanned aerial vehicles (UAV), it is of great significance to develop a system which can identify drone models by radio signal. To identify similar UAVs, a UAV signal recognition method based on re-classification and separation of image transmission signal (ITS) and flight control signal (FCS) is proposed. The OFDM time domain parameters of ITS are extracted for model pre-classification. Then, after extracting the time-frequency features of ITS and FCS respectively, the UAV can be identified with high precision by SVM. The experimental results show that the average accuracy of the four DJI UAVs can reach more than 95% when the signal-to-noise ratio (SNR) is 0dB.
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