Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.
With the maturity of unmanned aerial vehicle (UAV) technology and the improvement of relevant laws and regulations, UAVs are increasingly being used for the development of the Internet of Things (IoT). For example, UAVs have been broadly used in the military IoT, smart agriculture, and smart cities to obtain and transmit geospatial information, sensor data information, and controlling information. In smart agriculture, UAV systems are used to gather near real-time remote sensing data for precision farming. In such applications, UAV systems need to be positioned precisely to obtain data from sensors. In smart cities, almost all aspects of the city are combined with the IoT, a task that requires large amounts of data transmission. This data volume requires a large number of base stations for IoT data transmission. Researchers have equipped UAVs with various sensors, such as high-definition cameras, as well as temperature, humidity, and air pollution sensors. In addition, todays UAVs are equipped with high quality wireless communication functions, including 5G, Wifi, Bluetooth, radio-frequency identification (RFID), and other communication means [
At present, the positioning technology of UAVs is mainly achieved by using the GPS system but this method is sensitive to certain environments. Theoretically, the GPS system only requires three satellites to obtain positioning data but in practice, the UAV needs to communicate with at least 10 satellites to obtain a stable GPS system. When flying indoors, through tunnels, or dense construction areas, the GPS signal is not reliable and UAV positioning based on the GPS system will cause difficulties [
In this study, the precise positioning of UAVs is achieved through radar-assisted detection and identification of UAV technology. The method prevents many problems associated with traditional GPS positioning technology, such as GPS signal failure indoors, satellite occlusion areas, and interference with GPS signals. The radar-assisted detection and identification method has the advantage of working under all weather conditions and is capable of extracting additional features of the UAV due to the micro-Doppler features [
The rest of this paper is organized as follows. The second part introduces the system model of the radar-assisted UAV positioning method in the IoT. In the third part, the scattering model of the UAV and rotors is established, the HRRP of a rotor-type UAV is introduced, and the micro-Doppler effect of the UAV is analyzed. The cepstrum method is used to extract the speed of the UAV and the number of rotors and the method for identifying multiple UAVs is described. In the fourth part, the simulation results and one-dimensional imaging of a multitarget model of two UAVs are provided. The identification of the UAV based on the micro-Doppler information is provided and the method for extracting the number and speed of the rotors are described. The conclusions are provided in the final section.
5G mmWave technology and distributed networks will facilitate radar detection and identification of small UAVs. The narrow steerable beam enables the base station to function as a millimeter wave radar system so that the base station is able to detect and identify drones in urban environments [
The model for detecting and identifying the UAV in the 5G network is shown in Figure
UAV detection based on 5G network.
The flowchart of the process of radar-assisted detection and identification of UAVs is shown in Figure
Flowchart of radar-based identification.
High bandwidth, low latency, and good beamforming of 5G networks provide a reliable basis for resolution because future 5G mmWave systems cannot only support very high carrier frequencies but also wider bandwidths (effective operation up to 2 GHz). Wider bandwidth facilitates the detection of UAVs flying in close formation. The SFW is a type of 5G mmWave signal and produces an HRRP of the target. A typical SFW burst is shown in Figure
SFW burst.
Within a pulse width
where
The echo signal of the target is defined in
where
where
The received signal is split in the quadrature phase detector and enters the mixer of the two synchronous detectors. In the first mixer, the reference signal and the received signal are mixed and the 90° phase-shifted echo signal is mixed with the reference signal in the second mixer. After combining the outputs of the two mixers and low pass filtering, the complex signal is
If we assume that the targets’ moving distance is less than one range resolution cell for each group of pulse signals, then
The inverse discrete Fourier transform (IDFT) of
By normalizing [
which can be simplified as
where
In traditional radar applications, the antenna illuminates the target with a microwave signal and receives the echo reflected by the target. The echo signal includes the target characteristics of interest. For example, if the transmit signal hits a moving target, the carrier frequency of the echo signal shifts, which is called the Doppler effect. The Doppler shift reflects the moving speed of the target. Mechanical vibration or rotation of a structure on the target can also cause additional frequency shifts in the returned radar signal; this is known as the micro-Doppler effect. The micro-Doppler effect allows us to determine additional properties of the target [
In many cases, some components of the target may have rotations or vibrations in addition to the main target translation, such as a rotor on a UAV or the flapping wing of a bird. The motion dynamics of the rotating rotor or wing will frequency-modulate the backscattered signals and cause additional Doppler shifts near the center of the translational Doppler shift, which is known as the micro-Doppler shift. Therefore, the micro-Doppler shift can be considered a unique signature of targets with vibrations, rotations, or other nonuniform motions. The micro-Doppler shift depends on the signal wavelength, the vibration or rotation speed of the target, and the incident angle of the radar line of sight (LOS) relative to the rotation or vibration plane. The reflected radar signal has the largest micro-Doppler shift when the radar LOS is parallel to the rotation or vibration plane. When the radar LOS is perpendicular to the rotation or vibration plane, the reflected signal has the smallest micro-Doppler shift. The micro-Doppler shift has been widely used to classify targets with rotations or vibrations.
Almost all UAVs have at least one or more rotating rotors. For example, a four-rotor UAV has two rotors that rotate clockwise and two that rotate counterclockwise about a vertical axis. The UAV can take off vertically, hover, and fly forward, backward, and sideways. Other multirotor UAVs, such as UAVs with six or eight rotors, have matching rotor sets that rotate in opposite directions. Therefore, it is possible to detect and identify UAVs by using the micro-Doppler effect of the rotors. In this study, we extract the characteristics of UAVs based on the micro-Doppler effect [
A UAV is a slow small target that can be simplified to a point target in traditional radar; however, since we plan to identify the UAV by using information on the rotors, we have to develop a model of a UAV in which the rotor is separate from the fuselage. Figure
Scattering model of a rotor-type UAV.
The fuselage scattering of the UAV can be obtained by (
Then the echo signal can be defined as
We assume that the blade length is L as shown in Figure
where
The movement of the UAV can be divided into two parts, namely, the movement of the main body of the UAV and the rotation of the rotor. Since the time of a one-step sweep measurement is about hundreds of milliseconds and the UAV is a small slow target, its displacement during the one-step sweep measurement is negligible. Therefore, the UAV can be regarded as a stationary target during the one-step sweep measuring time. In this study, we do not consider the movement of the fuselage.
It is evident from (
The IDFT of the expanded rotor echo signal based on Eq. (
According to the characteristics of the Bessel function:
The energy of the frequency-domain signal produces a divergence effect. The magnitude of the harmonic amplitude is modulated by the Bessel function. According to the Carson criterion [
The width of spectral broadening caused by the rotation of the rotor is [
The rotational motion appears in the HRRP as a symmetrical range distribution centered on the position of the fuselage. When the rotor scattering is smaller than the fuselage scattering or slightly larger than the fuselage scattering, the amplitude of the rotor in the HRRP is much smaller than the amplitude of the fuselage because the rotor rotational motion is dispersed after the Bessel series expansion. For the purpose of detection, the influence of the rotor rotation on the HRRP can be neglected and the position information of the UAV can be obtained by directly reading the position of the main scattering from the fuselage. In most cases, the fuselage scattering is greater than the rotor scattering and the effects of the rotor rotation do not have to be considered for obtaining the UAV position.
When the rotor scattering is much larger than the fuselage scattering, the main body scattering is submerged in the distance harmonic caused by the rotation of the rotor. In this case, the harmonic boundary caused by the rotation can be estimated and the center position of the two boundary positions can be used to estimate the target position. Alternatively, the SFM parameter optimization method described below can be used to compensate for the effect of rotor rotation on the HRRP.
The Doppler shift of the received signal can be expressed as
The micro-Doppler frequency shift changes over time and the joint time-domain analysis method is the most intuitive means to observe the target’s micro-Doppler effect. Short-time Fourier transform (STFT) [
The extraction of the parameters of the rotor, such as the number of rotors and the speed, is used to identify the type of UAV. For single-rotor UAVs, the STFT can be used for the identification. For multirotor UAVs, the micro-Doppler frequencies of different rotors are aliased together, which makes the target difficult to resolve and requires a new extraction method.
It is evident in (
Cepstrum is an effective method used in speech signal processing [
A Fourier transform is performed on
Function diagram of
By calculating the log spectrum, the multiplicative relationship between the comb function and amplitude modulation is converted into a summation. Then, the inverse Fourier transform is performed and the square of the amplitude is used to obtain the cepstrum of the function.
The specific equation is as follows:
The cepstrum domain is shown in Figure
Cepstrum domain.
The rotor rotation widens the HRRP distance. If the distance between two targets is large enough so that they can be separated in the HRRP, each target can be separately processed to determine whether it is a UAV. If the distance between the targets is very small due to distance expansion by rotation, a multi-SFM signal separation method based on parameter optimization is used to compensate for the rotational motion.
We discretize (
Table
Variable simplification.
Initial parameter | Simplified parameters |
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Equation (
The unknown parameters in (
The modulation period of the signal can be obtained by the cepstrum method and will not be described here.
(a) Estimation of the modulation period
(b) Estimation of the initial phase
We assume
Equation (
For the ith SFM component,
(c) Estimation of
Fast Fourier transform (FFT) is performed on (
The steps for identifying close-range multi-UAVs using the SFM parameter optimization method are as follows:
(a) The cepstrum method is used to calculate the rotation period of each rotating target.
(b) Since the fuselage scattering will affect the SFM parameter optimization method, the influence of the subject scattering must be removed prior to using the SFM parameter optimization method. First, two subsequent frames of signals are measured and subtracted from each other for the cancellation of the fuselage scattering. Second, HRRP is used to determine the maximum value of the rotor scattering. Finally, the maximum value is set as a threshold. If the single frame in the HRRP is larger than the threshold, the single signal is set as the mean value of the subtracted HRRP.
(c) The SFM parameter optimization method is used for the rotation periods of the rotors obtained in step (a) to determine their position.
Figure
Schematic of the simulation model.
The HRRP of the echo signal is shown in Figure
HRRP of the target.
Figure
Micro-Doppler of target 1.
There are multiple rotors in this target; therefore, the micro-Doppler signal is aliased and the type parameters of the UAV rotor cannot be directly obtained. The cepstrum method is used to determine the rotor type.
Figure
Cepstrum extraction of target 1.
In order to verify the robustness of the cepstrum method, noise with different signal-to-noise ratios (SNRs) is added to the echo signal of target 1. Figure
Cepstrum results with the addition of different SNRs.
SNR= −5dB
SNR= 0dB
SNR= 5dB
Target 2 and target 3 are analyzed as a single target. Figure
Micro-Doppler of target 2 and target 3.
As shown in Figure
Cepstrum result of target 2 and target 3.
The position of the single rotor is estimated by the SFM parameter optimization method after the two target body scatterings are removed. The estimated results are shown in Figure
Target parameters.
Target type | Position (m) | Rotor speed (r/s) | |
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Target 1 | Quad-rotor UAV | 300 | 157.9, 150.0, 142.0, 138.9 |
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Target 2 | Other targets | 799.2 | - |
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Target 3 | One-rotor UAV | 850.2 | 88 |
Rotor position estimation.
Table
UAV flight route.
In this paper, a radar-assisted UAV detection and identification method that is independent of a GPS system was proposed. The HRRP technology of wideband radar was used for UAV detection and positioning and the micro-Doppler signal, which is capable of detecting rotating targets, was used for UAV identification. Cepstrum analysis was used to extract the number and rotation speed of the UAV rotors. The simulation results demonstrated the good robustness of this method. An SFM parameter optimization algorithm was used to compensate for the rotational motion and estimate the rotor positions; two UAVs with aliasing signals in the HRRP were separated effectively using the proposed method. The simulation results showed that the UAVs can be identified and the number of UAVs, the number of rotors on each UAV, and the rotation speeds of the rotors can be determined. In addition, the proposed radar-assisted UAV detection and identification method can provide alerts if UAVs deviate from their routes during GNSS spoofing attacks.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there is no conflict of interest regarding the publication of this paper.