Unmanned aerial vehicle target active detection method and system based on millimeter wave radar

Abstract

The invention discloses an unmanned aerial vehicle target active detection method and system based on a millimeter wave radar, and relates to the technical field of unmanned aerial vehicle detection, and the method comprises the steps: carrying out the frequency mixing of an obtained emission frequency modulation continuous wave signal and an echo signal, and obtaining an intermediate frequency signal; performing fast Fourier transform on the intermediate frequency signal to obtain distance information of the target unmanned aerial vehicle; acquiring a plurality of Chirp signals based on the echo signals; based on the phase difference among the plurality of Chirp signals, calculating to obtain the speed information of the target unmanned aerial vehicle; performing angle estimation on the echo signal to obtain angle information of the target unmanned aerial vehicle; integrating the distance information, the speed information and the angle information of the target unmanned aerial vehicle to obtain motion parameters of the target unmanned aerial vehicle; and transmitting the motion parameters of the target unmanned aerial vehicle to a flight control system or other data receiving equipment of the unmanned aerial vehicle. The detection capability and the tracking effect of the unmanned aerial vehicle target can be improved, and the operation safety of the unmanned aerial vehicle is effectively guaranteed.

Description

Technical Field

[0001] The present application relates to the field of drone detection technology, and in particular to a method and system for active drone target detection based on millimeter-wave radar. Background Art

[0002] In the current drone sector, technologies for detecting other drones in flight have made considerable progress. Traditional non-cooperative detection methods primarily include optical detection, radar detection, and radio spectrum detection. Optical detection often uses devices such as infrared thermal imagers to capture images or heat signatures of target drones, enabling detection and identification. For example, in some security surveillance scenarios, infrared thermal imagers can be deployed to monitor drones within a certain range. Radar detection uses the transmission and reception of radio waves to determine the target drone's location, speed, and other information based on the echoes. Common pulse radars, for example, emit periodic pulse signals and analyze the echo's time delay and Doppler shift to determine target parameters. Radio spectrum detection primarily monitors the radio communication signals between a drone and its remote controller to determine its presence and approximate location.

[0003] Traditional optical detection methods are significantly affected by environmental factors. In adverse weather conditions, such as heavy rain, fog, and dust, light transmission can be severely disrupted, resulting in image quality degradation or even inability to obtain valid images, significantly reducing detection effectiveness. Furthermore, when the target drone is at a distance, the resolution limitations of optical equipment can make it difficult to discern target details, making accurate detection and identification difficult.

[0004] In terms of radar detection, traditional radars face numerous challenges when dealing with small, slow, and low-lying targets such as drones. These targets have a small radar cross-sectional area, resulting in weak signal echoes that are easily drowned out by background noise. This limits detection range and makes it difficult to detect targets at longer distances. Furthermore, due to the low speed of drones, radars using moving target display technology have a subtle Doppler shift in their echo signals, making it easy to miss targets. Without moving target display technology, radars are subject to interference from a large amount of echo noise from fixed ground objects, compromising target detection and tracking. In particular, traditional angle estimation methods, such as the angle-dimensional FFT, have angular resolution limited by the array aperture, significantly degrading performance in low signal-to-noise ratio environments. Traditional digital beamforming (DBF) algorithms, when using smaller array elements, have larger mainlobe widths and higher sidelobe levels, resulting in low angle estimation accuracy and resolution. These algorithms are susceptible to sidelobe interference, leading to angle quantization errors.

[0005] Traditional radio spectrum detection relies primarily on detecting radio communications between drones and their remote controllers. However, if drones employ radio silence—that is, they turn off radio signals when not communicating—then traditional radio spectrum detection methods struggle to detect them. In urban areas and other radio-dense environments, radio spectrum detection can be affected by interference from other radio signals, degrading its performance.

[0006] In summary, the existing non-cooperative UAV detection technology has deficiencies in distance measurement, speed measurement, and angle measurement, and is unable to meet the growing demand for UAV operation safety. There is an urgent need for a new detection system to improve the detection capability of UAV targets. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for active detection of UAV targets based on millimeter-wave radar, which can enable the UAV to detect the distance, speed and angle information of other running UAV targets in real time and accurately through the millimeter-wave radar carried by the UAV during operation, thereby significantly improving the detection capability and tracking effect of UAV targets, and effectively ensuring the safety of UAV operation.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] In a first aspect, the present application provides a method for actively detecting drone targets based on millimeter-wave radar, the method comprising:

[0010] Acquire the transmitted frequency modulated continuous wave (FMCW) signal and the echo signal reflected by the target UAV.

[0011] The transmit frequency modulated continuous wave signal is mixed with the echo signal to obtain an intermediate frequency signal.

[0012] Perform fast Fourier transform (FFT) on the intermediate frequency signal to obtain the distance information of the target UAV.

[0013] Based on the echo signal, several Chirp signals are acquired.

[0014] Based on the phase difference between several Chirp signals, the speed information of the target UAV is calculated.

[0015] Angle estimation is performed on the echo signal to obtain angle information of the target UAV.

[0016] The distance information of the target UAV, the speed information of the target UAV and the angle information of the target UAV are integrated to obtain the motion parameters of the target UAV.

[0017] The motion parameters of the target UAV are transmitted to the flight control system or other data receiving equipment of the UAV.

[0018] Optionally, performing angle estimation on the echo signal to obtain angle information of the target UAV specifically includes:

[0019] The phase of the echo signal is calibrated using a calibration vector to obtain a calibrated signal.

[0020] Doppler compensation is performed on the calibrated signal to obtain a compensated echo signal.

[0021] Taylor windowing is performed on the compensated echo signal using a Taylor window function to obtain a weighted signal vector.

[0022] A global coarse scan is performed on the weighted signal vector to obtain the beam output power of the peak point and its adjacent points.

[0023] A quadratic function fitting interpolation method is used to perform parabolic fitting on the beam output power of the peak point and its adjacent points, and an interpolation coefficient is calculated.

[0024] Based on the interpolation coefficients, a correction angle is calculated.

[0025] The corrected angle is scanned a second time to obtain angle information of the target UAV.

[0026] Optionally, the calibration vector is expressed as:

[0027]

[0028] Among them, c t is the calibration vector; N is the number of array elements; is the phase error of each array element channel.

[0029] Optionally, the expression of the Taylor window function is:

[0030]

[0031] Where w(n) is the Taylor window function; M is the total length of the window function; nbar is the number of side lobes with approximately constant level near the main lobe; n is the sample point position; and m is the side lobe number control parameter.

[0032] Optionally, the calculation formula of the interpolation coefficient is:

[0033]

[0034] Among them, δ is the interpolation coefficient; P(θ1) and P(θ2) are the adjacent point beam powers; P(θ k) is the beam power corresponding to the peak point.

[0035] In a second aspect, the present application provides an active detection system for UAV targets based on millimeter-wave radar, wherein the active detection system for UAV targets based on millimeter-wave radar includes: a signal transceiver module, a signal processing module, a data processing module and a data transmission module.

[0036] The signal transceiver module is used to transmit a frequency modulated continuous wave signal and receive an echo signal reflected by the target UAV.

[0037] The signal processing module includes: a distance measuring unit, a speed measuring unit and an angle measuring unit.

[0038] The distance measuring unit is used to obtain the distance information of the target UAV.

[0039] The speed measuring unit is used to obtain the speed information of the target UAV.

[0040] The angle measuring unit is used to estimate the angle of the echo signal to obtain the angle information of the target UAV.

[0041] The data processing module is used to integrate the distance information of the target UAV, the speed information of the target UAV and the angle information of the target UAV to obtain the motion parameters of the target UAV.

[0042] The data transmission module is used to transmit the motion parameters of the target UAV to the flight control system of the UAV or other data receiving equipment.

[0043] Optionally, the ranging unit includes:

[0044] The mixing processing submodule is used to mix the transmitted frequency-modulated continuous wave signal with the echo signal to obtain an intermediate frequency signal.

[0045] The distance information acquisition submodule is used to perform fast Fourier transform on the intermediate frequency signal to obtain the distance information of the target UAV.

[0046] Optionally, the speed measuring unit includes:

[0047] The Chirp signal acquisition submodule is used to acquire a plurality of Chirp signals based on the echo signal.

[0048] The speed information acquisition submodule is used to calculate the speed information of the target UAV based on the phase difference between several Chirp signals.

[0049] Optionally, the angle measuring unit includes:

[0050] The phase calibration submodule is used to perform phase calibration on the echo signal using a calibration vector to obtain a calibrated signal.

[0051] The Doppler compensation submodule is used to perform Doppler compensation on the calibrated signal to obtain a compensated echo signal.

[0052] The beamforming submodule is used to perform weighted summation on the compensated echo signals to obtain angle information of the target UAV.

[0053] Optionally, the beamforming submodule includes:

[0054] The windowing subunit is used to perform Taylor windowing processing on the compensated echo signal using a Taylor window function to obtain a weighted signal vector.

[0055] The global coarse scanning subunit is used to perform a global coarse scan on the weighted signal vector to obtain the beam output power of the peak point and its adjacent points.

[0056] The parabola fitting subunit is used to perform parabola fitting on the beam output power of the peak point and its adjacent points by adopting an interpolation method of quadratic function fitting, and calculate the interpolation coefficient.

[0057] The correction angle calculation subunit is used to calculate the correction angle based on the interpolation coefficient.

[0058] The secondary fine scanning subunit is used to perform a secondary fine scanning on the corrected angle to obtain angle information of the target UAV.

[0059] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0060] The present application provides a method and system for active detection of UAV targets based on millimeter wave radar. By acquiring a transmitted frequency modulated continuous wave signal and an echo signal reflected by the target UAV, a complete raw data basis can be provided for subsequent signal processing. By mixing the transmitted frequency modulated continuous wave signal with the echo signal to obtain an intermediate frequency signal, the high-frequency original signal can be converted into a lower-frequency intermediate frequency signal. This is beneficial for subsequent signal processing because the intermediate frequency signal is easier to amplify, filter, and other processing operations of the analog signal, reducing the frequency response requirements of the hardware equipment, while also reducing the complexity of signal processing and improving the efficiency of signal processing. By performing a fast Fourier transform on the intermediate frequency signal, the distance information of the target UAV can be quickly and accurately obtained. By acquiring a plurality of chirp signals based on the echo signal and calculating the speed information of the target UAV based on the phase difference between the plurality of chirp signals, the reflection characteristics of the target UAV in different time periods can be analyzed in more detail. This provides multiple data samples for subsequent speed information calculation, which helps to improve the accuracy of speed estimation. Since multiple Chirp signals are used, the measurement error can be effectively reduced and the accuracy of speed estimation can be improved. By estimating the angle of the echo signal, the angle information of the target drone is obtained, and the spatial posture and position relationship of the drone can be fully understood. By integrating the distance information of the target drone, the speed information of the target drone and the angle information of the target drone, the motion parameters of the target drone are obtained, and the motion parameters of the target drone are transmitted to the flight control system or other data receiving equipment of the drone, so that the flight control system or data receiving equipment can fully and accurately understand the movement of the drone. The present application can realize that during the operation of the drone, the millimeter-wave radar carried by the drone can accurately detect the distance, speed and angle information of other running drone targets in real time, thereby significantly improving the detection capability and tracking effect of the drone target, and effectively ensuring the safety of the drone operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0062] Figure 1 A flow chart of a method for active detection of UAV targets based on millimeter-wave radar is provided in accordance with one embodiment of the present application.

[0063] Figure 2A schematic diagram of the structure of an active UAV target detection system based on millimeter-wave radar provided in one embodiment of the present application.

[0064] Figure 3 A schematic diagram of the structure of a signal processing module provided in one embodiment of the present application.

[0065] Figure 4 A schematic structural diagram of a distance measuring unit provided in one embodiment of the present application.

[0066] Figure 5 A schematic structural diagram of a speed measurement unit provided in one embodiment of the present application.

[0067] Figure 6 A schematic structural diagram of an angle measuring unit provided in one embodiment of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0069] The core purpose of this application is to provide a method and system for active detection of UAV targets based on millimeter-wave radar, aiming to solve the problems of existing UAV detection technology in facing other running UAV targets, such as insufficient accuracy in distance measurement, speed measurement and angle measurement, and being greatly affected by the environment. During the operation of the UAV, the UAV can use the carried millimeter-wave radar to accurately detect the distance, speed and angle information of other running UAV targets in real time, thereby significantly improving the detection capability and tracking effect of UAV targets, and effectively ensuring the safety of UAV operation.

[0070] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0071] In an exemplary embodiment, Figure 1 As shown, a method for active detection of UAV targets based on millimeter wave radar is provided, which includes the following steps S1 to S8.

[0072] S1: Acquire the transmitted FMCW signal and the echo signal reflected by the target UAV.

[0073] S2: Mixing the transmitted frequency-modulated continuous wave signal with the echo signal to obtain an intermediate frequency signal.

[0074] S3: Perform fast Fourier transform on the intermediate frequency signal to obtain distance information of the target UAV.

[0075] S4: Acquire several Chirp signals based on the echo signal.

[0076] S5: Based on the phase difference between several Chirp signals, the speed information of the target UAV is calculated.

[0077] S6: Estimating the angle of the echo signal to obtain angle information of the target UAV.

[0078] S7: Integrate the distance information of the target UAV, the speed information of the target UAV, and the angle information of the target UAV to obtain motion parameters of the target UAV.

[0079] S8: Transmitting the motion parameters of the target UAV to the flight control system or other data receiving device of the UAV.

[0080] By implementing the above steps S1 to S8, the UAV can use the millimeter-wave radar on board to accurately detect the distance, speed and angle information of other running UAV targets in real time during operation, thereby significantly improving the detection capability and tracking effect of UAV targets and effectively ensuring the safety of UAV operation.

[0081] As an optional implementation, in step S6, the angle of the echo signal is estimated to obtain the angle information of the target UAV, specifically including:

[0082] S61: Perform phase calibration on the echo signal using a calibration vector to obtain a calibrated signal.

[0083] S62: Perform Doppler compensation on the calibrated signal to obtain a compensated echo signal.

[0084] S63: Perform Taylor windowing processing on the compensated echo signal using a Taylor window function to obtain a weighted signal vector.

[0085] S64: Perform a global coarse scan on the weighted signal vector to obtain the beam output power of the peak point and its adjacent points.

[0086] S65: Perform parabola fitting on the beam output power of the peak point and its adjacent points using an interpolation method of quadratic function fitting to calculate an interpolation coefficient.

[0087] S66: Calculate and obtain a correction angle based on the interpolation coefficient.

[0088] S67: Perform a second fine scan on the corrected angle to obtain angle information of the target UAV.

[0089] The present application also provides an application scenario that utilizes the above-mentioned millimeter-wave radar-based active drone target detection method. Specifically, the millimeter-wave radar-based active drone target detection method provided in this embodiment can be applied in drone detection scenarios. The drone detection scenario includes: a signal acquisition phase, a frequency mixing phase, a distance information acquisition phase, a speed information acquisition phase, and an angle information acquisition phase. First, a transmitted frequency-modulated continuous wave signal and an echo signal reflected by a target drone are acquired; the transmitted frequency-modulated continuous wave signal and the echo signal are mixed to obtain an intermediate frequency signal; second, the intermediate frequency signal is fast Fourier transformed to obtain the distance information of the target drone; third, based on the echo signal, multiple chirp signals are acquired; based on the phase difference between the multiple chirp signals, the speed information of the target drone is calculated; then, the angle of the echo signal is estimated to obtain the angle information of the target drone; finally, the distance information, speed information, and angle information of the target drone are integrated to obtain the motion parameters of the target drone; and the motion parameters of the target drone are transmitted to the drone's flight control system or other data receiving device.

[0090] Based on the same inventive concept, the present application also provides a millimeter-wave radar-based UAV target active detection system for implementing the millimeter-wave radar-based UAV target active detection method mentioned above. The implementation solution provided by this system is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more millimeter-wave radar-based UAV target active detection system embodiments provided below can be found in the limitations of the millimeter-wave radar-based UAV target active detection method above, and will not be repeated here.

[0091] In an exemplary embodiment, Figure 2 As shown, a millimeter-wave radar-based UAV target active detection system is provided, and the millimeter-wave radar-based UAV target active detection system includes: a signal transceiver module 110, a signal processing module 120, a data processing module 130 and a data transmission module 140.

[0092] The signal transceiver module 110 is used to transmit a frequency modulated continuous wave signal and receive an echo signal reflected by a target UAV.

[0093] like Figure 3 As shown, the signal processing module 120 includes: a distance measuring unit 121 , a speed measuring unit 122 and an angle measuring unit 123 .

[0094] The distance measuring unit 121 is used to obtain the distance information of the target UAV.

[0095] The speed measuring unit 122 is used to obtain the speed information of the target UAV.

[0096] The angle measuring unit 123 is used to estimate the angle of the echo signal to obtain angle information of the target UAV.

[0097] The data processing module 130 is configured to integrate the distance information, the speed information, and the angle information of the target UAV to obtain the motion parameters of the target UAV;

[0098] The data transmission module 140 is used to transmit the motion parameters of the target UAV to the flight control system of the UAV or other data receiving equipment.

[0099] It should be noted that if Figure 2 As shown, the millimeter-wave radar-based UAV target active detection system further includes: a power supply 150 for supplying power to the signal processing module 120 .

[0100] As an optional implementation, the signal transceiver module 110 can be TI's AWR1843 millimeter-wave radar, which transmits a frequency-modulated continuous wave signal through the MIMO radar antenna array 111 and receives the echo signal reflected by the target drone. The MIMO radar antenna array 111 constructs an equivalent virtual array through time division multiplexing (TDM). The total number of virtual array elements is the product of the number of transmitting antennas and the number of receiving antennas. The spacing between transmitting antennas is several times the wavelength of the transmitting antenna, and the spacing between receiving antennas is several times the wavelength of the receiving antenna.

[0101] In this embodiment, the MIMO radar antenna array 111 is configured with 3 transmitting terminals and 4 receiving terminals of the AWR1843 millimeter wave radar to form 12 virtual channels. The distance between the transmitting antennas is 2 times the wavelength, the distance between the receiving antennas is 0.5 times the wavelength, and the transmitting channels are separated by a time interval T. r Alternate switching, in the signal repetition period 2T r Orthogonal transmission is achieved within.

[0102] As an optional implementation, Figure 4 As shown, the distance measuring unit 121 includes:

[0103] The frequency mixing processing submodule 1211 is configured to perform frequency mixing processing on the transmit frequency modulated continuous wave signal and the echo signal to obtain an intermediate frequency signal.

[0104] The distance information acquisition submodule 1212 is configured to perform a fast Fourier transform on the intermediate frequency signal to obtain the distance information of the target UAV.

[0105] As an optional implementation, Figure 5 As shown, the speed measuring unit 122 includes:

[0106] Chirp signal acquisition submodule 1221, for obtaining a number of Chirp signals based on the echo signal.

[0107] The speed information acquisition submodule 1222 is used to calculate the speed information of the target UAV based on the phase difference between multiple Chirp signals.

[0108] As an optional implementation, Figure 6 As shown, the angle measuring unit 123 includes:

[0109] The phase calibration submodule 1231 is configured to perform phase calibration on the echo signal using a calibration vector to obtain a calibrated signal.

[0110] The Doppler compensation submodule 1232 is configured to perform Doppler compensation on the calibrated signal to obtain a compensated echo signal.

[0111] The beamforming submodule 1233 is configured to perform weighted summation on the compensated echo signals to obtain angle information of the target UAV.

[0112] As an optional implementation manner, the beamforming submodule 1233 includes:

[0113] The windowing subunit is used to perform Taylor windowing processing on the compensated echo signal using a Taylor window function to obtain a weighted signal vector.

[0114] The global coarse scanning subunit is used to perform a global coarse scan on the weighted signal vector to obtain the beam output power of the peak point and its adjacent points.

[0115] The parabola fitting subunit is used to perform parabola fitting on the beam output power of the peak point and its adjacent points by adopting an interpolation method of quadratic function fitting, and calculate the interpolation coefficient.

[0116] The correction angle calculation subunit is used to calculate the correction angle based on the interpolation coefficient.

[0117] The secondary fine scanning subunit is used to perform a secondary fine scanning on the corrected angle to obtain angle information of the target UAV.

[0118] Specifically, (1) Phase calibration module: measures the phase error of each array element channel Construct the calibration vector.

[0119] Assume that the phase error of the kth array element is Then the actual received signal vector can be expressed as:

[0120]

[0121] Among them, a t is the actual received signal vector; a i is the ideal steering vector; Indicates element-by-element multiplication; N is the number of array elements; is the phase error of each array element channel.

[0122] The error phase is obtained by calibration measurement Thus construct the calibration vector c t , used to compensate for the phase error:

[0123]

[0124] Among them, c t is the calibration vector.

[0125] The calibrated beamforming weights are:

[0126]

[0127] Among them, W t is the original beamforming weight; W is the calibrated beamforming weight.

[0128] (2) Doppler compensation submodule: In view of the phase accumulation effect between adjacent transmitting antennas in TDM-MIMO radar, a compensation factor is used to eliminate the phase offset caused by the target speed.

[0129] (3) Beamforming module: uses improved digital beamforming technology to perform weighted summation on the compensated signals, specifically including:

[0130] (3.1) Apply Taylor window function to the received signal vector to perform windowing processing to suppress the sidelobe level. The expression of Taylor window function is:

[0131]

[0132] Where w(n) is the Taylor window function; M is the total length of the window function; nbar is the number of side lobes with approximately constant level near the main lobe; n is the sample point position; and m is the side lobe number control parameter.

[0133] (3.2) Based on the interpolation method of quadratic function fitting, parabola fitting is performed on the beam output power of the peak point and its adjacent points obtained by coarse scanning to calculate the interpolation coefficient:

[0134]

[0135] Among them, δ is the interpolation coefficient; P(θ1) and P(θ2) are the adjacent point beam powers; P(θ k ) is the beam power corresponding to the peak point.

[0136] The correction angle is:

[0137] θ t =θ k +δΔθ (6);

[0138] Among them, θ t is the correction angle; θ k is the coarse scanning peak point angle; Δθ is the coarse scanning step size.

[0139] (3.3) Second fine scanning strategy: At the correction angle θ t Set the local scanning range [θ t -Δθ,θ t +Δθ], based on the target distance estimation value R obtained by the speed measuring unit, the step size of the secondary fine scan is dynamically selected to achieve a refined scan of the target area, thereby improving the accuracy and resolution of target positioning. According to the target position provided by the speed measuring unit, the corresponding angle θ of the target can be S Expressed as:

[0140] θ S =arctan(1 / 2R) (7);

[0141] The angular resolution of the radar is:

[0142] θ res =λ / (Nd cosθ t ) (8);

[0143] Where λ is the operating wavelength, N is the number of array elements, and d is the element spacing.

[0144] In this embodiment, the array element spacing is the virtual array element spacing of the MIMO radar antenna array. The calculation formula for the virtual array element spacing of the MIMO radar antenna array 111 is:

[0145] d virt =(N TX -1)d TX +(N RX -1)d RX (9);

[0146] Among them, d virt is the virtual array element spacing; N TX is the number of transmitting antennas; N RX is the number of receiving antennas; d TX is the distance between transmitting antennas; d RX is the receiving antenna spacing.

[0147] The target physical characteristics are combined with the system performance indicators, and the exponential adjustment factor γ is introduced to balance the weights of the two. The adaptive step size formula is designed as:

[0148]

[0149] Where η is the step size scaling factor.

[0150] Perform high-resolution scanning with a refined step size within the set local scanning range. Recalculate the beam output power at each angle point within this range and find the new maximum point as the final target angle estimate θ f :

[0151] θ f =argmax P(θ),θ f ∈[θ t -Δθ,θ t +Δθ] (11);

[0152] Among them, P(θ) is the beam power corresponding to each point.

[0153] The signal processing process of the angle measurement unit is as follows: after the phase offset caused by velocity is eliminated by the Doppler compensation module, the echo signal is input into the beamforming module for Taylor windowing processing to obtain a weighted signal vector; the weighted signal is subjected to a global coarse scan, and the angle estimation value is corrected by the quadratic function fitting interpolation method; a second fine scan is performed based on the corrected value to obtain a high-precision target angle; the phase calibration module compensates for the phase error of each array element before angle measurement, and the calibrated signal enters the angle measurement process.

[0154] The parameter nbar of the Taylor window function can be adjusted according to the sidelobe suppression requirements. By increasing nbar, the sidelobe level is reduced, while allowing several sidelobes of approximately constant level to exist near the main lobe, thereby balancing the mainlobe width and sidelobe suppression capability.

[0155] The local scanning range θ of the secondary fine scanning strategy m Set to correction angle θ t The step size of the secondary fine scan is dynamically selected for each Δθ before and after based on the target distance information obtained by the ranging unit 121 and the relevant performance indicators of the selected system to achieve a refined scan of the target area, thereby improving the accuracy and resolution of target positioning.

[0156] The phase calibration module measures the phase error of the non-uniform array A calibration vector is constructed to compensate the received signal vector and restore the ideal array response.

[0157] The distance and speed measurement unit 121 of the signal processing module 120 adopts a two-dimensional FFT (2D-FFT) method to analyze the difference frequency signal in the fast time dimension to obtain distance information, and analyze the phase change sequence in the slow time dimension to obtain speed information. The input signal of the angle measurement unit is a frequency domain signal after FFT in the distance dimension and the speed dimension.

[0158] As an optional implementation, the signal processing module 120 may be a DSP processor built into TI's AWR1843. The distance measuring unit 121, the speed measuring unit 122, and the angle measuring unit 123 may be programmed using an embedded language to obtain the state vector information collected by the signal transceiver module 110.

[0159] As an optional implementation, data processing module 130 can be the ARM processor built into AWR1843. The ARM Cortex-R4F can work in conjunction with the DSP subsystem to efficiently run various signal processing algorithms and data analysis programs, ensuring that the system processes target information quickly and accurately.

[0160] As an optional embodiment, the data transmission module 140 can be a wireless network card based on the IEEE 802.11ac protocol. This wireless network card can provide high-speed and stable data transmission, and transmit the distance, speed, and angle information of the target drone obtained by the data processing module 130 in real time to the drone's flight control system or other data receiving device.

[0161] During the installation process, the millimeter-wave radar is connected to the data processing module 130 via a dedicated data interface to ensure stable and accurate data transmission. The data processing module 130 and the data transmission module 140 are also connected via corresponding interfaces to achieve rapid data transmission. At the same time, the various hardware devices are rationally arranged and fixed to accommodate vibrations and environmental changes during drone flight. For example, the millimeter-wave radar is installed at the front of the drone to enable it to better detect target drones ahead; the data processing module 130 and the data transmission module 140 are installed in a relatively stable position inside the drone, and necessary vibration reduction measures are taken to ensure the normal operation of the equipment.

[0162] In terms of software algorithm implementation, data acquisition begins. The millimeter-wave radar continuously transmits a frequency-modulated continuous wave signal at a set frequency and receives the echo signal reflected from the target drone. After preliminary processing within the millimeter-wave radar, the echo signal is transmitted to the data processing module 130 via a data interface. During the data acquisition process, parameters such as the signal sampling frequency and number of sampling points are appropriately set to ensure that the collected data accurately reflects the target drone's information.

[0163] During the signal processing phase, the collected echo signals undergo preprocessing, including noise removal and amplification, to improve signal quality. The transmitted and received signals are then mixed to generate an intermediate frequency (IF) signal. A Fast Fourier Transform (FFT) is performed on the IF time-domain signal to convert it to the frequency domain, thereby obtaining target distance information. For speed measurement, the target's speed is calculated using the corresponding formula by analyzing the phase difference between different chirps.

[0164] To implement the angle measurement algorithm, an improved algorithm based on MIMO technology is employed. First, based on the principle of constructing an equivalent virtual array using time-division multiplexing, relevant parameters of the virtual array, such as virtual array spacing and the total number of virtual elements, are calculated. Then, addressing the shortcomings of the traditional Doppler compensation model, a new Doppler compensation model is used to process the signal, eliminating velocity-induced phase shifts through compensation factors. Next, an improved digital beamforming algorithm is employed for angle estimation. The weight vector is first subjected to Taylor windowing to reduce sidelobe levels. A global coarse scan is then used to initially locate the peak point, and an interpolation method based on quadratic function fitting is used to improve the accuracy of the preliminarily determined mainlobe. Finally, a second fine scan is performed near the interpolated result to further refine the angle scan step size, thereby improving the accuracy and resolution of the angle estimation.

[0165] During the algorithmic operation, the computing resources of data processing module 130 are fully utilized, employing techniques such as parallel computing to improve operational efficiency. For example, when performing FFT transformations, the multi-core CPU of data processing module 130 can be used for parallel computing, reducing computation time. Simultaneously, the algorithm parameters are optimized and adjusted based on the actual application scenario and the characteristics of the target drone to achieve optimal detection results.

[0166] During the result output phase, the processed target drone's distance, speed, and angle information is packaged in a specified format and transmitted in real time to the drone's flight control system or other data receiving device via data transmission module 140. Based on the received target information, the flight control system makes appropriate decisions, such as adjusting flight attitude or avoiding the target.

[0167] After the system is built, comprehensive debugging is performed. First, hardware debugging is performed to check whether the connections between hardware devices, such as the millimeter-wave radar, data processing module 130, and data transmission module 140, are correct and stable. Professional testing equipment is used to test the performance of the hardware devices to ensure proper operation. For example, a signal generator and oscilloscope are used to test the millimeter-wave radar's transmitted and received signals to verify that parameters such as frequency and amplitude meet design requirements.

[0168] In terms of software debugging, each software module, including data acquisition, signal processing, algorithm calculations, and result output, is debugged individually and in a coordinated overall manner. During individual debugging, each module is checked for proper functionality and parameter settings. For example, during data acquisition module debugging, the accuracy and completeness of collected data are checked; during signal processing module 120 debugging, the effectiveness of operations such as denoising and amplification is verified. During coordinated overall debugging, actual drone flight scenarios are simulated to test the overall system performance. By testing under various environmental conditions, such as varying weather and terrain, the system's detection and tracking capabilities for target drones are verified.

[0169] Take appropriate optimization measures for any issues encountered during debugging. If the millimeter-wave radar's detection range is insufficient, this could be due to improper antenna placement or insufficient transmit power. Adjust the antenna's position to better receive the echo signal, or increase the transmit power appropriately. If significant noise interference is detected during signal processing, optimize the denoising algorithm or add hardware filtering circuits. Regarding the angle measurement algorithm, if the angle estimation accuracy is insufficient, further optimize the interpolation method based on quadratic function fitting and the secondary fine scanning strategy, adjusting relevant parameters to improve angle estimation accuracy. Through continuous debugging and optimization, the system achieves optimal performance to meet the needs of practical applications.

[0170] From a theoretical analysis, in terms of ranging, this application is based on the FMCW ranging method. Through the precise processing of the intermediate frequency signal and FFT transformation, it can achieve high-precision distance measurement. Compared with traditional radars, its distance resolution is higher and it can more accurately distinguish target drones at different distances. In terms of speed measurement, the phase difference between multiple Chirps is used to calculate the speed, which effectively improves the precision and accuracy of speed measurement and can more accurately track the changes in the movement speed of the target drone. In terms of angle measurement, the virtual array construction based on MIMO technology and the improved DBF algorithm significantly improve the angular resolution and accuracy. Through the new Doppler compensation model, the influence of speed on angle estimation is effectively eliminated, making the angle estimation more accurate. The improved DBF algorithm reduces the main lobe width while reducing the sidelobe level by adding Taylor window and quadratic function fitting interpolation and secondary fine scanning strategy, thereby improving the resolution and accuracy of angle estimation and being able to more accurately determine the angular position of the target drone.

[0171] In practical applications, such as when operating a drone swarm, this detection system can accurately and in real time obtain the position, speed, and angle information of other drones in the swarm, thereby preventing collisions and ensuring the safe and efficient operation of the swarm. In the field of drone security monitoring, it can quickly and accurately detect and track intruding unidentified drones, promptly identifying potential threats and taking appropriate measures, thereby improving the reliability and timeliness of security monitoring. Furthermore, in complex environmental conditions such as inclement weather and strong light interference, millimeter-wave radar's low environmental impact allows this detection system to maintain stable operation and ensure effective detection of target drones, a feat unmatched by traditional optical detection methods.

[0172] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for active detection of UAV targets based on millimeter wave radar, characterized in that: The millimeter wave radar-based UAV target active detection method includes: Acquire the transmitted frequency modulated continuous wave signal and the echo signal reflected by the target UAV; Mixing the transmitted frequency modulated continuous wave signal with the echo signal to obtain an intermediate frequency signal; Performing a fast Fourier transform on the intermediate frequency signal to obtain distance information of the target UAV; Acquire a plurality of Chirp signals based on the echo signal; Based on the phase difference between several Chirp signals, the speed information of the target UAV is calculated; Performing angle estimation on the echo signal to obtain angle information of the target UAV; Integrating the distance information of the target UAV, the speed information of the target UAV, and the angle information of the target UAV to obtain motion parameters of the target UAV; The motion parameters of the target UAV are transmitted to the flight control system or other data receiving equipment of the UAV.

2. The method for active detection of UAV targets based on millimeter wave radar according to claim 1, characterized in that: Performing angle estimation on the echo signal to obtain angle information of the target UAV includes: Performing phase calibration on the echo signal using a calibration vector to obtain a calibrated signal; Performing Doppler compensation on the calibrated signal to obtain a compensated echo signal; Performing Taylor windowing processing on the compensated echo signal using a Taylor window function to obtain a weighted signal vector; Performing a global coarse scan on the weighted signal vector to obtain the beam output power of the peak point and its adjacent points; Using a quadratic function fitting interpolation method to perform parabolic fitting on the beam output power of the peak point and its adjacent points, and calculating the interpolation coefficient; Based on the interpolation coefficient, a correction angle is calculated; The corrected angle is scanned a second time to obtain angle information of the target UAV.

3. The method for active detection of UAV targets based on millimeter wave radar according to claim 2, characterized in that: The expression of the calibration vector is: Among them, c t is the calibration vector; N is the number of array elements; is the phase error of each array element channel.

4. The method for active detection of UAV targets based on millimeter wave radar according to claim 2, characterized in that: The expression of the Taylor window function is: Where w(n) is the Taylor window function; M is the total length of the window function; nbar is the number of side lobes with approximately constant level near the main lobe; n is the sample point position; and m is the side lobe number control parameter.

5. The method for active detection of UAV targets based on millimeter wave radar according to claim 2, characterized in that: The calculation formula of the interpolation coefficient is: Among them, δ is the interpolation coefficient; P(θ1) and P(θ2) are the adjacent point beam powers; P(θ k ) is the beam power corresponding to the peak point.

6. An active detection system for UAV targets based on millimeter wave radar, characterized in that: The millimeter-wave radar-based UAV target active detection system includes: a signal transceiver module, a signal processing module, a data processing module, and a data transmission module; The signal transceiver module is used to transmit a frequency modulated continuous wave signal and receive an echo signal reflected by the target UAV; The signal processing module includes: a distance measuring unit, a speed measuring unit and an angle measuring unit; The distance measuring unit is used to obtain the distance information of the target UAV; The speed measuring unit is used to obtain the speed information of the target UAV; The angle measurement unit is used to estimate the angle of the echo signal to obtain the angle information of the target drone; The data processing module is used to integrate the distance information of the target drone, the speed information of the target drone, and the angle information of the target drone to obtain the motion parameters of the target drone; The data transmission module is used to transmit the motion parameters of the target UAV to the flight control system of the UAV or other data receiving equipment.

7. The millimeter-wave radar-based UAV target active detection system according to claim 6, characterized in that: The ranging unit includes: A mixing processing submodule, configured to mix the transmitted frequency modulated continuous wave signal with the echo signal to obtain an intermediate frequency signal; The distance information acquisition submodule is used to perform fast Fourier transform on the intermediate frequency signal to obtain the distance information of the target UAV.

8. The millimeter-wave radar-based UAV target active detection system according to claim 6, characterized in that: The speed measuring unit comprises: A Chirp signal acquisition submodule, configured to acquire a plurality of Chirp signals based on the echo signal; The speed information acquisition submodule is used to calculate the speed information of the target UAV based on the phase difference between several Chirp signals.

9. The millimeter-wave radar-based UAV target active detection system according to claim 6, characterized in that: The angle measuring unit comprises: A phase calibration submodule, configured to perform phase calibration on the echo signal using a calibration vector to obtain a calibrated signal; A Doppler compensation submodule, configured to perform Doppler compensation on the calibrated signal to obtain a compensated echo signal; The beamforming submodule is used to perform weighted summation on the compensated echo signals to obtain angle information of the target UAV.

10. The millimeter-wave radar-based UAV target active detection system according to claim 9, characterized in that: The beamforming submodule includes: a windowing subunit, configured to perform Taylor windowing processing on the compensated echo signal using a Taylor window function to obtain a weighted signal vector; A global coarse scanning subunit, configured to perform a global coarse scan on the weighted signal vector to obtain the beam output power of a peak point and its adjacent points; a parabola fitting subunit, configured to perform parabola fitting on the beam output power of the peak point and its adjacent points using an interpolation method of quadratic function fitting, and calculate an interpolation coefficient; A correction angle calculation subunit, configured to calculate a correction angle based on the interpolation coefficient; The secondary fine scanning subunit is used to perform a secondary fine scanning on the corrected angle to obtain angle information of the target UAV.

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