CN107783128B - Multi-target anti-collision system of fixed-wing unmanned aerial vehicle based on millimeter wave radar - Google Patents
Multi-target anti-collision system of fixed-wing unmanned aerial vehicle based on millimeter wave radar Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
A multi-target anti-collision system of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar belongs to the field of signal processing and comprises an antenna subsystem, a radio frequency subsystem, a signal conditioning subsystem and a signal processing subsystem in order to solve the problem of multi-target anti-collision of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar; the signal processing subsystem enables four paths of I/Q intermediate frequency signals output by the signal conditioning subsystem to be collected into the AD collecting channel, processed and output. The effect is as follows: the combination of all subsystems enables the system to obtain parameters such as distance and speed of the multi-target barrier on the basis of data acquisition, and therefore the possibility of collision between the multi-target barrier and the unmanned aerial vehicle is detected.
Description
Technical Field
The invention belongs to the field of signal processing, and relates to a multi-target anti-collision system of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar.
Background
The unmanned aerial vehicle is an unmanned aerial vehicle and is an unmanned aerial vehicle operated by utilizing a radio remote control device and a self-contained program control device. According to the application field, the method can be divided into military use and civil use.
The civil field of unmanned aerial vehicles can be further subdivided into two main categories: one is administrative departments, such as meteorology, police, surveying and mapping, environmental protection, scientific research, disaster prevention and management, and the like; one is commercial, such as film and television aerial photography, agriculture and forestry plant protection, electric power energy routing inspection and the like. At present, unmanned aerial vehicles in China are widely applied in government departments, and are also rapidly popularized in the commercial field.
The american consumer electronics association data shows that 40 million worldwide civilian drones are expected to sell in 2015, and the market size is expected to increase 55% over the last year, reaching $ 1.3 billion. By 2018, market demand will soon emerge, with the expectation that the global drone market will scale to at least $ 10 billion.
Although the civil unmanned aerial vehicle is not large in scale in the current domestic market, the civil unmanned aerial vehicle is wide in application and huge in future development space. The domestic civil unmanned aerial vehicle is demanded to be 0.5 hundred million dollars in 2013, the domestic civil unmanned aerial vehicle is expected to keep the speed increase of more than 20% in the next 10 years, and the market scale of the domestic unmanned aerial vehicle is expected to approach 3 hundred million dollars in 2022.
Unmanned aerial vehicle mainly divide into rotor unmanned aerial vehicle and fixed wing unmanned aerial vehicle. The two unmanned aerial vehicles have different flying principles, so that the two unmanned aerial vehicles respectively have different characteristics. Many rotor unmanned aerial vehicle relies on the lift that a plurality of rotors produced to come the gravity of balanced aircraft, lets the aircraft can fly, controls the steady and the gesture of aircraft through the rotational speed that changes every rotor. Therefore, the multi-rotor aircraft can hover and fly at any speed within a certain speed range, basically is an aerial flying platform, can be additionally provided with sensors, cameras and the like, even instruments such as manipulators and the like, is simple to operate, and can be operated by people through simple training. At present, unmanned aerial vehicle rotor companies mainly include companies such as Dajiang and parrot.
The fixed-wing unmanned aerial vehicle takes thrust generated by a propeller or a turbine engine as power for forward flight of the aircraft, and the main lifting force is from relative movement of wings and air. Therefore, fixed wing aircraft must have a certain airless relative velocity to fly with lift. Because of the principle, the fixed wing aircraft has the characteristics of high flying speed, economy and high carrying capacity. Fixed wing drones are also very useful, and are generally selected when large voyage and altitude demands are met, such as power line patrol, highway monitoring and the like.
Fixed wing aircraft have both advantages and disadvantages when compared to rotorcraft. Fixed wing aircraft are more forgiving in the face of piloting and technical errors in the air due to their natural ability to glide when they are without power. Fixed wing aircraft also have the ability to fly to greater distances with greater loads at low battery. Fixed wing aircraft are at a disadvantage when precise missions are required. As they must have air flowing over their wings to generate lift. This means that they must remain moving forward, that is they cannot hover in a location like an aircraft and therefore cannot provide an accurate level of camera position. Therefore, for longer and more heavily loaded tasks, a fixed wing is the best choice.
Unmanned aerial vehicles have been developed for many years, and how to let unmanned aerial vehicles perceive distance and avoid obstacles is a great problem all the time. Foreign relevant organizations count that 10 accidents happen to helicopters on average in every 10000h of flight, and among various accidents, the accident rate caused by collision with obstacles in low-altitude flight accounts for about 35%, and far exceeds other accident reasons. The object threatening the outdoor low-altitude flight safety of the unmanned aerial vehicle mainly comprises natural objects such as trees and the like and artificial objects such as power lines, telegraph poles and buildings, wherein the power lines are small in size and difficult to find by naked eyes, so that the flying safety hazard to the unmanned aerial vehicle is the greatest.
The earliest distance measurement method is actually somewhat like a reversing radar, emits electric waves to a distance measurement object through the hearing similar to bats, senses the direction and the position of an object after reflection. An AR.Drone unmanned aerial vehicle under the Parrot flag of French unmanned aerial vehicle company firstly measures distance to the lower part in an ultrasonic mode, so that the unmanned aerial vehicle can fly at the same height; the second generation (XIRO X plorer 2) of the seeker of the zero-degree unmanned aerial vehicle adopts a special infrared mode to measure 360-degree distance, thereby avoiding the obstacle. However, the maximum limitations of radar ranging are: it needs to transmit electric wave first and then reconnaissance electric wave reflection; under the limit of endurance and radio wave emission power, it is difficult to perform long-distance ranging: for example, the ultrasound height of the Parrot Bebop Drone is set to be 8 meters at the maximum distance, and the maximum avoidance radius of the zero degree seeker 2 is 6 meters. Xinntom 4 or Yuneec Typhoon H in Xinjiang penetrates through the binocular sensor, and as long as the automatic obstacle avoidance distance is far longer than that of an ultrasonic radar type obstacle avoidance under a good light environment: the binocular sensor in Da Jiang can judge the obstacle about 15 meters farthest, which is nearly one time farther than the Parrot Bebop Drone. However, the obstacle avoidance is realized by adopting vision, and the obstacle avoidance function is greatly influenced by environmental changes.
According to the technical method, the visual sensor, the infrared sensor and the ultrasonic radar sensor are mainly used for carrying out obstacle avoidance on obstacles in the flight environment of the unmanned aerial vehicle, but due to the fact that the sensors are short in action distance, obstacle avoidance response time is short in the process of fast flight of the fixed-wing unmanned aerial vehicle, and the fixed-wing unmanned aerial vehicle is susceptible to severe weather and sudden environmental change, obstacle avoidance failure and the like, the fixed-wing unmanned aerial vehicle is achieved through the millimeter wave radar sensor. Because the working wavelength of the millimeter wave radar is between 1mm and 10mm, compared with other detection modes, the millimeter wave radar mainly has the advantages of stable detection performance, good environmental adaptation, small size, low price, capability of being used in relatively severe rainy and snowy weather and the like, and the millimeter wave radar has long working distance,
disclosure of Invention
In order to solve the problem of multi-target collision avoidance of a fixed-wing unmanned aerial vehicle of a millimeter wave radar, the invention provides the following technical scheme: a multi-target anti-collision system of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar comprises an antenna subsystem, a radio frequency subsystem, a signal conditioning subsystem and a signal processing subsystem;
the antenna subsystem forms transmitting and receiving beams required by radar detection, radiates a transmitting signal to a designated area and receives a target scattering echo signal in the designated area;
the radio frequency subsystem generates a transmitting signal, and the frequency of the transmitting signal is changed according to the rule of a modulating signal, so that linear frequency modulation continuous waves are output;
the signal conditioning subsystem is used for filtering and amplifying the intermediate frequency analog signal;
the signal processing subsystem enables four paths of I/Q intermediate frequency signals output by the signal conditioning subsystem to be collected into the AD collecting channel, processed and output.
Has the advantages that: according to the scheme, the millimeter wave radar-based multi-target collision avoidance system for the fixed-wing unmanned aerial vehicle is provided, and the combination of all subsystems enables the system to obtain the distance, speed and other parameters of the multi-target obstacle on the basis of data acquisition, so that the possibility of collision between the multi-target obstacle and the unmanned aerial vehicle is detected.
Drawings
FIG. 1 is a block diagram of a multi-target collision avoidance system for a fixed-wing drone based on millimeter wave radar;
FIG. 2 is a diagram of frequency variations within a sweep period of a constant frequency wave and chirp triangular wave group and waveform;
FIG. 3 is a signal processing flow chart of the multi-target collision avoidance system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar.
Detailed Description
Example 1: a multi-target anti-collision system of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar comprises an antenna subsystem, a radio frequency subsystem, a signal conditioning subsystem and a signal processing subsystem;
the antenna subsystem forms transmitting and receiving beams required by radar detection, radiates a transmitting signal to a designated area and receives a target scattering echo signal in the designated area;
the radio frequency subsystem generates a transmitting signal, and the frequency of the transmitting signal is changed according to the rule of a modulating signal, so that linear frequency modulation continuous waves are output;
the signal conditioning subsystem is used for filtering and amplifying the intermediate frequency analog signal;
the signal processing subsystem enables four paths of I/Q intermediate frequency signals output by the signal conditioning subsystem to be collected into the AD collecting channel, processed and output.
Preferably, the antenna subsystem comprises a transmitting antenna and a receiving antenna, wherein the receiving antenna is two receiving antennas consisting of three rows of receiving antennas through a back feed network, and a microstrip rectangular patch is used for forming a grouped array; the transmitting antenna and the receiving antenna are connected with the back microwave circuit through the via holes.
The signal processing subsystem comprises an ARM chip, a power supply module, a serial port module and a CAN module, wherein the AMR chip collects four paths of I/Q intermediate frequency signals output by the signal conditioning subsystem into four paths of AD (analog-to-digital) collecting channels of the ARM chip, the ARM chip processes the signals and outputs the signals through the serial port module and/or the CAN module.
The antenna divides the system to include transmitting antenna and receiving antenna, the radio frequency divides the system to include voltage controlled oscillator and mixer, signal processing divides the system to include signal conditioning circuit and PLL phase-locked loop, signal processing divides the system to include AD converter and ARM chip, and the one end of ARM chip is connected in signal generator, and signal generator connects in voltage controlled oscillator, and voltage-controlled vibrator connects respectively in the first end of transmitter and mixer, and the receiver is connected to the second end of mixer, and signal conditioning circuit is connected to the third end of mixer, and signal conditioning circuit connects the AD converter, and the other end of ARM chip is connected to the AD converter.
As an embodiment, the signal processing method is:
s1, respectively calculating the mean values of IQ two-path data of an upper frequency sweep section and a lower frequency sweep section of a triangular wave FMCW and a constant frequency wave CW1 section acquired by AD in a channel 1, and respectively subtracting the calculated mean values from data points of the IQ two paths; calculating the mean value of IQ two-path data of a constant frequency wave CW2 section acquired by AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ two paths respectively; the step mainly plays a role in removing direct current, and reduces the influence of the direct current on the detection of nearby targets;
s2, windowing is carried out on the time domain data after the direct current is removed in each section of the channel 1 and the channel 2 respectively, a Hanning window, a Hamming window and the like can be selected, side lobes are reduced, and therefore the detection performance of the target is improved;
s3, removing front part of data points of a first section of triangular wave FMCW up-scanning frequency band, a second section of triangular wave FMCW down-scanning frequency band, a third section of constant frequency wave CW1 in the channel 1 and a constant frequency wave CW2 in the channel 2, selecting proper points according to the number of the data points to perform FFT (fast Fourier transform), namely time-frequency change, and converting time domain data into frequency domain data;
and S4, performing threshold detection CFAR on the complex modulus value after the waveform FFT conversion of each section, outputting the position of a threshold point, and designing a corresponding threshold in a mode of selecting a unit average selection to be small and the like for the threshold detection. And calculating the corresponding frequency value according to the point of passing the threshold, and simultaneously calculating the corresponding phase value of the constant frequency band passing the threshold.
S5, calculating a corresponding speed matrix by using the channel 1 constant frequency band frequency matrix;
s6, pairing the data in the frequency matrix corresponding to the upper frequency sweep and the data in the frequency matrix corresponding to the lower frequency sweep of the triangular wave of the channel 1 in pairs to calculate the distance and the speed, correspondingly calculating to obtain a distance matrix and a speed matrix, finding out the coordinate value of the real target in the speed matrix from the distance matrix and the speed matrix, and taking the distance value corresponding to the corresponding coordinate in the distance matrix according to the coordinate value as the distance value of the real target.
And S7, carrying out real speed matching and searching of multiple targets through the speed matrix of the constant frequency wave and the speed matrix of the triangular wave, and simultaneously obtaining the real distance of the multiple targets.
And S8, carrying out multi-target azimuth calculation.
As an example, the step S4:
in the channel 1:
the number of points of the sweep threshold on the triangular wave is N1, and the corresponding position matrix is N_up＝[a1,a2,…an1]According to the formula
Calculating a frequency matrix at the corresponding point, wherein the calculated frequency matrix is F_up＝[fa1,fa2,…fan1](ii) a Wherein: f. ofsTaking the sampling rate as M is the number of points of FFT transformation, N is a position point, and f is a frequency value;
the number of points for sweeping the threshold under the triangular wave is N2, and the corresponding position matrix is N_down＝[b1,b2,…bn2]The corresponding frequency matrix obtained by calculation is F_down＝[fb1,fb2,…fbn2]；
The number of the points of the constant frequency band passing through the threshold is N3, and the corresponding position matrix is N_cw1＝[c1,c2,…cn3]The corresponding frequency matrix obtained by calculation is F_cw1＝[fc1,fc2,…fcn3]Meanwhile, suppose the complex data after FFT transform corresponding to the peak point is a _ cw1+1j _ b _ cw1, and the phase thereof is according to the formula
Calculating to obtain a phase matrix phi corresponding to the point with the threshold crossing pointCW1＝[ψc1,ψc2,…ψcn3]；
In the channel 2:
the number of the threshold-crossing points of the constant frequency band is the same as that of the threshold-crossing points in the channel 1, and the corresponding position matrix is N_cw2＝[c1,c2,…cn3]The corresponding frequency matrix obtained by calculation is F_cw2＝[fc1,fc2,…fcn3]Its corresponding phase matrix psiCW2＝[ψc′1,ψc′2,…ψc′n3](ii) a Wherein: a represents the data value of the I path, b represents the data value of the Q path, and a _ cw1 represents that the coordinate corresponding to the peak value point of the threshold crossing is cw1 in the array formed by a + j × b.
If the position point passing the threshold is equal to 1, the position point is regarded as a direct current component, the direct current component is not used as a target for judgment, and the position point is directly removed;
as an example, in the step S5, the frequency matrix F is used to determine the frequency of the received signal_cw1＝[fc1,fc2,…fcn3]According to a formula of velocity calculation
Obtain its velocity matrix as V_cw1＝[vc1,vc2,…vcn3]Where c is the speed of light, and c is 3 × 108，f0Is the center frequency, f0＝24.125GHz。
As an embodiment, in step S6, the frequency matrix F is swept on the triangular wave of the channel 1_up＝[fa1,fa2,…fan1]Frequency matrix F corresponding to lower sweep frequency_down＝[fb1,fb2,…fbn2]According to the formula
Calculating the distance value according to the formula
Calculating the velocity value, wherein T is the triangular wave period, T is 20ms, B is the bandwidth, B is 200MHz, c is the speed of light, c is 3.0 × 108，f0Is the center frequency, f024.125 GHz; according to the above description, matrix F_up＝[fa1,fa2,…fan1]Data sum matrix F in (1)_down＝[fb1,fb2,…fbn2]The data in (1) are paired and distance is calculatedThe distance and velocity, the calculated distance matrix is
Wherein r isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downThe distance value obtained by calculating the jth element; the velocity matrix calculated is
Wherein v isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downAnd finding out the coordinate value of the real target in the speed matrix V from the distance matrix R and the speed matrix V according to the speed value obtained by calculating the jth element, wherein the distance value corresponding to the corresponding coordinate in the distance matrix R through the coordinate value is the distance value of the real target.
As an example, the step S7 is to pass the velocity matrix V of the constant frequency wave_cw1Matching and searching the real speed of multiple targets by the triangular wave acquired speed matrix V, and acquiring the real distance of the multiple targets;
the specific operation is as follows: a constant frequency wave velocity matrix V_cw1Each speed value in the velocity matrix is matched with the velocity matrix V of the triangular wave in the velocity matrix V, and the velocity matrix V is searched_cw1In the speed matrix V, after the speed of a real target is not found, all data of the row and the column are deleted, so that the unique pairing relation between the frequencies is ensured. According to the speed of the real target, the row value and the column value of the speed matrix V, the distance value corresponding to the row and the column is found in the corresponding distance matrix, and the distance value is the distance value corresponding to the real target under the speed value. Thereby completing the search for all real target distances and velocities.
As an example, in step S8, the phase matrix ψ is obtained by channel 1 constant frequency band CW1CW1＝[ψc1,ψc2,…ψcn3]Phase matrix psi obtained from channel 2 constant frequency band CW2CW2＝[ψc′1,ψc′2,…ψc′n3]By formula
I is more than or equal to 1 and less than or equal to n3, and the phase difference matrix is calculated to be [ delta phi ]c1,Δψc2,…Δψcn3]According to the formula
Calculating an azimuth, wherein: d is the antenna spacing and λ is the wavelength.
1. The invention provides the overall design method and the working principle of the multi-target anti-collision system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar for the first time, the VCO is controlled in a phase-locked loop PLL mode, the accuracy of transmitted waveform data is improved, and therefore, the high performance of the system is realized, and meanwhile, the millimeter wave radar is applied to the fixed-wing unmanned aerial vehicle with the maximum speed of 150km/h, and the farthest detection distance is 150 m.
2. The invention provides a design and a processing method of a fixed wing unmanned aerial vehicle multi-target anti-collision system signal processing part based on a millimeter wave radar. The multi-target detection function is realized by adopting a mode of constant frequency waves, triangular wave groups and waveforms.
Example 2: in embodiment 1, each anti-collision method for the multi-target anti-collision system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar includes the following steps:
s1, an FPGA chip of the signal processing subsystem controls a PLL (phase locked loop) to transmit a modulation signal with a certain amplitude and frequency, the embodiment is a group and waveform of a linear frequency modulation continuous triangular wave and a constant frequency wave, and the phase locked loop can be used for transmitting waveform data more accurately, so that the performance of the system is improved;
s2, a voltage controlled oscillator VCO of the radio frequency subsystem generates a transmitting signal in a certain range under the action of a PLL phase-locked loop, and the frequency of the transmitting signal is changed according to the rule of a modulating signal, so that the working mode of the linear frequency modulation continuous wave FMCW is realized.
S3, one path of the transmitted signal is radiated to the space in front of the flight of the unmanned aerial vehicle through the transmitter, and the other path of the transmitted signal is mixed with the reflected echo signal; the frequency of the echo signal is changed compared with the previous transmitting signal, and the signal obtained after the frequency mixer is the difference frequency signal containing the target information.
S4, inputting the difference frequency signal into an FPGA chip for AD sampling after signal conditioning, namely signal amplification and filtering, and sending the two-channel IQ data of the AD sampling to a DSP signal processing chip of the signal processing subsystem;
s5, carrying out digital signal processing on the sampled two-channel four-path IQ data in a DSP signal processing chip; the digital signal processing mainly comprises FFT time-frequency change, CFAR threshold detection, distance and speed decoupling calculation, azimuth calculation, moving target display (MTI) technology, Moving Target Detection (MTD) technology and the like which may be required in some occasions.
And S5, obtaining relevant information such as the distance, the speed, the angle and the like of the target through signal processing, and accessing the relevant information into the unmanned aerial vehicle main controller or outputting the relevant information to be transmitted back to a host computer or a mobile phone and other terminals for real-time display through a CAN or other communication modes.
As an example: further comprising the steps of:
s6, calculating the distance, the speed and the direction of an obstacle in front of the unmanned aerial vehicle, and carrying out data processing by the unmanned aerial vehicle main controller according to the data information of the distance, the speed and the direction updated in real time for a front target, wherein the data processing mainly comprises filtering, prediction and the like; the method can be carried out by adopting Kalman filtering, prediction and other methods, and the front obstacle target is detected and tracked in real time through a filtering and prediction algorithm. By judging the distance and the speed azimuth angle of the front target and combining the self flying speed of the unmanned aerial vehicle, the obstacle avoidance strategy is planned in advance, so that the obstacle avoidance process in the multi-target environment is completed.
Example 3: as the supplement of embodiment 1 or 2, the fixed-wing unmanned aerial vehicle multi-target collision avoidance system based on millimeter wave radar that this embodiment designed can reach 150m or farther distance, it is the distance that obstacle avoidance sensors such as vision sensor, infrared sensor and ultrasonic radar sensor can not reach, and millimeter wave radar can realize the accurate acquisition of the relevant information of target in the fixed-wing flight place ahead environment, like target and unmanned aerial vehicle's relative distance, relative speed and relative angle etc., and this system can realize in the complex environment many move, the detection of quiet target obstacle, thereby realize that fixed-wing unmanned aerial vehicle is even in complicated many move, in the environment of quiet target obstacle, also can be quick carry out obstacle avoidance action. Therefore, the embodiment mainly introduces an implementation method of a high-performance fixed-wing unmanned aerial vehicle obstacle avoidance system based on a millimeter wave radar sensor.
The fixed-wing unmanned aerial vehicle collision avoidance system based on millimeter wave radar that this embodiment designed, this collision avoidance system mainly calculates the relative distance, relative velocity and the position of many movements, quiet target barrier and unmanned aerial vehicle in fixed-wing unmanned aerial vehicle flight the place ahead environment through adopting millimeter wave radar sensor, and through the perception to a plurality of barrier target characteristics in the place ahead, control decision makes fixed-wing unmanned aerial vehicle's anticollision.
The millimeter wave radar sensor used in this embodiment has an operating frequency in a band of 24GHz or 77GHz, and the basic operating principle of the millimeter radar is the same, so the millimeter radar sensor is not limited to the above two operating bands. The fixed wing anti-collision millimeter wave radar system adopts a linear frequency modulation continuous wave body system LFMCW, and the distance resolution is high mainly due to the millimeter wave radar system of the LFMCW system. In this embodiment, the designed waveform is a chirp triangular waveform, and the target distance and speed of the obstacle are resolved mainly by the upper frequency sweep and the lower frequency sweep of the triangular waveform. Because the maximum flying speed of the fixed-wing unmanned aerial vehicle can reach 150km/h, and obstacle avoidance is carried out for giving the early warning time of 2 s-3 s of the fixed-wing unmanned aerial vehicle, the maximum distance measurement of the collision prevention of the fixed-wing unmanned aerial vehicle designed in the embodiment is 150m, which is higher than the collision prevention distance of the unmanned aerial vehicle on the current market by more than 10 times.
The embodiment firstly provides a general design method and a design block diagram of a multi-target collision avoidance system (hereinafter referred to as a high-performance unmanned aerial vehicle collision avoidance system) of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar.
The high-performance unmanned aerial vehicle collision avoidance system determines the distance, the speed and the azimuth angle of a measured target by using the frequency difference between millimeter radar transmitting signals and echo signals.
The working principle of the unmanned aerial vehicle anti-collision millimeter wave radar system is that the distance and the speed of a target to be detected are determined by using the frequency difference between a transmitting signal and an echo signal. The system generally comprises a phase-locked loop, a Voltage Controlled Oscillator (VCO), a transmitting antenna, a receiving antenna, a mixer, a signal processing module, a digital signal processing module, a main controller and the like. The composition block diagram of the system is shown in fig. 1, and the millimeter wave radar anti-collision system of the high-performance fixed wing unmanned aerial vehicle is mainly divided into an antenna subsystem, a radio frequency subsystem, a signal conditioning subsystem, a signal processing subsystem, a main control system and the like.
The basic working principle of the unmanned aerial vehicle anti-collision millimeter wave radar is as follows:
1. the FPGA chip controls the PLL to emit the chirp triangular wave, namely, outputs a modulation signal (the waveform of a group of a chirp continuous triangular wave and a constant frequency wave in the embodiment) with a certain amplitude and frequency, and the PLL can emit waveform data more accurately, so that the performance of the system is improved.
2. The VCO generates a transmitting signal within a certain range under the action of the PLL, and the frequency of the transmitting signal is changed according to the rule of a modulation signal, so that the working mode of the linear frequency modulation continuous wave FMCW is realized.
3. One path of the emission signal is radiated to the space in front of the flight of the unmanned aerial vehicle through the emitter, and the other path of the emission signal is mixed with the reflected echo signal. The frequency of the echo signal is changed compared with the previous transmitting signal, and the signal obtained after the frequency mixer is the difference frequency signal containing the target information.
4. The difference frequency signals are subjected to signal conditioning, namely signal amplification and filtering, and then input to an FPGA chip for AD sampling, and dual-channel thought IQ data of the AD sampling are sent to a DSP signal processing chip.
5. And carrying out digital signal processing on the sampled two-channel four-path IQ data in the DSP chip. The digital signal processing mainly comprises FFT time-frequency change, CFAR threshold detection, distance and speed decoupling calculation, azimuth calculation, moving target display (MTI) technology, Moving Target Detection (MTD) technology and the like which may be required in some occasions.
6. And then, obtaining relevant information such as the distance, the speed, the angle and the like of the target through certain signal processing, accessing the information into an unmanned aerial vehicle main controller through a CAN (controller area network) or other communication modes, or outputting the information and transmitting the information back to an upper computer or a mobile phone and other terminals through a wireless transmission mode to display the information in real time.
7. Through the calculation to dangerous barrier distance in the place ahead of unmanned aerial vehicle, speed and position, unmanned aerial vehicle main control unit carries out data processing according to the data information to the real-time update of place ahead target, mainly include processing such as filtering prediction, can adopt methods such as kalman filtering and prediction to go on, accomplish real-time detection and tracking to its place ahead obstacle target through filtering and prediction algorithm, through judging place ahead target distance and speed azimuth, combine unmanned aerial vehicle self flying speed, plan in advance and keep away the barrier strategy, thereby accomplish the obstacle avoidance process in the multi-target environment.
In the embodiment, the design of the signal processing part and the signal processing method of the high-performance fixed-wing unmanned aerial vehicle anti-collision millimeter wave radar are provided below.
The multi-obstacle target obstacle avoidance method is used for solving the problem that multi-target simultaneous detection can be realized by a fixed-wing unmanned aerial vehicle with high performance aiming at the complex environment. The method has various main methods for realizing multi-target detection of millimeter waves, and realizes the accurate multi-target detection function by adopting a combined waveform of triangular waves and constant frequency waves.
In this embodiment, the center frequency of the millimeter wave is 24GHz or 77GHz, and the waveform is a combination of a CW signal based on constant frequency modulation and an FMCW signal based on triangular wave modulation. The waveform emission form is that the first section is triangular wave, the working frequency variation range is from 24.025GHz to 24.225GHz, the bandwidth is 200MHz, the period of the triangular wave is 20ms, the second section is constant frequency wave, the working frequency is 24.125GHz, and the period is 20 ms. The frequency variation of the constant frequency wave CW and the chirp triangular wave FMCW within a frequency sweep period is shown in fig. 2:
fixed wing unmanned aerial vehicle multiple target collision avoidance system based on millimeter wave radar that this embodiment designed requires not only mainly to a plurality of targets realization range finding, the function of testing the speed, has certain angle measurement function in addition, keeps away the barrier action to the barrier to fixed wing unmanned aerial vehicle like this and provides better space foundation, can realize rotor unmanned aerial vehicle to the perception ability and the decision-making judgement ability of flight the place ahead environment better. Therefore, the present embodiment employs the acquisition of two-channel IQ data. And calculating the target azimuth angle by a two-channel phase comparison method.
Rotor unmanned aerial vehicle complex environment anticollision system signal processing flow chart based on group and waveform, as shown in fig. 3, concrete implementation steps are as follows:
1. respectively calculating the mean values of IQ two paths of data of an upper frequency sweep section and a lower frequency sweep section of a triangular wave FMCW and a constant frequency wave CW1 section acquired by AD in the channel 1, and respectively subtracting the calculated mean values from data points of the IQ two paths; and calculating the mean value of IQ two paths of data of a constant frequency wave CW2 section acquired by AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ two paths respectively. The step mainly plays a role in removing direct current, and reduces the influence of the direct current on the detection of nearby targets;
2. windowing the time domain data after the direct current is removed in each section of the channel 1 and the channel 2 respectively, wherein a Hanning window, a Hamming window and the like can be selected to reduce side lobes, so that the detection performance of a target is improved;
3. selecting data with high linearity of each section of the first section of the triangular wave FMCW up-scanning frequency band, the second section of the triangular wave FMCW down-scanning frequency band, the third section of the constant frequency wave CW1 section in the channel 1 and the constant frequency wave CW2 section in the channel 2, selecting FFT with proper points according to the number of the data points, carrying out time-frequency change, and converting time domain data into frequency domain data;
4. and performing threshold detection CFAR on the complex modulus value after the FFT conversion of each section of waveform, outputting the position of a threshold point, and designing a corresponding threshold by selecting a unit average selection mode and the like for threshold detection. And calculating the corresponding frequency value according to the point of passing the threshold, and simultaneously calculating the corresponding phase value of the constant frequency band passing the threshold.
In the channel 1, N1 points of the sweep frequency threshold on the triangular wave are set, and the corresponding position matrix is N_up＝[a1,a2,…an1]According to the formula
(fsM is the number of FFT points, N is a position point, and F is a frequency value) to calculate a frequency matrix on the corresponding point, wherein the calculated frequency matrix is F_up＝[fa1,fa2,…fan1]；
The number of points for sweeping the threshold under the triangular wave is N2, and the corresponding position matrix is N_down＝[b1,b2,…bn2]The calculated frequency matrix is F_down＝[fb1,fb2,…fbn2]；
The number of the points of the constant frequency band passing through the threshold is N3, and the corresponding position matrix is N_cw1＝[c1,c2,…cn3]The calculated frequency matrix is F_cw1＝[fc1,fc2,…fcn3]Meanwhile, suppose the complex data after the FFT corresponding to the peak point is a _ cw1+1j × b _ cw1, the phase of which can be according to the formula
Calculating to obtain the phase matrix psi corresponding to the point with the thresholdCW1＝[ψc1,ψc2,…ψcn3](ii) a The number of the points of the constant frequency band threshold crossing points in the channel 2 is the same as the number of the points of the threshold crossing points in the channel 1, and the corresponding position matrix is N_cw2＝[c1,c2,…cn3]The calculated frequency matrix is F_cw2＝[fc1,fc2,…fcn3]Its corresponding phase matrix psiCW2＝[ψc′1,ψc′2,…ψc′n3]。
If the position point passing the threshold is equal to 1, the position point is regarded as a direct current component, the direct current component is not used as a target for judgment, and the position point is directly removed;
5. f calculated from channel 1 in step 2_cw1＝[fc1,fc2,…fcn3]According to a formula of velocity calculation
Obtain its velocity matrix as V_cw1＝[vc1,vc2,…vcn3]Where c is the speed of light, and c is 3 × 108，f0Is the center frequency, f0＝24.125GHz。
6. A frequency matrix F of frequency sweeping on channel-triangular wave obtained in the step 2_up＝[fa1,fa2,…fan1]Frequency matrix F corresponding to lower sweep frequency_down＝[fb1,fb2,…fbn2]According to the formula
Calculating the distance value according to the formula
Calculating the velocity value, wherein T is the triangular wave period, T is 20ms, B is the bandwidth, B is 200MHz, c is the speed of light, c is 3.0 × 108，f0Is the center frequency, f024.125 GHz. According to the above description, matrix F_up＝[fa1,fa2,…fan1]Data sum matrix F in (1)_down＝[fb1,fb2,…fbn2]And (4) pairing the data in (1) to calculate the distance and the speed. The calculated distance matrix is
Wherein r isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downThe distance value obtained by calculating the jth element; the velocity matrix calculated is
Wherein v isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downThe jth element performs the calculated velocity value. It can be seen from the distance matrix R and the velocity matrix V that if the coordinate value of the real target in the velocity matrix is obtained, the distance value corresponding to the corresponding coordinate in the distance matrix R by the coordinate value is the distance value of the real target.
7. Velocity matrix V of following constant frequency wave_cw1And matching and searching the real speed of multiple targets by the triangular wave obtained speed matrix V, and obtaining the real distance of the multiple targets at the same time.
The specific operation is as follows: a constant frequency wave velocity matrix V_cw1Each speed value in the velocity matrix is matched with the velocity matrix V of the triangular wave in the velocity matrix V, and the velocity matrix V is searched_cw1The same speed value and the row value and the column value of the speed value. In the velocity matrix V, after a velocity of a real target is not found, all data of the row and the column are deleted, so that a unique pairing relationship between frequencies is ensured. According to the speed of the real target, the row value and the column value of the speed matrix V, the distance value corresponding to the row and the column is found in the corresponding distance matrix, and the distance value is the distance value corresponding to the real target under the speed value. Thereby completing the search for all real target distances and velocities.
8. And carrying out multi-target azimuth calculation. In step 2, the phase matrix psi obtained by the channel 1 constant frequency band CW1 is calculatedCW1＝[ψc1,ψc2,…ψcn3]Phase matrix psi obtained from channel 2 constant frequency band CW2CW2＝[ψc′1,ψc′2,…ψc′n3]Corresponding to the data on the column, by formula
Calculating to obtain the phase difference, and obtaining the phase difference matrix of [ delta phi' ]c1,Δψc2,…Δψcn3]. According to the formula
And calculating the azimuth angle, wherein d is the antenna spacing and lambda is the wavelength.
The above steps are related design methods of the signal processing method of the multi-target anti-collision system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar, resolving work of multi-target distance, speed and azimuth in a complex environment is achieved, sensing work of the complex environment with multi-target obstacles in front of flight of the fixed-wing unmanned aerial vehicle is completed, and therefore obstacle avoidance behaviors are made for the fixed-wing unmanned aerial vehicle in the complex environment, and more accurate sensing capability of the complex environment and quicker judging capability and execution capability are provided.
Example 3: for the peak processing in the above solutions, this embodiment provides a peak processing method applied to the signal of the unmanned aerial vehicle:
setting a peak point threshold factor α for limiting the absolute value of the difference between the detected threshold-crossing maximum peak point and the maximum peak point appearing in the previous cycle, so that the absolute value of the difference is not greater than the peak point threshold factor α:
the expression is as follows:
|L_max(k)-L_max(k-1)|≤α；
wherein: l _ max (k) is the maximum peak point coordinate of the threshold passing of the k period, L _ max (k-1) is the maximum peak point coordinate of the last period, and k represents the kth moment; v. ofmaxThe maximum flight speed of the unmanned aerial vehicle is shown, lambda is the wavelength of the millimeter wave radar, fs is the sampling rate, and N is the number of points of FFT;
if the absolute value difference value of the threshold-crossing maximum peak point at the moment k and the threshold-crossing maximum peak point at the moment k-1 is within the set range of the threshold factor alpha of the peak point, the peak point of the kth period is considered to be effective; and if the threshold-crossing maximum peak point exceeds the set peak point threshold factor alpha at the moment k, replacing the peak point output at the moment k with the peak point at the moment k-1.
As explained in the above technical means, in a time unit of an adjacent period, the peak point calculated in the current period and the peak point in the previous period, if the speed does not change in the adjacent period, the peak point will remain unchanged in the adjacent period, however, if the horizontal flying speed of the unmanned aerial vehicle changes in the adjacent period time, the peak point of the current period will change to some extent in the previous period, if the unmanned aerial vehicle is close to the target, the number of points in the current period is smaller than the number of points in the previous period, if the unmanned aerial vehicle is far away from the target, the number of points in the current period is larger than the number of points in the previous period, the variation range of the peak point is the designed threshold factor alpha of the peak point, and the value range selected by the factor mainly depends on the maximum flight speed of the unmanned aerial vehicle in the adjacent period, namely a formula.
Wherein v ismaxThe maximum flight speed of the unmanned aerial vehicle is shown, lambda is the millimeter wave radar wavelength, fs is the sampling rate, and N is the number of points of FFT.
However, if the flight environment of the unmanned rotorcraft changes suddenly, the number of peak points corresponding to the threshold may also continuously exceed the designed threshold factor. If the correction is not carried out, after mutation occurs, the threshold-crossing maximum peak point detected in each period exceeds the set threshold factor, and the threshold-crossing maximum peak point coordinate is corrected to the peak point coordinate at the last moment every time, namely, the value before mutation is also kept by the same value, and the value after mutation cannot be adapted. In order to improve the adaptability of the unmanned aerial vehicle to various environments, a peak point mutation accumulation factor phi is introduced for the unmanned aerial vehicle.
And setting a peak point sudden change accumulation factor phi, wherein the peak point sudden change accumulation factor phi is defined as that if b periods are continuously carried out from the moment k, the value range of b is 5-10, and the threshold-crossing maximum peak point is compared with the threshold-crossing maximum peak point of the previous period and exceeds a threshold factor a, the threshold-crossing maximum peak point calculated at the moment k + b is taken as the threshold-crossing maximum peak point at the moment. In order to ensure the real-time performance of tracking, the value of b is 5-10.
And after the threshold-crossing maximum peak point is obtained in the last step, in order to improve the precision of the measurement of the table system value, a spectrum maximum estimation algorithm for improving the distance measurement precision is provided.
Ideally, the frequency spectrum of the echo difference frequency signal has only one spectral line, but actually, in the using process, due to the barrier effect existing in sampling, the spectral line with the maximum amplitude of the discrete frequency spectrum inevitably shifts the position of a spectral peak, so that a certain error exists between the distance value calculated by the peak point and the actual distance. When a spectral peak is shifted, the central spectral line corresponding to the main lobe peak will be shifted to the left or to the right. If the left peak value is larger than the right peak value in the left and right peak values of the threshold-crossing maximum value peak value point, the position of the central spectral line is between the maximum peak value point and the left peak value point, otherwise, the position is between the maximum peak value point and the right peak value point.
Because the spectrum obtained by FFT calculation samples continuous distance spectrum at equal intervals, the maximum point of the spectrum amplitude is necessarily positioned in the main lobe of the curve, and the main lobe has two sampling points. Setting the coordinates of the threshold-crossing maximum peak point A1 as (a1, k1), wherein a1 represents the value of the threshold-crossing maximum peak point, and k1 represents the amplitude value corresponding to the threshold-crossing peak point; the coordinates of the secondary peak points are A3(A3, k3), the coordinate of the central peak point A is (amax, kmax), and e is amax-a1, the point A1, the coordinate of the point A2 symmetric to the point A is (a2, k1) is (a1+2e, k1), and the zero point A4 of the complex envelope is (a4, k1) is (A3+ e, 0);
wherein: a2, a3 and a4 are the values of the over-threshold maximum peak point of the corresponding point, and k3 and k4 are the amplitude values corresponding to the over-threshold peak point of the corresponding point;
a2, A3 and A4 are approximately a straight line, and the linear relationship is as follows:
order to
Then
Setting error E and deviation E to compare, if | E tint<E, the value of the over-threshold peak point at the moment is the value of the required central peak point, if the deviation E is greater than the set error E,
beta is a correction factor, the value range is 1.5-1.9, and the correction factor is selected from the following reasons: due to the initial time
The coordinate of the point a symmetric point a2 is (a2, k1) — (a1+2E, k1), the abscissa of the point a is symmetric to the abscissa of the point a2 about the maximum peak point under the initial condition, that is, the coordinate of the point a2 is a1+2E, if the deviation E is greater than the set error E, it means that the coordinate of the point a2 is selected too large, that is, the maximum peak point is between a1+2E, and the 2-fold deviation E needs to be reduced. The value principle of the correction factor beta can be selected according to the required E value, if the required precision of E is not high, the correction factor beta can be selected to be 1.9 for correction, if the required precision of E is high, multiple iterations are possibly required to meet the requirement, the correction factor beta needs to be selected to be as small as possible, and 1.5 can be selected for correction. The value of e calculated by the correction factor is changed to calculate the value amax of the central peak point as a1+ e.
As another embodiment, the method further comprises the steps of: distance tracking: setting a threshold factor epsilon, which is used for limiting the absolute value of the difference between the current distance data H (k) and the distance data H (k-1) appearing in the previous period, so that the absolute value of the difference is not larger than the threshold factor epsilon;
the expression is as follows:
the value of | H (k) | -H (k-1) | is less than or equal to epsilon, and the value range of epsilon is 0.8-1.3;
if the absolute value difference value of the data at the k moment and the absolute value difference value at the k-1 moment are within the range of the set threshold factor epsilon, the peak point of the k-th period is considered to be effective; if the data at time k exceeds the set threshold factor epsilon, the data output at time k is replaced with the data at time k-1.
And setting a sudden change accumulation factor theta, wherein the sudden change accumulation factor theta is defined in that if b periods are continued from the time k, and the data are compared with the data of the previous period and exceed a threshold factor theta, the data obtained by resolving the current time are taken as the data of the current time at the time k + b.
As an embodiment, specifically, in the embodiment, for the distance data which is not subjected to the distance tracking or is subjected to the distance tracking, when outputting, the distance value is output by using a sliding window algorithm for the distance data which is output once;
the data at time k is equal to N in the sliding windowcThe average value of the values after the maximum value and the minimum value are removed is used as the final data output, and the calculation formula is
Wherein N iscRepresenting the number of data points employed by the sliding window.
By adopting the peak value tracking algorithm and the tracking algorithm, the abnormal phenomenon of one or more times of data calculation caused by single or multiple times of peak value searching errors can be effectively avoided, for example, in the single peak value searching process, peak value jumping occurs, the peak value difference value between adjacent periods is large, and simultaneously, the large jumping occurs caused by the jumping with the peak value, namely, the jumping range caused by the peak value jumping in the period is far larger than the distance change range caused by one period caused by the speed of the unmanned aerial vehicle. Therefore, the peak tracking and tracking can effectively avoid abnormal values caused by the abnormal peaks, and the stability of the tracked data is effectively improved.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (8)
1. A multi-target anti-collision system of a fixed-wing unmanned aerial vehicle based on a millimeter wave radar is characterized by comprising an antenna subsystem, a radio frequency subsystem, a signal conditioning subsystem and a signal processing subsystem;
the antenna subsystem forms transmitting and receiving beams required by radar detection, radiates a transmitting signal to a designated area and receives a target scattering echo signal in the designated area;
the radio frequency subsystem generates a transmitting signal, and the frequency of the transmitting signal is changed according to the rule of a modulating signal, so that linear frequency modulation continuous waves are output;
the signal conditioning subsystem is used for filtering and amplifying the intermediate frequency analog signal;
the signal processing subsystem is used for acquiring four paths of I/Q intermediate frequency signals output by the signal conditioning subsystem into the AD acquisition channel, processing the signals and outputting the signals;
the signal processing method comprises the following steps:
s1, respectively calculating the mean values of IQ two-path data of an upper frequency sweep section and a lower frequency sweep section of a triangular wave FMCW and a constant frequency wave CW1 section acquired by AD in a channel 1, and respectively subtracting the calculated mean values from data points of the IQ two paths; calculating the mean value of IQ two-path data of a constant frequency wave CW2 section acquired by AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ two paths respectively;
s2, windowing the time domain data after the direct current is removed in each section in the channel 1 and the channel 2 respectively;
s3, removing front part data points of a first section of triangular wave FMCW up-scanning frequency band, a second section of triangular wave FMCW down-scanning frequency band, a third section of constant frequency wave CW1 in the channel 1 and a constant frequency wave CW2 in the channel 2, performing FFT (fast Fourier transform) conversion, and converting time domain data into frequency domain data;
s4, performing threshold detection CFAR on the complex modulus values after the waveform FFT conversion of each section, outputting the position of a threshold-crossing point, calculating a frequency value corresponding to the threshold-crossing point according to the threshold-crossing point, and calculating a phase value corresponding to the threshold-crossing point of the constant-frequency section; the method for processing the threshold-crossing peak point is characterized by comprising the following steps: setting a peak point threshold factor α for limiting the absolute value of the difference between the detected threshold-crossing maximum peak point and the maximum peak point appearing in the previous cycle, so that the absolute value of the difference is not greater than the peak point threshold factor α:
the expression is as follows:
|L_max(k)-L_max(k-1)|≤α；
wherein: l _ max (k) is the maximum peak point coordinate of the threshold passing of the k period, L _ max (k-1) is the maximum peak point coordinate of the last period, and k represents the kth moment; v. ofmaxThe maximum flight speed of the unmanned aerial vehicle is determined, lambda is the wavelength of the millimeter wave radar, fs is the sampling rate, N is the number of points of FFT (fast Fourier transform), and the object of FFT is sawtooth wave data after windowing;
if the absolute value difference value of the threshold-crossing maximum peak point at the moment k and the threshold-crossing maximum peak point at the moment k-1 is within the set range of the threshold factor alpha of the peak point, the peak point of the kth period is considered to be effective; if the threshold-crossing maximum peak point exceeds the set peak point threshold factor alpha at the moment k, replacing the peak point output at the moment k with the peak point at the moment k-1;
s5, calculating a speed matrix corresponding to the channel 1 constant frequency band frequency matrix;
s6, pairing the data in the frequency matrix corresponding to the upper frequency sweep and the lower frequency sweep of the triangular wave of the channel 1 pairwise to calculate the distance and the speed, correspondingly calculating to obtain a distance matrix and a speed matrix, finding the coordinate value of the real target in the speed matrix from the distance matrix and the speed matrix, and taking the distance value corresponding to the corresponding coordinate in the distance matrix through the coordinate value as the distance value of the real target;
s7, carrying out real speed matching and searching of multiple targets through a speed matrix of the constant frequency wave and a speed matrix of the triangular wave, and simultaneously obtaining real distances of the multiple targets;
and S8, carrying out multi-target azimuth calculation.
2. The multi-target collision avoidance system for fixed-wing drone based on millimeter wave radar as claimed in claim 1, wherein said step S4,
in the channel 1:
the number of points of the sweep threshold on the triangular wave is N1, and the corresponding position matrix is N_up＝[a1,a2,…an1]According to the formula
Calculating a frequency matrix at the corresponding point, wherein the calculated frequency matrix is F_up＝[fa1,fa2,…fan1](ii) a Wherein: f. ofsTaking the sampling rate as M is the number of points of FFT transformation, N is a position point, and f is a frequency value;
the number of points for sweeping the threshold under the triangular wave is N2, and the corresponding position matrix is N_down＝[b1,b2,…bn2]The corresponding frequency matrix obtained by calculation is F_down＝[fb1,fb2,…fbn2]；
The number of the points of the constant frequency band passing through the threshold is N3, and the corresponding position matrix is N_cw1＝[c1,c2,…cn3]The corresponding frequency matrix obtained by calculation is F_cw1＝[fc1,fc2,…fcn3]Meanwhile, suppose the complex data after FFT transform corresponding to the peak point is a _ cw1+1j _ b _ cw1, and the phase thereof is according to the formula
Calculating to obtain a phase matrix phi corresponding to the point with the threshold crossing pointCW1＝[ψc1,ψc2,…ψcn3]；
In the channel 2:
the number of the threshold-crossing points of the constant frequency band is the same as that of the threshold-crossing points in the channel 1, and the corresponding position matrix is N_cw2＝[c1,c2,…cn3]The corresponding frequency matrix obtained by calculation is F_cw2＝[fc1,fc2,…fcn3]Its corresponding phase matrix psiCW2＝[ψ′c1,ψ′c2,…ψ′cn3](ii) a Wherein: a represents the data value of the I path, b represents the data value of the Q path, and a _ cw1 represents that the coordinate corresponding to the peak value point of the threshold crossing is cw1 in the array formed by a + j × b.
3. The multi-target collision avoidance system for fixed-wing drones based on millimeter wave radar as claimed in claim 2, wherein in step S5, the frequency matrix F is used to determine the collision avoidance of the fixed-wing drones_cw1＝[fc1,fc2,…fcn3]According to a formula of velocity calculation
Obtain its velocity matrix as V_cw1＝[vc1,vc2,…vcn3]Where c is the speed of light, f0Is the center frequency.
4. The multi-target collision avoidance system for fixed-wing drone based on millimeter wave radar as claimed in claim 2, wherein in step S6, the frequency matrix F is swept on the triangular wave of the channel 1_up＝[fa1,fa2,…fan1]Frequency matrix F corresponding to lower sweep frequency_down＝[fb1,fb2,…fbn2]According to the formula
Calculating the distance value according to the formula
Calculating the velocity value, wherein T is the period of the triangular wave, B is the bandwidth of the frequency modulation, c is the velocity of light, f0For the center frequency, matrix F_up＝[fa1,fa2,…fan1]Data sum matrix F in (1)_down＝[fb1,fb2,…fbn2]The distance and the speed are calculated by pairing every two data in the data, and the distance matrix obtained by calculation is
Wherein r isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downThe distance value obtained by calculating the jth element; the velocity matrix calculated is
Wherein v isaibj(1. ltoreq. i.ltoreq.n 1, 1. ltoreq. j.ltoreq.n 2) expressed by F in the up-swept matrix_upThe ith element of (a) and F in the lower sweep matrix_downAnd finding out the coordinate value of the real target in the speed matrix V from the distance matrix R and the speed matrix V according to the speed value obtained by calculating the jth element, wherein the distance value corresponding to the corresponding coordinate in the distance matrix R through the coordinate value is the distance value of the real target.
5. The multi-target collision avoidance system for fixed-wing drone based on millimeter wave radar as claimed in claim 4, wherein the step S7 is to pass the velocity matrix V of the constant frequency wave_cw1Matching and searching the real speed of multiple targets by the triangular wave acquired speed matrix V, and acquiring the real distance of the multiple targets;
the specific operation is as follows: a constant frequency wave velocity matrix V_cw1Each speed value in the velocity matrix is matched with the velocity matrix V of the triangular wave in the velocity matrix V, and the velocity matrix V is searched_cw1The same speed value and the row value and the column value of the speed value are in the speed matrix VAnd according to the speed of the real target, finding the distance value corresponding to the row and the column in the corresponding distance matrix according to the row value and the column value of the real target in the speed matrix V, wherein the distance value is the distance value corresponding to the real target under the speed value.
6. The multi-target collision avoidance system for fixed-wing drones based on millimeter wave radar as claimed in claim 4, wherein in step S8, the phase matrix ψ is obtained by channel 1 constant frequency band CW1CW1＝[ψc1,ψc2,…ψcn3]Phase matrix psi obtained from channel 2 constant frequency band CW2CW2＝[ψ′c1,ψ′c2,…ψ′cn3]By formula
Calculating to obtain the phase difference, and obtaining the phase difference matrix of [ delta phi' ]c1,Δψc2,…Δψcn3]According to the formula
Calculating an azimuth, wherein: d is the antenna spacing and λ is the wavelength.
7. The anti-collision method of the multi-target anti-collision system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar as claimed in claim 1, characterized by comprising the following steps:
s1, controlling a PLL phase-locked loop to transmit a modulation signal with certain amplitude and frequency by an FPGA chip of the signal processing subsystem;
s2, a voltage controlled oscillator VCO of the radio frequency subsystem generates a transmitting signal in a certain range under the action of a PLL phase-locked loop, and the frequency of the transmitting signal is changed according to the rule of a modulating signal, so that the working mode of a linear frequency modulation continuous wave FMCW is realized;
s3, one path of the transmitted signal is radiated to the space in front of the flight of the unmanned aerial vehicle through the transmitter, and the other path of the transmitted signal is mixed with the reflected echo signal;
s4, inputting the difference frequency signal to an FPGA chip for AD sampling through signal conditioning, and sending the two-channel IQ data of the AD sampling to a DSP signal processing chip of the signal processing subsystem;
s5, carrying out digital signal processing on the sampled two-channel four-path IQ data in a DSP signal processing chip;
and S5, obtaining relevant information of the distance, the speed and the angle of the target through signal processing, and accessing the relevant information into the main controller of the unmanned aerial vehicle or outputting the relevant information to be transmitted back to an upper computer or a terminal in a wireless transmission mode for real-time display.
8. The anti-collision method for the multi-target anti-collision system of the fixed-wing unmanned aerial vehicle based on the millimeter wave radar as claimed in claim 7, further comprising the steps of:
s6, calculating the distance, the speed and the direction of the obstacle in front of the unmanned aerial vehicle, and carrying out data processing by the unmanned aerial vehicle main controller according to the data information of the distance, the speed and the direction updated in real time for the front target, wherein the data processing mainly comprises filtering and prediction processing; and detecting and tracking the obstacle target in front of the target in real time through a filtering and predicting algorithm.
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