CN107783129B - Anti-collision millimeter wave radar signal processing method for rotor unmanned aerial vehicle - Google Patents
Anti-collision millimeter wave radar signal processing method for rotor unmanned aerial vehicle Download PDFInfo
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Abstract
Rotor unmanned aerial vehicle anticollision millimeter wave radar signal processing method belongs to the signal processing field for collision between the easy emergence of when solving rotor unmanned aerial vehicle low-altitude flight and the barrier leads to the problem of rotor unmanned aerial vehicle's damage, and the technical essential is: s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2; s2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data; and S3, carrying out CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies.
Description
Technical Field
The invention belongs to the field of signal processing, and relates to a radar signal processing method.
Background
In recent years, with the continuous development of technologies, the price of a civil small-sized rotor unmanned aerial vehicle is lower and lower, and the civil small-sized rotor unmanned aerial vehicle is widely applied to the fields of aerial photography, film shooting, pesticide spraying, on-site rescue, ground remote sensing surveying and mapping, high-voltage line power grid inspection and the like. But because the rotor unmanned aerial vehicle easily takes place when the low-altitude flight with the collision between the barrier, lead to rotor unmanned aerial vehicle's damage. At present, natural objects such as trees and artificial objects such as power lines, telegraph poles and buildings are mainly used as objects threatening the safety of outdoor low-altitude flight of the rotor unmanned aerial vehicle.
Because the working wavelength of the millimeter wave radar is between 1mm and 10mm, compared with other detection modes, the millimeter wave radar 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. Therefore, the invention mainly introduces the realization of the unmanned aerial vehicle obstacle avoidance function method based on the millimeter wave radar.
Disclosure of Invention
In order to solve the problem that the rotor unmanned aerial vehicle is damaged due to collision with a barrier when the rotor unmanned aerial vehicle flies in a low altitude mode, the invention provides a method for processing an anti-collision millimeter wave radar signal of the rotor unmanned aerial vehicle, so that the speed, the distance and the angle of the barrier can be obtained through calculation, and therefore the barrier can be avoided.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a rotor unmanned aerial vehicle anti-collision millimeter wave radar signal processing method comprises the following steps:
s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2;
s2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data;
s3, performing CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies;
s4, calculating to obtain the distance of the unmanned aerial vehicle to the front obstacle target by using the frequency value of the upper sweep frequency and the frequency value corresponding to the lower sweep frequency in the channel 1 obtained in the step S3;
and S5, respectively calculating the phase positions of the channel 1 and the channel 2 obtained in the step S3 according to the respective upper frequency sweep to obtain an azimuth angle.
Further, in the step S3,
if the peak coordinate of the first threshold point of the up-scan band in the channel 1 is p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT-transformed data is a _ p1_ up +1j b _ p1_ up, and the phase is
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT-transformed data is a _ p2_ up +1j × b _ p2_ up, and the phase is
Setting the peak value coordinate of the first threshold passing point of the lower sweep frequency segment in the channel 1 as p1_ down, and setting the frequency value corresponding to the point as f1_ down;
wherein: a represents the data value of the I path, b represents the data value of the Q path, a _ p1 represents that in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, a _ p2 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p2, b _ p1 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, b _ p2 represents in the array formed by a + j × b, and the corresponding coordinate of the peak point of the threshold is p 2.
Further, in the step S4, the frequency value f1_ up of the upper sweep frequency and the frequency value f1_ down corresponding to the lower sweep frequency in the channel 1 obtained in the step S3 are calculated according to a formula
Calculating to obtain the distance of the unmanned aerial vehicle to the obstacle target, wherein T is a triangular wave period, B is a frequency modulation bandwidth, and c is the speed of light;
according to the formula
Calculating to obtain the speed of the unmanned aerial vehicle forward obstacle target, wherein f0Is the center frequency.
Further, in step S5, the phases calculated from the respective upper frequency sweeps in the channel 1 and the channel 2 obtained in step 3 are respectively used as the reference frequency sweeps
And
according to a calculation formula
Further, the dc removing method in step S1 is: calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; and calculating the mean value of the upper frequency sweep IQ data collected by the AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ.
Further, in step S2, a windowing step is further included, which is located after the dc removing step.
Further, the method also comprises a step S6. filtering and tracking, and predicting the distance and speed value at the next measurement moment.
Further, the filtering uses an alpha-beta filter with a constant gain filter having a prediction equation of
X(k+1/k)＝ΦX(k/k)；
The filter equation is:
X(k+1/k+1)＝X(k+1/k)+K[Z(k+1)-H(k+1/k)]；
wherein X (k/k) is a filter value at the time k, X (k +1/k) is a predicted value of the time k to the next time, and Z (k) is an observed value at the time k;
when the target motion equation adopts a constant velocity model, the constant gain matrix K is [ alpha, beta/T ]]TIts state transition matrix
The measurement matrix of the model is H ═ 1, 0]；
Wherein: alpha is more than 0 and less than 1, beta is more than 0 and less than 1.
Has the advantages that:
1. the invention provides a waveform design for realizing a rotor unmanned aerial vehicle anti-collision millimeter wave radar system based on linear frequency modulation triangular waves for the first time;
2. the invention provides a processing process of a rotor unmanned aerial vehicle anti-collision millimeter wave radar signal processing subsystem based on linear frequency modulation triangular waves, the subsystem can detect the relative distance and the relative speed of a front obstacle, and meanwhile, the detection function of a target direction angle can be realized.
Drawings
FIG. 1 is a graph of the frequency variation of a chirped triangular wave FMCW over a frequency sweep period;
figure 2 embodiment rotor unmanned aerial vehicle short distance collision avoidance system signal processing flow chart.
Detailed Description
Example 1: a rotor unmanned aerial vehicle anti-collision millimeter wave radar signal processing method comprises the following steps:
s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2; the dc removing method in step S1 includes: calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; calculating the mean value of the upper sweep IQ data collected by AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ data; the step mainly plays a role in removing direct current.
S2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data; in step S2, a windowing step is further included, which is located after the dc removing step. I, Q data after direct current removal are merged into an I + jQ data form, then windowing processing is carried out, and windowing processing is carried out on the data of the up-scanning frequency band and the down-scanning frequency band in the channel 1 and the data of the up-scanning frequency band in the channel 2. A Hanning window or a Hamming window and the like can be selected to reduce side lobes, so that the detection performance of the target is improved; the hanning window will cause the main lobe to widen and decrease, but the side lobes will decrease significantly.
The Hanning window has the calculation formula of
S3, carrying out CFAR threshold detection on the complex modulus value after FFT,outputting a first peak value point of the threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies; as an embodiment, in step S3, the object closest to the drone is mainly considered as the object with the greatest risk to the drone aircraft, so that the maximum value of all the threshold values is not found, but the peak value of the first threshold value is selected. If the peak coordinate of the first threshold point of the up-scan band in the channel 1 is p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT-transformed data is a _ p1_ up +1j b _ p1_ up, and the phase is
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT-transformed data is a _ p2_ up +1j × b _ p2_ up, and the phase is
Setting the peak value coordinate of the first threshold passing point of the lower sweep frequency segment in the channel 1 as p1_ down, and setting the frequency value corresponding to the point as f1_ down; wherein: a represents the data value of the I path, b represents the data value of the Q path, a _ p1 represents that in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, a _ p2 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p2, b _ p1 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, b _ p2 represents in the array formed by a + j × b, and the corresponding coordinate of the peak point of the threshold is p 2.
S4, calculating to obtain the distance of the unmanned aerial vehicle to the front obstacle target by using the frequency value of the upper sweep frequency and the frequency value corresponding to the lower sweep frequency in the channel 1 obtained in the step S3;
as an example: in the step S4, the frequency value f1_ up of the upper sweep frequency and the frequency value f1_ down corresponding to the lower sweep frequency in the channel 1 obtained in the step S3 are calculated according to the formula
Calculating to obtain the distance of the unmanned aerial vehicle to the obstacle target, wherein T is a triangular wave period, T is 20ms, B is a bandwidth, B is 200MHz, c is the speed of light, and c is 3.0 × 108；
According to the formula
Calculating to obtain the speed of the unmanned aerial vehicle forward obstacle target, wherein f0Is the center frequency, f0＝24.125GHz；
And S5, respectively calculating the phase positions of the channel 1 and the channel 2 obtained in the step S3 according to the respective upper frequency sweep to obtain an azimuth angle.
As an example: in step S5, the phases calculated according to the respective upper frequency sweeps in the channel 1 and the channel 2 obtained in step 3 are respectively obtained
And
according to a calculation formula
As an embodiment, further comprising the steps of: and S6, filtering and tracking, and predicting the distance and the speed value at the next measurement moment.
Further, the signal processing of the unmanned aerial vehicle short-distance anti-collision millimeter wave radar system based on the group and the waveform of the sawtooth wave and the constant frequency wave is completed through the steps, and the resolving process of the relative speed, the relative distance and the corresponding azimuth angle of the single target is completed.
After the unmanned aerial vehicle short-distance anti-collision millimeter wave radar system finishes the resolving process of the relative speed, the relative distance and the corresponding azimuth angle of the target, a filtering and tracking module is needed. Because the system output data has a high refresh rate and the variation of distance, speed and the like is small in a short time, the system can be approximately regarded as uniform motion, the variation rate of the height can be estimated through a certain algorithm, and the distance, the speed value and the like at the next measurement moment can be predicted. The tracking and predicting method is the premise and the basis of the self-adaptive tracking and tracking filter. The main methods currently include linear autoregressive filtering, wiener filtering, weighted least squares filtering, alpha-beta and alpha-beta-gamma filtering, kalman filtering, simplified kalman filtering, and the like.
The present invention recommends the use of an alpha-beta filter. The alpha-beta filter is suitable for the condition that the change rate of the tracking error is relatively uniform, so that the alpha-beta filter is basically suitable for the flight scene of the unmanned aerial vehicle.
In the α - β filter, the prediction equation of the constant gain filter is X (K +1/K) ═ Φ X (K/K), and the filter equation is X (K +1/K +1) ═ X (K +1/K) + K [ Z (K +1) -H (K +1/K) ], where X (K/K) is a filtered value at time K, X (K +1/K) is a predicted value at time K to the next time, and Z (K) is an observed value at time K.
When the target motion equation adopts a constant velocity model, the constant gain matrix K is [ alpha, beta/T ]]TIts state transition matrix
The measurement matrix of the model is H ═ 1, 0]. The alpha-beta filter is a constant gain filter satisfying the long gain matrix K, the state transition matrix phi and the measurement matrix H described by the above expressions, i.e. the constant gain filter
The selection of the parameters a and β in the a- β filter is relevant for the response of the tracking, the convergence speed and the tracking stability. Generally, 0 < alpha < 1Beta is more than 0 and less than 1. In engineering, the values of alpha and beta can be calculated according to a formula, namely
And
where k is the number of times, and α and β take different values as k changes, in practice, these two parameters tend to be constant.
The target speed and distance of single settlement can be filtered, tracked and predicted through the alpha-beta filter. The target tracking can be better realized, the output data is smoother, the appearance of abnormal values is reduced, and the stability of the system is effectively improved.
Example 2: in addition to embodiment 1, this embodiment mainly accomplishes the measurement of the distance, speed and orientation of the environmental obstacle ahead of the unmanned rotorcraft in flight. The front obstacles are mainly aimed at people, trees, walls, nets, high-voltage lines and other targets.
The millimeter wave radar designed by the embodiment has the working frequency of 24GHz or 77GHz, adopts an FMCW continuous wave system, and adopts linear frequency modulation, so that the distance resolution is high. The waveform adopts a chirp triangular wave FMCW, mainly because the calculation of the target distance and speed is realized by the present embodiment. Target distance and speed calculation can be achieved through the upper frequency sweep and the lower frequency sweep of the triangular wave. The rotor unmanned aerial vehicle's of this embodiment design maximum airspeed is 40km/h, and the biggest range finding of unmanned aerial vehicle anticollision is 50m, is higher than the unmanned aerial vehicle anticollision distance on the present market more than 3 times.
The embodiment mainly provides the design of the signal processing part of the anti-collision millimeter wave radar of the unmanned aerial vehicle and a signal processing method.
The radar center frequency f designed by the embodiment is 24.125 GHz. Triangular waves are selected as the emission waveforms, the period is 20ms, and the bandwidth is 200 MHz. The transmit waveform is shown in fig. 1.
In the embodiment, the resolving of the target distance and speed is realized through single-path IQ data, and because the calculation of the target azimuth angle is realized in the embodiment, the embodiment adopts a double-receiving antenna mode, namely, two-path IQ data, and the angle measurement function of the target is realized through the calculation of respective up-scanning frequency bands of two paths.
Rotor unmanned aerial vehicle anticollision millimeter wave radar signal processing flow chart, as shown in fig. 2, concrete realization step is as follows:
1. calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; and calculating the mean value of the upper frequency sweep IQ data collected by the AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ. The step mainly plays a role in removing direct current.
2. And carrying out FFT (fast Fourier transform) on the IQ data which are subjected to direct current removal and are collected by the A/D in the channel 1 and the channel 2, and converting time domain data into frequency data.
3. The embodiment carries out CFAR threshold detection on the complex modulus value after FFT conversion, outputs the first peak value point of the threshold, mainly considers that the object which has the largest risk degree to the unmanned plane and is closest to the unmanned plane, and therefore the maximum value of all the threshold is not found, and the peak value of the first threshold is selected.
Setting the peak coordinate of the first threshold point of the up-scan frequency band in the channel 1 as p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT data is a _ p1_ up +1j b _ p1_ up, and the phase is
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT data is a _ p2_ up +1j × b _ p2_ up, and the phase position is
And if the peak value coordinate of the first threshold point of the lower sweep frequency segment in the channel 1 is p1_ down, the frequency value corresponding to the point is f1_ down.
4. The frequency value f1_ up of the upper sweep frequency in the channel I and the frequency value f1_ down corresponding to the lower sweep frequency obtained in the step three are processed according to a formula
Where 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(ii) a According to the formula
Wherein f is0Is the center frequency, f024.125 GHz. According to the two formulas, the distance and the speed of the unmanned aerial vehicle to the obstacle target are obtained.
5. Respectively calculating the phases of the channel 1 and the channel 2 obtained in the step 3 according to the respective upper frequency sweep
And
the calculation is according to a calculation formula
According to the formula
And calculating the azimuth angle, wherein d is the antenna spacing.
The function of resolving information such as the distance, the speed and the azimuth angle of the unmanned aerial vehicle to the obstacle in front of the unmanned aerial vehicle in operation by the rotor unmanned aerial vehicle anti-collision millimeter wave radar is completed by the steps.
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 A2 is (a2, k1) — (a1+2E, k1), the abscissa of the point A is symmetrical with 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, the coordinate of the point A2 is selected to be too large, that is, the maximum peak point is between a1+2E, and the 2 times deviation E needs to be reducedAnd E is iterated until E is smaller than the set error E. 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
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. The anti-collision millimeter wave radar signal processing method for the rotor unmanned aerial vehicle is characterized by comprising the following steps of:
s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2;
s2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data;
s3, performing CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies;
the method for processing the over-threshold peak point of CFAR threshold detection 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;
s4, calculating to obtain the distance of the unmanned aerial vehicle to the front obstacle target by using the frequency value of the upper sweep frequency and the frequency value corresponding to the lower sweep frequency in the channel 1 obtained in the step S3;
and S5, respectively calculating the phase positions of the channel 1 and the channel 2 obtained in the step S3 according to the respective upper frequency sweep to obtain an azimuth angle.
2. The method for processing millimeter wave radar signals for unmanned gyroplane anti-collision as claimed in claim 1, wherein said step S3 is performed
If the peak coordinate of the first threshold point of the up-scan band in the channel 1 is p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT-transformed data is a _ p1_ up +1j b _ p1_ up, and the phase is
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT-transformed data is a _ p2_ up +1j × b _ p2_ up, and the phase is
Setting the peak value coordinate of the first threshold passing point of the lower sweep frequency segment in the channel 1 as p1_ down, and setting the frequency value corresponding to the point as f1_ down;
wherein: a represents the data value of the I path, b represents the data value of the Q path, a _ p1 represents that in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, a _ p2 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p2, b _ p1 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, b _ p2 represents in the array formed by a + j × b, and the corresponding coordinate of the peak point of the threshold is p 2.
3. The method for processing millimeter wave radar signals for collision avoidance of rotor unmanned aerial vehicles according to claim 1, wherein in step S4, the frequency value f1_ up of the upper sweep and the frequency value f1_ down corresponding to the lower sweep in the channel 1 obtained in step S3 are calculated according to the formula
Calculating to obtain the distance of the unmanned aerial vehicle to the obstacle target, wherein T is a triangular wave period, B is a frequency modulation bandwidth, and c is the speed of light;
according to the formula
Calculating to obtain the speed of the unmanned aerial vehicle forward obstacle target, wherein f0Is the center frequency.
4. A screw as claimed in claim 1The method for processing the anti-collision millimeter wave radar signal of the wing unmanned aerial vehicle is characterized in that in the step S5, the phases obtained by respectively calculating the frequency sweeps of the channel 1 and the channel 2 obtained in the step 3 are respectively obtained
And
according to a calculation formula
Obtaining the phase difference delta psi; according to the formula
And calculating the azimuth angle, wherein d is the antenna spacing and lambda is the radar wavelength.
5. The method for processing millimeter wave radar signals for unmanned gyroplane collision avoidance according to claim 1, wherein the step S1 dc-removing method comprises: calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; and calculating the mean value of the upper frequency sweep IQ data collected by the AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ.
6. The method for millimeter wave radar signal processing for unmanned rotorcraft for collision avoidance according to claim 1, further comprising a windowing step subsequent to the dc removal step in step S2.
7. The method for processing millimeter wave radar signals for unmanned gyroplane collision avoidance according to claim 1, further comprising step s6. filtering and tracking, and predicting the distance and velocity values at the next measurement time.
8. The method of claim 7, wherein the filtering is performed using an α - β filter with a constant gain filter having a prediction equation of
X(k+1/k)＝ΦX(k/k)；
The filter equation is:
X(k+1/k+1)＝X(k+1/k)+K[Z(k+1)-H(k+1/k)]；
wherein X (k/k) is a filter value at the time k, X (k +1/k) is a predicted value of the time k to the next time, and Z (k) is an observed value at the time k;
when the target motion equation adopts a constant velocity model, the constant gain matrix K is [ alpha, beta/T ]]TIts state transition matrix
The measurement matrix of the model is H ═ 1, 0]；
Wherein: alpha is more than 0 and less than 1, beta is more than 0 and less than 1.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7352320B2 (en) * | 2004-05-31 | 2008-04-01 | Fujitsu Ten Limited | FM-CW radar |
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| CN105445714A (en) * | 2015-11-24 | 2016-03-30 | 大连楼兰科技股份有限公司 | Automobile forward direction anticollision system signal processing method |
Non-Patent Citations (2)
| Title |
|---|
| α － β 滤波器的基本问题分析及仿真研究;邱丽原;《电讯技术》;20160430;第56卷(第4期);全文 * |
| 基于双通道射频前端的汽车变道辅助雷达设计与实现;吴鹏飞;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150215(第2期);第45页 * |
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