Radar fixed-wing unmanned aerial vehicle using dual-polarization characteristics and clutter recognition method
阅读说明：本技术 一种利用双极化特征的雷达固定翼无人机与杂波识别方法 (Radar fixed-wing unmanned aerial vehicle using dual-polarization characteristics and clutter recognition method ) 是由 杨勇 王雪松 施龙飞 马佳智 庞晨 李永祯 于 2019-08-28 设计创作，主要内容包括：本发明公开一种利用双极化特征的雷达固定翼无人机与杂波识别方法,该方法包括以下步骤：1、匹配滤波；2、脉冲对消；3、对脉冲对消后的输出信号进行FFT运算；4、二维恒虚警率检测；5、多帧检测结果累积与判决；6、双极化通道检测结果匹配识别。本发明方法可以降低雷达虚警,无人机与杂波识别精准；本发明方法无需无人机与杂波先验信息；本发明方法适于工程实现。(The invention discloses a radar fixed wing unmanned aerial vehicle and a clutter recognition method by utilizing dual polarization characteristics, which comprises the following steps: 1. matching and filtering; 2. pulse cancellation; 3. performing FFT operation on the output signal after the pulse cancellation; 4. detecting a two-dimensional constant false alarm rate; 5. accumulating and judging the multi-frame detection results; 6. and matching and identifying the detection result of the dual-polarized channel. The method can reduce the false alarm of the radar, and the unmanned aerial vehicle and the clutter recognition are accurate; the method does not need unmanned aerial vehicles and clutter prior information; the method is suitable for engineering realization.)
1. A radar fixed wing unmanned aerial vehicle and clutter recognition method using dual polarization features is characterized in that: the method comprises the following steps:
step 1, matched filtering: performing matched filtering and windowing on the radar receiving signals;
step 2, pulse cancellation: performing two-pulse cancellation or three-pulse cancellation on the output signals after matched filtering and windowing;
and step 3, filtering by a Doppler filter bank: performing FFT operation on the output signal after the pulse cancellation;
step 4, two-dimensional constant false alarm rate detection: aiming at the Doppler filter bank filtering output obtained in the step 3, carrying out two-dimensional CFAR detection on each distance-Doppler unit signal; judging whether each range-Doppler unit has a target or not;
step 5, multi-frame detection result accumulation and judgment: detecting a result for each range-doppler cell; performing multi-frame accumulation; after accumulation, setting a threshold to judge whether each range-Doppler unit has a target;
step 6, matching and identifying the detection result of the dual-polarized channel: comparing judgment results obtained after multi-frame accumulation of the horizontal polarization channel and the vertical polarization channel; when a target exists in a certain range-Doppler unit at the same time, two polarization channels exist; judging the existence of a target; otherwise; and judging that the target does not exist.
2. A radar fixed-wing drone and clutter recognition method using dual polarization features according to claim 1; the method is characterized in that: the specific process of the step 1 is as follows:
the dual-polarization radar transmits a linear frequency modulation pulse signal; the transmitted signal can be expressed as
Wherein; a is the amplitude of the transmitted signal; f. of0Is the signal carrier frequency; mu is the frequency modulation slope; t is a pulse repetition period; τ is the pulse width;
the time domain response of the radar matched filter is
h(t)＝as*(t0-t) (1.2)
Wherein; a is a constant; t is t0To ensure a physically realizable time delay for the filter; superscript denotes conjugation; without loss of generality; here; we get
The radar matched filter output may be expressed as
z(t)＝ifft[S(ω)H(ω)] (1.4)
Wherein; ifft denotes inverse fourier transform; s (ω) and H (ω) are Fourier transforms of S (t) and H (t), respectively.
3. A radar fixed-wing drone and clutter recognition method using dual polarization features according to claim 2; the method is characterized in that: further, in order to reduce the output side lobe level of the matched filter, the output of the matched filter is subjected to time domain or frequency domain windowing; adopting a hanning window to carry out frequency domain windowing on the output signal of the matched filter; the windowed output time-domain signal can be represented as
z(t)＝ifft[S(ω)H(ω)F(ω)] (1.5)
Wherein; f (omega) is the frequency response of the hanning window function.
4. The method of claim 1, wherein the method comprises the following steps: the specific process of the step 2 is as follows:
the output after two-pulse cancellation can be expressed as
y(t)＝y(t)-y(t-T) (1.6)
The output after three-pulse cancellation can be expressed as
y(t)＝y(t)-2y(t-T)+y(t-2T) (1.7)。
5. The method of claim 1, wherein the method comprises the following steps: the specific process of the step 3 is as follows:
the discretization of the pulse cancellation output signal can be expressed as y (k); assuming that a radar has N sampling points in a pulse repetition period; the radar comprises M pulse repetition periods in one frame; the total sampling point number of the radar in one frame is MN; arranging the pulse cancellation output discrete signals in one frame into an array of M rows and N columns; the nth row element of the mth row in the array is marked as y (m, n); filtering the array signals by adopting a Doppler filter bank; the doppler filter bank filtering is implemented by FFT on each column of the array of signals:
Yω(n)＝fft[y(1,n) y(2,n) … y(M,n)] (1.8)
wherein; fft represents fourier transform; y isω(n) is an array of M × 1;
arranging output signals obtained after FFT operation is carried out on signals of all the columns into an array of M multiplied by N; the array is a radar range-doppler diagram filtered by a doppler filter bank.
6. The method of claim 1, wherein the method comprises the following steps: the specific process of the step 4 is as follows:
aiming at the Doppler filter bank filtering output { Y obtained in the step 3ω(m, n) }; performing two-dimensional CFAR detection on each range-Doppler unit signal; during detection; the number of the protection units is L; the number of the reference units is P; the two-dimensional CFAR detection judgment expression is
Wherein; alpha is a threshold factor;
carrying out statistical averaging on the signal amplitude of the reference unit; multiplying the obtained average amplitude by a threshold factor to be used as a detection threshold; then comparing the signal amplitude of the range-Doppler unit with detection with a detection threshold; if the detection threshold is larger than the detection threshold, the target is considered to exist; the detection result of the range-Doppler unit is 1; if the detection threshold is smaller than the detection threshold, the target does not exist; the detection result of the range-Doppler unit is 0; finally, recording a decision result array consisting of 0 and 1 as D; which is an mxn matrix.
7. The method of claim 1, wherein the method comprises the following steps: the specific process of the step 5 is as follows:
obtaining a target detection result D of each frame according to the steps 1-4; recording the detection result of the ith frame as Di(ii) a After multi-frame detection; obtaining a multi-frame detection result; accumulating the multi-frame detection results; obtaining a multi-frame detection accumulation result:
wherein; d' is an M multiplied by N matrix;
judging each element in D';
wherein; a is a natural number; the same is true; when a target is present; marking as 1; when the target is not present; is marked as 0; the decision result array consisting of MN 0 or 1 is denoted as D ".
8. The method of claim 1, wherein the method comprises the following steps: the specific process of the step 6 is as follows:
for a dual-polarization radar adopting a single-polarization transmitting and dual-polarization receiving system; each polarization channel can obtain a judgment result D'; the radar adopts horizontal or vertical polarization transmission; receiving signals simultaneously using horizontal and vertical polarizations; the final decision results of the horizontal and vertical polarization channels are respectively D ″HAnd D ″)V(ii) a For D ″)HAnd D ″)VEach element in (1); if D ″)HAnd D ″)VAn element at a certain position is simultaneously 1; judging that the target of the position is the unmanned aerial vehicle; if D ″)HAnd D ″)VOnly one of the elements at a position is 1; the target at that location is determined to be clutter.
Technical Field
The invention relates to a radar identification method, in particular to a radar unmanned aerial vehicle and a clutter identification method, and more particularly relates to a radar fixed wing unmanned aerial vehicle utilizing dual polarization characteristics and a clutter identification method.
Background
The wide use of unmanned aerial vehicle has brought a great deal of facility for people's life, and meanwhile, unmanned aerial vehicle has also brought serious threat for national and personal safety, interests. For example: drones are used for military target strikes, military reconnaissance, terrorist attacks, crimes, and the like. In order to prevent the harm to national and personal safety and benefits caused by abuse of the unmanned aerial vehicle, the unmanned aerial vehicle is effectively detected and identified.
Means to detect and identify drones include acoustic, optical and radar. Among them, the radar has the characteristics of all-time and all-weather, and is concerned about. For radar, the unmanned aerial vehicle belongs to a typical low-small-slow target, and has the advantages of low flying height, small radar cross section and low flying speed. The radar faces clutter interference in both time domain and frequency domain when detecting the unmanned aerial vehicle. When the radial speed of the unmanned aerial vehicle is high, the unmanned aerial vehicle can be effectively detected by a frequency domain or time-frequency two-dimensional detection method. However, some strong clutters are also detected, and the radar is difficult to distinguish the unmanned aerial vehicle and the clutters for a plurality of detected targets. To this, how fully to excavate the characteristic difference of unmanned aerial vehicle and clutter, then utilize the characteristic difference of the two to realize the discernment of unmanned aerial vehicle and clutter, be the key technology that radar unmanned aerial vehicle detected the discernment, have important meaning.
Unmanned aerial vehicles can be divided into two types, one type is rotor unmanned aerial vehicles, and the other type is fixed-wing unmanned aerial vehicles. These two types of unmanned aerial vehicle are similar with other artificial targets, belong to the complicated structure target, and its radar echo has many domain characteristics such as time domain, frequency domain, airspace, polarization domain, and analysis these characteristics are the basis of looking for unmanned aerial vehicle echo and clutter characteristic difference, help radar unmanned aerial vehicle to detect and discern.
At present, more research reports are reported in the research of rotor unmanned aerial vehicle radar echo characteristic. The work mainly aims at characteristics of radar cross section area (RCS) mean value and statistical distribution, Doppler spectrum, micro Doppler spectrum, polarization and the like of the unmanned aerial vehicle to carry out research. And to fixed wing unmanned aerial vehicle echo characteristic research, radar fixed wing unmanned aerial vehicle darkroom and outfield measurement test have all been carried out at home and abroad to combine actual measurement data to assay fixed wing unmanned aerial vehicle RCS characteristic, in addition, do not see the research report in other aspects.
In the unmanned aerial vehicle identification method, foreign scholars develop some internal and external field tests aiming at radar rotor unmanned aerial vehicle identification, and the measured data is processed, analyzed and found: the radar rotor unmanned aerial vehicle can be identified by utilizing characteristics such as target point traces, micro Doppler spectrum characteristics and polarization characteristic parameters formed by long-time observation. However, no relevant public reports are found for radar fixed wing drone identification.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: utilize unmanned aerial vehicle, clutter in the difference of the many frame testing results of each polarization passageway of dual polarization radar in order to discern unmanned aerial vehicle and clutter to reject the clutter, reduce radar false alarm probability, improve radar detection performance. When the unmanned aerial vehicle and the clutter are identified, the clutter and the prior information of the unmanned aerial vehicle are not needed, the method has no additional requirement on a hardware system, is easy to implement, and has strong engineering applicability and high identification probability.
The technical scheme of the invention is as follows: a radar fixed wing unmanned aerial vehicle and clutter recognition method using dual polarization features comprises the following steps:
step 1, matched filtering: and performing matched filtering and windowing on the radar receiving signals.
The dual polarization radar transmits a chirp signal, which may be represented as
Where A is the amplitude of the transmitted signal, f0Is the signal carrier frequency, mu is the frequency modulation slope, T is the pulse repetition period, tau is the pulse width,
the time domain response of the radar matched filter is
h(t)＝as*(t0-t) (1.2)
Wherein a is a constant and t0To ensure a physically realizable time delay for the filter, the superscript indicates taking the conjugate.
Without loss of generality, here we fetch
The radar matched filter output may be expressed as
z(t)＝ifft[S(ω)H(ω)] (1.4)
Where ifft represents the inverse fourier transform, and S (ω) and H (ω) are the fourier transforms of S (t) and H (t), respectively.
To reduce the matched filter output sidelobe levels, the matched filter output is typically windowed in either the time or frequency domain. Here, we perform frequency domain windowing on the output signal of the matched filter by using a hanning window, and the output time domain signal after windowing can be expressed as
z(t)＝ifft[S(ω)H(ω)F(ω)] (1.5)
Wherein F (omega) is the frequency response of the hanning window function.
Step 2, pulse cancellation: performing two-pulse cancellation or three-pulse cancellation on the output signals after matched filtering and windowing;
the output after two-pulse cancellation can be expressed as
y(t)＝y(t)-y(t-T) (1.6)
The output after three-pulse cancellation can be expressed as
y(t)＝y(t)-2y(t-T)+y(t-2T) (1.7)
And step 3, filtering by a Doppler filter bank: and performing FFT operation on the output signal after the pulse cancellation.
The pulse cancellation output signal may be represented as y (k) after discretization. Assume that the radar has N sampling points within a pulse repetition period, and a frame of the radar contains M pulse repetition periods. The total number of sampling points of the radar in one frame is MN. The pulse cancellation output discrete signals in one frame are arranged into an array with M rows and N columns, and the nth row element in the mth row in the array is marked as y (M, N). Filtering the array signal by using a doppler filter bank, wherein the doppler filter bank filtering is realized by performing FFT on each column of the array signal:
Yω(n)＝fft[y(1,n) y(2,n) … y(M,n)] (1.8)
wherein fft represents Fourier transform, Yω(n) is an array of M1.
The output signals after the FFT operation is carried out on the signals of all the columns are arranged into an array of M multiplied by N, and the array is the radar range-Doppler image filtered by the Doppler filter bank.
Step 4, detecting a two-dimensional Constant False Alarm Rate (CFAR): and performing two-dimensional CFAR detection on the signal of each range-Doppler unit, and judging whether a target exists in each range-Doppler unit.
Aiming at the Doppler filter bank filtering output { Y obtained in the step 3ω(m, n) }, two-dimensional CFAR detection is performed for each range-Doppler cell signal. During detection, the number of the protection units is L, the number of the reference units is P, and the two-dimensional CFAR detection judgment expression is
Wherein, alpha is a threshold factor,
The schematic diagram of two-dimensional CFAR detection is shown in fig. 1, in which a distance-doppler cell where a circle is located is a to-be-detected cell, a distance-doppler cell of a shaded portion is a reference cell, and a distance-doppler cell between the to-be-detected cell and the reference cell is a protection cell.
Carrying out statistical averaging on the signal amplitude of the reference unit, multiplying the obtained average amplitude by a threshold factor to be used as a detection threshold, then comparing the signal amplitude of the range-Doppler unit with detection with the detection threshold, if the signal amplitude is larger than the detection threshold, determining that a target exists, and determining that the detection result of the range-Doppler unit is 1; if the detection result is smaller than the detection threshold, the target does not exist, and the detection result of the range-Doppler unit is 0. The final decision result array composed of 0 and 1 is denoted as D, which is an M × N matrix.
The target detection result D of each frame can be obtained according to steps 1-4. Here, the detection result of the i-th frame is denoted as DiAnd after multi-frame detection, multi-frame detection results are obtained. Accumulating the multi-frame detection results to obtain a multi-frame detection accumulation result:
where D' is an M N matrix.
Judging each element in D';
wherein A is a natural number. Similarly, the presence of a target is denoted as 1, and the absence of a target is denoted as 0. The decision result array consisting of MN 0 or 1 is denoted as D ".
Step 6, matching and identifying the detection result of the dual-polarized channel: comparing judgment results obtained after multi-frame accumulation of the horizontal polarization channel and the vertical polarization channel, and judging that a target exists when the two polarization channels simultaneously exist in a certain distance-Doppler unit; otherwise, the target is judged to be absent.
For the dual-polarization radar adopting a single-polarization transmitting and dual-polarization receiving system, each polarization channel obtains a judgment result D'. The radar adopts horizontal or vertical polarization to transmit, and adopts horizontal and vertical polarization to receive signals simultaneously, so that the final decision results of the horizontal and vertical polarization channels are respectively D ″HAnd D ″)V. For D ″)HAnd D ″)VIf D ″ ", each element inHAnd D ″)VIf the elements at a certain position are simultaneously 1, the target at the position is judged to be the unmanned aerial vehicle, and if D ″)HAnd D ″)VIf only one element at a certain position is 1, the target at the position is judged to be clutter.
The invention has the beneficial effects that:
firstly, reduce radar false alarm, unmanned aerial vehicle and clutter discernment are accurate. The invention realizes the identification of the unmanned aerial vehicle target and the clutter false alarm by accumulating and matching the multi-frame detection results of the dual-polarized channel, and has high identification precision. While clutter is identified, radar false alarm is reduced.
And secondly, unmanned aerial vehicles and clutter prior information are not needed. The invention directly adopts the dual-polarization radar to receive signals to carry out processing such as matched filtering, pulse cancellation, Doppler filtering, two-dimensional CFAR detection, multi-frame accumulation judgment, dual-polarization channel matching and the like, and the whole processing link does not need any unmanned aerial vehicle and clutter prior information.
And thirdly, the method is suitable for engineering realization. According to the technical scheme, the method has the advantages of few implementation steps, small calculation amount and no need of modifying the conventional radar hardware system, so that the method is easy to implement and has high engineering applicability.
Drawings
FIG. 1 is a schematic diagram of two-dimensional CFAR detection
FIG. 2 is a flow chart of a dual-polarization radar unmanned aerial vehicle and a clutter detection and identification method provided by the invention;
FIGS. 3a and b are output results after matched filtering according to the present invention;
FIGS. 4a and b are the results of multi-frame detection decision using the present invention;
FIG. 5 is a result of the dual polarized channel detection result matching identification using the present invention;
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
Fig. 2 is a flowchart of a method for identifying a dual-polarization radar unmanned aerial vehicle and clutter detection provided by the present invention, and the method comprises six steps: the first step is as follows: matching and filtering; the second step is that: pulse cancellation; the third step: filtering by a Doppler filter bank; the fourth step: two-dimensional CFAR detection; the fifth step: accumulation and judgment of multi-frame detection results; and a sixth step: and matching and identifying the detection result of the dual-polarized channel.
Fig. 3 to 5 are graphs of results obtained by processing actual measurement data detected by the radar drone.
The test radar is a dual-polarization radar system of the institute of electronic science of national defense science and technology, the unmanned aerial vehicle is a fixed-wing unmanned aerial vehicle, the unmanned aerial vehicle flies back and forth within a range of 3-7 kilometers from the radar at a height of about 400 meters, a radar main beam always aims at the unmanned aerial vehicle in the flying process, and the clutter is ground clutter. During the test, the radar transmits a horizontal or vertical single polarization signal, the receiving adopts the horizontal and vertical polarization for simultaneous receiving, the radar transmits the signal as a linear frequency modulation signal, the pulse width is 5 mus, the bandwidth is 5MHz, the pulse repetition period is 1.25ms, and the radar sampling rate is 10 MHz.
Fig. 3a shows the result of matched filtering of measured data in the case of horizontally polarized transmission and horizontally polarized reception (HH) by the present invention, and fig. 3b shows the result of matched filtering of measured data in the case of horizontally polarized transmission and vertically polarized reception (VH) by the present invention, where the abscissa indicates the number of sampling points and the ordinate indicates the number of pulses. In the figure, there is a bright line near the
Fig. 4a is a detection result of the HH channel signal after multi-frame detection accumulation and decision, and fig. 4b is a detection result of the VH channel signal after multi-frame detection accumulation and decision, the abscissa indicates the number of sampling points (equivalent to distance), and the ordinate indicates the number of pulses (equivalent to doppler frequency). Here, in the two-dimensional CFAR detection, 50 pulses are regarded as one frame, and then, 10 frames of data are total. After each frame of data is subjected to FFT and two-dimensional CFAR detection, accumulation and judgment are carried out on the detection results of 10 frames of data, a protection range-Doppler unit L is 2, the number P of reference range-Doppler units is 8, a threshold factor alpha is 3, and a judgment threshold A is 2. Fig. 4 illustrates that with the present invention, both the HH channel and the VH channel are able to detect targets, some of which are drones and some of which are false alarms caused by clutter.
Fig. 5 shows the result of the dual-polarization channel detection result after matching identification, wherein the abscissa represents the number of sampling points (equivalent to distance) and the ordinate represents the number of pulses (equivalent to doppler frequency). When there is a target in both the HH channel and the VH channel, we determine that the target is an unmanned aerial vehicle. While clutter has large fluctuation and uncertainty, when only one channel of the HH channel and the VH channel detects a target, the target is judged to be clutter, and the clutter is removed to eliminate false alarms. The final unmanned aerial vehicle detection and identification result is shown in fig. 5. The detection identification result is consistent with the real position (namely, the bright line in the figure) of the unmanned aerial vehicle in the figure 3, and the validity of the invention is verified.
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