CN114814815A - Method for solving signal processing crossing loss based on interpolation and single-point DFT filtering - Google Patents
Method for solving signal processing crossing loss based on interpolation and single-point DFT filtering Download PDFInfo
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- G—PHYSICS
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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- G—PHYSICS
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The invention provides a method for solving signal processing crossing loss based on interpolation and single-point DFT filtering, which comprises the following steps: performing target detection on the obtained distance-Doppler-energy spectrum, and obtaining dimension indexes and energy of a target point and Doppler adjacent points thereof; performing interpolation processing, obtaining accurate Doppler coordinates according to interpolation processing results and generating a single-point DFT filtering rotation factor; and filtering the slow time data vector by using the rotation factor to obtain the range-Doppler processing data of the target and measuring the angle to obtain the angle information of the target. The method estimates the accurate Doppler frequency of the target by interpolation operation, improves the signal-to-noise ratio of the range-Doppler two-dimensional FFT processing result by single-point DFT filtering, solves the single-point DFT filtering rotation factor by using the compensation factor in engineering, has short calculation time and high accuracy, and effectively solves the problem of radar signal crossing loss.
Description
Technical Field
The invention belongs to the field of radar signal processing, relates to a method for solving signal processing crossing loss, in particular to a method for solving signal processing crossing loss based on interpolation and single-point DFT filtering, and can be used in the technical field of radar signal processing such as angle radar, forward radar, imaging radar and the like.
Background
The millimeter wave radar has all-weather and all-day working capacity, can continuously detect the target in severe weather such as rain, snow and fog and special environments such as night, and estimates the distance, the radial speed and the angle of the target. The current common target detection method is to perform distance-doppler two-dimensional FFT processing on echo data to obtain a distance-doppler-energy spectrogram. And searching a peak point on the spectrogram, and extracting data of a plurality of antenna channel distance-Doppler processing results at the peak point for target angle estimation.
Because of the limited frequency sampling points of the two-dimensional FFT, the range frequency and the doppler frequency of the target generally do not exactly fall at the frequency sampling point position of the range-doppler-power spectrogram, and directly extracting the data of the range-doppler processing results of the plurality of antenna channels at the position as the target array data may cause the loss of the signal-to-noise ratio of the array signal, i.e., the crossing loss. When the array signal is used for target angle measurement, the angle measurement accuracy is reduced.
Disclosure of Invention
The invention aims to provide a method for solving crossing loss of signal processing based on interpolation and single-point DFT filtering with small operation amount.
In order to achieve the above object, the present invention provides a method for solving signal processing crossing loss based on interpolation and single-point DFT filtering, comprising:
s1: performing target detection on the obtained distance-Doppler-energy spectrum to extract a peak point corresponding to a target, obtaining the corresponding coordinates (R, D) of the target and the energy P (R, D) thereof at the peak point, and selecting the coordinates (R, D) of the adjacent points along the Doppler axis - ) And (R, D) + ) So as to obtain the energies P (R, D) of adjacent points - ) And P (R, D) + ) (ii) a R and D are the index of the range dimension and the index of the Doppler dimension of the peak point, D-and D, respectively + Indices of neighboring points along the doppler dimension for the peak point;
s2: the peak point and its adjacent point energy value obtained in step S1 are used to determine the energy value of the peak point and its adjacent pointsInterpolating the peak value to obtain the correction deviation delta D of the precise Doppler coordinate of the peak value point relative to the index D of the distance dimension of the peak value point and obtain the precise Doppler coordinate D corresponding to the peak value point peak ；
S3: precise doppler coordinate D corresponding to the peak point obtained in step S2 peak Generating a single-point DFT filtering rotation factor corresponding to the peak point;
s4: extracting the R-th numerical value in the FFT processing result of each chirp distance dimension through the index R of the distance dimension of the peak point obtained in the step S1 to form a slow time data vector;
s5: filtering the slow time data vector obtained in the step S4 by using the single-point DFT twiddle factor obtained in the step S3 to obtain range-doppler processing data X of the target corresponding to the peak point in each antenna channel as array data;
s6: the angle of the target is obtained by the array data X obtained in step S5 using an angle measurement algorithm.
The step S3 includes a step S31: using precise Doppler coordinates D peak Calculating the rotation factor w' of single-point DFT filtering corresponding to the peak point [ n ]](k)；
The single point DFT filtered twiddle factor w' n (k) corresponding to the peak point is:
wherein j is an imaginary unit, N D For the number of FFT processing points in the Doppler dimension, N is 0,1,2 … N D 1, k taking the precise Doppler coordinate D peak 。
The step S3 includes a step S31': performing compensation calculation on Doppler processing twiddle factors generated during distance-Doppler processing to obtain single-point DFT filtering twiddle factors corresponding to peak points;
the single point DFT filtering rotation factor w' n (k) corresponding to the peak point is:
w′[n](k)＝w[n,D]·φ(n,ΔD)，
wherein w [ n, D ] is a Doppler processing rotation factor generated during the distance-Doppler processing, and phi (n, Delta D) is a compensation factor;
the generated Doppler processing twiddle factors w [ n, D ] are:
the compensation factor φ (n, Δ D) is:
wherein j is an imaginary unit, N D For the number of FFT processing points in the Doppler dimension, N is 0,1,2 … N D 1, D0, 1,2 … N D -1; Δ D is the correction offset.
The compensation factor is within delta D epsilon [0,0.5 ∈]Is equally divided into N seg Storing the data in a memory in advance so as to obtain a corresponding compensation factor result nearby; when Δ D ∈ [ -0.5,0]Then, according to phi (n, delta D) conj (phi (n-delta D)), conjugate to obtain corresponding compensation factor; the generated Doppler processing twiddle factors w [ n, D]Are pre-calculated and stored.
In step S1, the range-doppler-power spectrum is a discrete spectrogram obtained by performing discrete fourier transform on an intermediate frequency signal obtained by radar sampling in a fast time dimension and a slow time dimension.
In step S1, the algorithm used for target detection is cell average constant false alarm detection, and the cell average selects one of small constant false alarm detection and large constant false alarm detection.
In the step S2, the interpolation method is a quadratic interpolation algorithm or a dual-beam interpolation algorithm.
In step S4, the obtained slow-time data vector includes N chirp Element N chirp Is the number of chirps.
In the slow time data vector, when radar waveformThe chirp number of which is greater than the FFT processing point number N of the Doppler dimension D The deficient elements in the slow-time data vector are complemented with 0.
The method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering of the invention also comprises S7: repeating the steps S1-S6 until the extraction of the peak points on the spectrum corresponding to all the targets is completed.
The method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering estimates the accurate Doppler frequency of the target by interpolation operation, and performs single-point DFT filtering processing on the slow time data according to the accurate Doppler information of the target obtained after interpolation, thereby improving the signal-to-noise ratio of the distance-Doppler two-dimensional FFT processing result, obtaining the array data with high signal-to-noise ratio, and improving the angle measurement precision; in addition, when filtering processing is carried out, the compensation factor is used for obtaining the single-point DFT filtering rotation factor, the compensation factor is used for carrying out offset processing on the existing filtering rotation factor, certain storage space is consumed, meanwhile, the complexity of calculating the filtering rotation factor is fully reduced, filtering processing is efficiently completed, the calculation time consumption is short, the accuracy is high, and the problem of radar signal crossing loss is effectively solved.
Drawings
Fig. 1 is a flow diagram of a method for solving signal processing crossover loss based on interpolation and single-point DFT filtering according to an embodiment of the invention.
Figure 2 is a range-doppler-power spectrogram according to one embodiment of the present invention.
FIG. 3 is a schematic diagram of quadratic interpolation according to one embodiment of the present invention.
FIG. 4 is a statistical graph of the mean square error between the measured angle result and the true angle obtained by the Monte Carlo experiment under different SNR conditions according to the method for solving the crossing loss of signal processing based on interpolation and single-point DFT filtering of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The technical idea of the invention is as follows: acquiring the energy of a target point and the energy of adjacent Doppler points thereof according to the information of a distance unit and a Doppler unit detected by the target, and performing interpolation operation processing; obtaining target accurate Doppler frequency according to the interpolation processing result and generating a single-point DFT filtering rotation factor; and filtering a plurality of chirp data (slow time data) results of the distance units corresponding to the target points by using the rotation factor to obtain a target array signal with high signal-to-noise ratio, and measuring an angle to obtain target azimuth information.
Based on the above technical idea, as shown in fig. 1, the method for solving the crossing loss of signal processing based on interpolation and single-point DFT filtering of the present invention comprises the following steps:
s1: performing target detection on the obtained distance-Doppler-energy spectrum to extract a peak point corresponding to a target, obtaining the corresponding coordinates (R, D) of the target and the energy P (R, D) thereof at the peak point, and selecting the coordinates (R, D) of the adjacent points along the Doppler axis - ) And (R, D) + ) So as to obtain the energies P (R, D) of adjacent points - ) And P (R, D) + ). R, D are the index of the distance dimension and the index of the Doppler dimension of the peak point, D-and D, respectively + The indexes of the Doppler dimensions of the left adjacent point and the right adjacent point of the peak point along the Doppler dimension are adopted.
In step S1, the range-doppler-power spectrum is a discrete spectrogram obtained by performing discrete fourier transform on an intermediate frequency signal obtained by radar sampling in a fast time dimension and a slow time dimension.
Index R of the distance dimension of a peak point, index D of the Doppler dimension of a peak point and index D of the neighboring point of the peak point along the Doppler dimension - ，D + Are all integers. In the present embodiment, for the neighboring point of the peak point along the doppler axis with coordinates (R, D), the index D of the doppler dimension of the neighboring point on the right side thereof + Should be D +1, the index D of the Doppler dimension of its left-hand neighbor - Should be D-1.
It should be noted that the number of target points (i.e. the peak point corresponding to the target) may be one or more, but S1 of the present invention is processed only for one of the target points.
The target detection algorithm used for extracting the target point (i.e., the peak point corresponding to the target) is usually a CFAR detection method, such as a cell average constant false alarm detection (CA-CFAR), a cell average selection small constant false alarm detection (SOCA-CFAR), a cell average selection large constant false alarm detection (GOCA-CFAR), and the like, and a suitable detection algorithm is selected according to a specific application scenario. In this embodiment, a unit average constant false alarm rate detection (CA-CFAR) algorithm is temporarily selected for target detection.
The shape of the signal peak is not particularly limited, and only a local peak value is needed, which is larger than the amplitude of the adjacent unit.
In this embodiment, the target true distance processing coordinate is set to be 60.1, the doppler processing coordinate is set to be 20.4, and the azimuth angle is set to be 57.33 °. As shown in fig. 2, in the present embodiment, the range coordinate R of the detected target is 60, the index D of the doppler dimension is 20, the index D-of the doppler dimension of the left neighboring point is 19, and the index D of the doppler dimension of the right neighboring point is D + Is 21.
S2: interpolating the peak value through the peak value point and the energy value of the adjacent point of the doppler dimension thereof obtained in the step S1 to obtain a corrected deviation Δ D of the accurate doppler coordinate of the peak value point relative to the index D of the distance dimension of the peak value point, and calculating to obtain the accurate doppler coordinate D corresponding to the peak value point peak 。
In step S2, the correction offset Δ D should be in the range of [ -0.5,0.5 ].
The interpolation algorithm is a quadratic interpolation algorithm or other interpolation algorithms such as a dual-beam interpolation algorithm, and a proper detection algorithm is selected according to a specific application scene. It should be noted that, by using any interpolation method, an estimated value of a specific position of an energy spectrum peak point different from an original frequency point of discrete fourier transform is obtained, and the estimated value is theoretically more accurate than the original frequency point of discrete fourier transform. In this embodiment, a quadratic interpolation algorithm is selected, and the specific method is as follows:
where Δ D is the correction offset, D is the index of the Doppler dimension of the peak point, D - Index of Doppler dimension for left-hand neighboring point, D + Index of Doppler dimension for the right hand neighbor, P (R, D) - ) The left adjacent point.
As shown in fig. 3, in this embodiment, the accurate doppler coordinate corresponding to the peak point obtained by quadratic interpolation is 20.412.
S3: precise doppler coordinate D corresponding to the peak point obtained in step S2 peak And generating the rotation factor of the single-point DFT filtering corresponding to the peak point.
According to the first embodiment of the present invention, the step S3 includes a step S31: using precise Doppler coordinates D peak Calculating the rotation factor w' of single-point DFT filtering corresponding to the peak point [ n ]](k)；
The single-point DFT filtering twiddle factor w' n (k) corresponding to the peak point is:
wherein j is an imaginary unit, N D For the number of FFT processing points in the Doppler dimension, N is 0,1,2 … N D -1, thereby obtaining a product comprising N D Vector of each element, k is the Doppler coordinate corresponding to the single-point DFT filtering, where the precise Doppler coordinate D should be taken peak 。
Wherein, N D The number of the FFT processing points is set artificially, and is generally larger than the number of chirps, but can be modified according to specific situations.
According to the second embodiment of the present invention, the step S3 may also include the step S31': and performing compensation calculation on the Doppler processing rotation factor generated during the distance-Doppler processing to obtain a single-point DFT filtering rotation factor corresponding to the peak point.
The single point DFT filtering rotation factor w' n (k) corresponding to the peak point is:
w′[n](k)＝w[n,D]·φ(n,ΔD)，
where w [ n, D ] is a doppler processing rotation factor that has been generated when performing range-doppler processing, and phi (n, Δ D) is a compensation factor.
The elements of the twiddle factor and the compensation factor are complex data and are expressed in the form of Re + j & Im. Where Re is the real part and Im is the imaginary part.
Wherein the Doppler processing twiddle factors w [ n, D]Number of FFT processing points N according to Doppler dimension of radar system D Determined and generated while performing range-doppler processing on the radar sample signal to obtain a range-doppler-power spectrum before being performed at step S1.
The Doppler processing twiddle factor w [ n, D ] is:
In step S31', the doppler processing twiddle factors W [ n, D ] should be data calculated and stored in advance.
The Doppler processing twiddle factor w [ n, D ] is stored in the following matrix format:
in the formula, the D-th column represents a rotation factor corresponding to the Doppler coordinate D.
The compensation factor φ (n, Δ D) is:
In this embodiment, the compensation factor can be set at Δ D ∈ [0,0.5 ]]Is equally divided into N seg The compensation factors phi (n, delta D) are stored in the memory in advance, so that the corresponding compensation factors phi (n, delta D) are obtained nearby.
Due to equidistant division into N seg And the step size of the compensation factor phi (n, delta D) thus stored is
That is, the storage format of the compensation factor is the following matrix:
the method for obtaining the corresponding compensation factor nearby specifically comprises the following steps:
taking the mth column of the stored compensation factors as the corresponding compensation factor, wherein m is equal to the correction offset delta D divided by the step length delta D and rounded down to the nearest integer.
Preferably, when Δ D ∈ [ -0.5,0], the corresponding compensation factor Φ (n, Δ D) can be found from Φ (n, Δ D) ═ conj (Φ (n, - Δ D)), conjugate.
The conjugate operation represented by the function conj is:
conj(Re+j·Im)＝Re-j·Im。
table 1 shows the results of calculating the single-point DFT filter twiddle factor using the compensation factor and calculating the single-point DFT filter twiddle factor directly. The data in the table are the average results of 10000 Monte Carlo experiments and the randomly generated Doppler coordinates corresponding to the twiddle factors through statistical calculation. As can be seen from the results, the number N of equidistant divisions is reasonably selected seg The calculation time consumption of the twiddle factor can be effectively reduced while the accuracy of the twiddle factor is ensured.
TABLE 1 comparison of results of calculating single-point DFT filtering twiddle factor by compensation factor and directly calculating single-point DFT filtering twiddle factor
In this embodiment, the number of equally spaced divisions N is selected seg At 30, the compensation factor is the 22 th column of the stored compensation factor matrix.
S4: through the index R of the distance dimension of the peak point obtained in step S1, the R-th numerical value in the FFT processing result of the distance dimension of each chirp is extracted to form a slow time data vector.
The distance dimension FFT processing result is obtained by performing distance dimension FFT processing on the sample data of each chirp separately.
The resulting slow-time data vector S contains N chirp Elements, where the nth element of the slow time data vector S is denoted as S (N), N is 0,1,2 … N chirp -1, and typically N chirp Less than N D 。N chirp I.e. the number of chirps. Number of chirps N chirp Determined by the waveform of the peak point.
In step S4, in the slow-time data vector, when the chirp number N of the radar waveform is smaller chirp Number of FFT processing points N larger than Doppler dimension D The deficient elements in the slow-time data vector are complemented with 0.
S5: the slow time data vector obtained in step S4 is filtered using the single-point DFT twiddle factor obtained in step S3, and range-doppler processing data of the target corresponding to the peak point in each antenna channel is obtained as array data X.
The specific operation of filtering by using the single-point DFT twiddle factor (i.e. single-point DFT filtering) is as follows:
where x is the array data, s (N) is the nth element of the slow time data vector, w' (N) is the nth element of the single-point DFT filtering twiddle factor, and N is 0,1,2 … N chirp -1。
S6: from the array data X obtained in step S5, the angle of the target is obtained using an angle measurement algorithm.
The angle measurement algorithm adopts a digital beam forming algorithm (DBF), a multi-signal classification algorithm (MUSIC) and the like, and selects a proper detection algorithm according to a specific application scene. In this embodiment, a digital beam forming algorithm (DBF) is temporarily selected to perform the target angle measurement.
It should be noted that the target points (i.e. the peak points on the spectrum corresponding to the target) may be one or more, but S1 of the present invention is only processed for one of the target points, and S2-S6 may be used for each target point, so the present invention may further include S7: and repeating the steps S1-S6 until the extraction of the peak points on the spectrum corresponding to all the targets is completed.
In this embodiment, a monte carlo simulation experiment is performed 10000 times on a target under different signal-to-noise ratios, the deviation between the target angle and the real angle is counted, and the result of measuring the mean square error is shown in fig. 4. From the results, under the noisy condition, compared with the method for directly extracting the Doppler processing result to measure the angle by adopting the method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering, the angle measurement error can be effectively reduced.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for solving signal processing crossover loss based on interpolation and single-point DFT filtering, comprising:
step S1: performing target detection on the obtained distance-Doppler-energy spectrum to extract a peak point corresponding to a target, obtaining coordinates (R, D) corresponding to the target at the peak point and energy P (R, D) thereof, and selecting coordinates (R, D) of adjacent points along a Doppler axis - ) And (R, D) + ) Thereby to makeObtaining the energies P (R, D) of adjacent points - ) And P (R, D) + ) (ii) a R and D are the index of the distance dimension and the index of the Doppler dimension of the peak point, respectively, D - And D + Indices of neighboring points along the doppler dimension for the peak point;
step S2: interpolating the peak value through the peak value point and the energy value of the adjacent point obtained in the step S1 to obtain a corrected deviation Δ D of the accurate doppler coordinate of the peak value point relative to the index D of the distance dimension of the peak value point, and obtain an accurate doppler coordinate D corresponding to the peak value point peak ；
Step S3: precise doppler coordinate D corresponding to the peak point obtained in step S2 peak Generating a single-point DFT filtering rotation factor corresponding to the peak point;
step S4: extracting the R-th numerical value in the FFT processing result of each chirp distance dimension through the index R of the distance dimension of the peak point obtained in the step S1 to form a slow time data vector;
step S5: filtering the slow time data vector obtained in the step S4 by using the single-point DFT twiddle factor obtained in the step S3 to obtain range-doppler processing data X of the target corresponding to the peak point in each antenna channel as array data;
step S6: the angle of the target is obtained by the array data X obtained in step S5 using an angle measurement algorithm.
2. The method for solving the signal processing crossover loss based on interpolation and single-point DFT filtering as claimed in claim 1, wherein said step S3 comprises the steps of S31: using precise Doppler coordinates D peak Calculating the rotation factor w' of single-point DFT filtering corresponding to the peak point [ n ]](k)；
The single-point DFT filtered twiddle factor w' [ n ] (k) corresponding to the peak point is:
wherein j is an imaginary unit, N D For the number of FFT processing points in the Doppler dimension, N is 0,1,2 … N D 1, k taking the precise Doppler coordinate D peak 。
3. The method for solving the signal processing crossover loss based on interpolation and single-point DFT filtering as claimed in claim 1, wherein said step S3 comprises the steps of S31': performing compensation calculation on Doppler processing twiddle factors generated during distance-Doppler processing to obtain single-point DFT filtering twiddle factors corresponding to peak points;
the single point DFT filtering rotation factor w' n (k) corresponding to the peak point is:
w′[n](k)＝w[n,D]·φ(n,ΔD)，
wherein w [ n, D ] is a Doppler processing rotation factor generated during the distance-Doppler processing, and phi (n, Delta D) is a compensation factor;
the generated Doppler processing twiddle factors w [ n, D ] are:
the compensation factor φ (n, Δ D) is:
wherein j is an imaginary unit, N D For the number of FFT processing points in the Doppler dimension, N is 0,1,2 … N D 1, D0, 1,2 … N D -1; Δ D is the correction offset.
4. The method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering as claimed in claim 3, wherein the compensation factor is within Δ D ∈ [0,0.5 ∈]Is equally divided into N seg Storing the data in a memory in advance so as to obtain a corresponding compensation factor result nearby; when Δ D ∈ [ -0.5,0]Then, based on phi (n, delta D) conj (phi (n, -delta D)), the corresponding conjugate is obtainedA compensation factor;
the generated doppler processing twiddle factors w n, D are pre-calculated and stored.
5. The method for solving the signal processing crossover loss based on interpolation and single-point DFT filtering as recited in claim 1, wherein in said step S1, said range-doppler-energy spectrum is a discrete spectrogram obtained by performing discrete fourier transform in fast and slow time dimensions on an intermediate frequency signal obtained by radar sampling.
6. The method for solving the crossing loss of signal processing based on interpolation and single-point DFT filtering as claimed in claim 1, wherein in said step S1, the algorithm used for target detection is one of cell average constant false alarm detection, cell average selected small constant false alarm detection and cell average selected large constant false alarm detection.
7. The method for solving the signal processing crossover loss based on interpolation and single-point DFT filtering as recited in claim 1, wherein in said step S2, said interpolation method is a quadratic interpolation algorithm or a dual-beam interpolation algorithm.
8. The method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering of claim 1, wherein in said step S4, the obtained slow time data vector contains N chirp Element N chirp Is the number of chirps.
9. The method for solving the signal processing crossing loss based on interpolation and single-point DFT filtering as claimed in claim 8, wherein in the slow time data vector, when the chirp number of radar waveform is larger than the number N of FFT processing points in Doppler dimension D The deficient elements in the slow-time data vector are complemented with 0.
10. The method for solving the signal processing crossover loss based on interpolation and single-point DFT filtering as recited in claim 1, further comprising the step of S7: repeating the steps S1-S6 until the extraction of the peak points on the spectrum corresponding to all the targets is completed.
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