CN112462342A - Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target - Google Patents
Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target Download PDFInfo
- Publication number
- CN112462342A CN112462342A CN202011227427.7A CN202011227427A CN112462342A CN 112462342 A CN112462342 A CN 112462342A CN 202011227427 A CN202011227427 A CN 202011227427A CN 112462342 A CN112462342 A CN 112462342A
- Authority
- CN
- China
- Prior art keywords
- frequency
- time
- signal
- discretization
- self
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G01S7/411—Identification of targets based on measurements of radar reflectivity
Abstract
The invention discloses a stage discretization Virgenahoff transform time-frequency form self-reconstruction detection method for a high maneuvering weak target, and belongs to a target detection method in the field of radar signal processing. The invention analyzes the collected data sample through the Wigner distribution to obtain a time-frequency plane distribution signal. Performing staged window sliding on the time-frequency distribution signals to obtain discrete smooth pseudo-adaptive transformed signals; and projection of a signal on a time-frequency domain frequency axis is taken as a parameter to adaptively adjust the size of a sliding window so as to inhibit cross terms, and then the coordinates of different peaks of the transformed signal are taken as the center to perform sliding window neighborhood accumulation to complete self-term energy accumulation, so that the energy average value of a kernel function of the transformed signal is transferred from a geometric center to a mass center to form energy accumulation, self-reconstruction of the time-frequency energy form of the signal is completed, the signal can be effectively accumulated, the peak value of a weak signal is improved, the detection of a plurality of weak targets is realized, and the calculation amount of an algorithm is reduced.
Description
Technical Field
The invention relates to a target detection method in the field of radar signal processing, in particular to a phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for a high maneuvering weak target.
Background
High maneuvering weak target detection has been a difficult problem that plagues the radar world, mainly because of two problems: on one hand, the target mobility is higher and higher, so that the target echo fluctuation is extremely large, and the target signal energy cannot be gathered by using the traditional Doppler processing mode, so that effective detection and parameter estimation cannot be carried out; on the other hand, targets themselves show the development trend of stealth and miniaturization, and are influenced by external clutter and interference, so that echo signals of the targets are often low in signal-to-noise ratio and are difficult to detect. Around this category of goals, many research institutes and researchers have conducted intensive research and exploration, mainly using time-frequency analysis methods, such as the Wigner-Ville distribution (WVD) method.
The achievement is rich, but for the radar system with extremely high requirements on real-time performance and resolution, only the technology with highest efficiency and minimum cost can be selected for realization. However, a plurality of target time-frequency detection methods have advantages and disadvantages, and no obvious and well-recognized optimal scheme is obtained. This is mainly because when multi-component signals and non-chirped signals exist for multiple targets, cross-term interference occurs in the WVD, which seriously affects the resolvability and interpretability of the time-varying spectral law of the signals. While some improved approaches can improve some cross term suppression problems, most often at the expense of increased complexity and reduced resolution.
At present, most WVD (WVD) time-frequency signal detection methods for high-mobility weak targets lack engineering practicability, and a time-frequency signal rapid detection method based on energy-space difference of multiple signals in time-frequency domain is not reported yet, so that a blank exists in the field.
Disclosure of Invention
The invention provides a stage discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak targets, which is used for carrying out Virgenahoff transformation on the basis of time-frequency analysis, improving weak signal peak values, eliminating the shielding of strong signals on weak signals and effectively detecting a plurality of high maneuvering weak targets at the same time.
The technical scheme provided by the invention is as follows:
a phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for a high maneuvering weak target comprises the following steps:
performing data sampling on radar signals to obtain discretization data samples, analyzing the discretization data samples through the Viger distribution, and distributing the energy of the radar signals in a time-frequency plane to obtain time-frequency plane distribution signals;
carrying out Hough transform on the time-frequency distribution signal to obtain a transform signal after self-adaptive analysis;
and taking the coordinates of different peaks of the transformed signal as a center, and performing summation accumulation in a sliding window neighborhood to ensure that the energy average value of the kernel function of the transformed signal is transferred to a mass center from a geometric center to form energy aggregation, so as to reconstruct the time-frequency energy shape of the transformed signal, improve the peak value of a weak signal and realize target detection.
Preferably, the process of data sampling of the radar signal includes the following steps:
carrying out data sampling on the radar signal to obtain a two-dimensional discretization parameter signal related to time and frequency;
independently resampling the parameter set after the discrete change on a time-frequency plane to obtain a plurality of discretization sample data, dividing the samples with the same or similar Doppler change into a group to obtain a plurality of groups of discretization data samples, and then carrying out staged processing on the discretization data samples.
Preferably, the method further comprises the following steps:
performing self-adaptive sliding window on the time-frequency plane distribution signal to inhibit cross terms of multi-component signals;
and carrying out threshold processing on the time-frequency plane distribution signal in the two-dimensional direction of time and Doppler frequency so as to filter residual isolated noise on the time-frequency plane and obtain a noise-filtering time-frequency distribution signal.
Preferably, the expression of the time and frequency two-dimensional discretization parameter signal is as follows:
wherein, ws(n, k) are two-dimensional discretization parameters of time and frequency, n is a time domain discrete variable, k is a frequency domain discrete variable, n is a sampling point, and when the sampling point n belongs to a Z integer set, l only takes a value at an even number point; when N belongs to Z +1/2, l takes value only at odd points, N is the number of time and frequency units, j is the continuation, s (t) is the continuous signal of the linear frequency modulation wave of the radar,
s (n + l) is the discretized sampling signal, s*(n + l) is a complex conjugate signal of the discretized sampling signal.
Preferably, the expression of the time-frequency plane distribution signal is as follows:
wherein DSPWVD (n.k) is time frequency distribution signal, g (u) is first real even window function, h (l) is second real even window function, n is number of sampling points, fcFor the sampling frequency, n ═ Tmfc，TmIs a chirp period.
Preferably, the adaptive sliding window process of the time-frequency plane distribution signal includes the following steps:
firstly, adopting projection mapping of a time-frequency distribution signal on a frequency axis on a time-frequency domain as a parameter to construct a sliding window weight;
and then carrying out window function weighting on the first real even window function and the second real even window function so as to adaptively control the window width.
Preferably, the calculation formula of the sliding window weight is as follows:
therein, maxk[pi(n,k)]Is pi(n, k) maximum value of each k value sample, maxn{maxk[pi(n,k)]Is the time-frequency global maximum, pi(n, k) are the projection mapping parameters of the frequency axis on the time-frequency plane,
ws(n,k)maxfor a time-frequency two-dimensional discretization of the maximum value, w, of the parameter signals(n,k)minIs the minimum value of the time-frequency two-dimensional discretization parameter signal, and K is the frequency modulation slope.
Preferably, the noise-filtering time-frequency distribution signal is obtained by processing according to the following formula:
wherein, ω is false alarm rate threshold, ω ═ μ ρ n, μ is threshold adjustment parameter determined by false alarm probability, ρ is signal frequency unit number in phase time, n is sampling point number, n isi，kjRespectively time-frequency two-dimensional coordinates, eta is the two-dimensional statistic of the detection unit,
is an estimate of the average peak value in the time direction,
is the mean peak in the Doppler directionThe value of the current is estimated by the value estimation,
l is the number of reference cells of the unit to be measured in the time direction, Y is the number of reference cells of the unit to be measured in the frequency direction, and DSPWVD (n.k) is a time-frequency distribution signal.
Preferably, the hough transform process includes the steps of:
firstly, carrying out Virgenaff transformation in a frequency modulation range, wherein the transformation formula is as follows:
then, carrying out Virgenhou transform accumulation in a stage, wherein the transform formula is as follows:
wherein the frequency modulation range is
giAs frequency modulated estimate, gi＝(ki+1-ki-1)/nΔt，WHs(k, g) is an in-phase Virgenahoff transform signal, WHi(k, g) is the Virgenahoff transform signal in the frequency modulation range, ki-1Initial frequency, k, preceding phase ii+1The initial frequency of the subsequent stage of phase i, Δ t is the sampling interval, D (g)i) To estimate the variance, D (g)i)＝48/(Sn4Δt4π2) (ii) a S is the signal-to-noise ratio, and n is the number of signal sampling points.
Preferably, the cumulative calculation formula of summation in the sliding window neighborhood is as follows:
wherein, WHs(ki0,gj0) ' is the sum of the accumulated values in the neighborhood of the sliding window, gi0For peak initial tuning, ki0For peak initial discrete frequency ki1For peak initial discrete frequency center coordinates, gj1Is the center coordinate of the peak initial tuning frequency.
The invention provides a stage discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak targets, which is used for carrying out Virgenahoff transformation on the basis of time-frequency analysis, improving weak signal peak values, removing the shielding of strong signals on the weak signals and effectively detecting a plurality of high maneuvering weak targets at the same time.
The method uses the time-frequency projection mapping parameters to perform self-adaptive sliding window width control so as to inhibit cross terms to complete filtering; self energy aggregation is completed on signal peaks by adopting a neighborhood accumulation method, and the signal time-frequency energy form is remolded; the peak value of the weak signal is improved, the effective detection of the high maneuvering weak target is realized, and the calculation amount of the algorithm is reduced.
The invention also takes the coordinates of different peaks as the center to carry out summation accumulation in the sliding window neighborhood, further reconstructs the energy form of the time frequency of the transformation signal, and the energy average value of the kernel function of the transformation signal is transferred from the geometric center to the mass center to form energy aggregation, thereby improving the peak value of the weak signal and realizing the target detection.
Drawings
Fig. 1 is a flow chart of a method for detecting self-reconstruction of a phase discretization wigner hough transform time-frequency form of a high maneuvering weak target according to the invention.
Fig. 2 is a flowchart of sampling radar signals and performing time-frequency plane analysis according to the present invention.
Fig. 3 is a flow chart of time-frequency distribution signal wigner hough transform according to the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that in the description of the present invention, the terms "in", "upper", "lower", "lateral", "inner", etc. indicate directions or positional relationships based on those shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; may be a mechanical connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1, based on the technical problem proposed by the background art, the invention provides a phase discretization wigner hough transform time-frequency morphology self-reconstruction detection method for a high maneuvering weak target, which comprises the following steps:
step S110, performing data sampling on the radar signals to obtain discretization data samples, and analyzing the discretization data samples through the Virgener distribution to enable the energy of the radar signals to be distributed in a time-frequency plane to obtain time-frequency plane distribution signals;
step S120, carrying out Virgenaff transformation on the time-frequency distribution signal to obtain a transformation signal after discrete smooth pseudo-adaptive analysis;
and step S130, taking the coordinates of different peaks of the transformation signal as a center, performing summation accumulation in a sliding window neighborhood, transferring the energy average value of the kernel function of the transformation signal from a geometric center to a mass center to form energy aggregation, further reconstructing the time-frequency energy form of the transformation signal, improving the peak value of a weak signal, realizing target detection, eliminating the shielding of the weak signal by a strong signal, and effectively and simultaneously detecting a plurality of high-mobility weak targets.
In another embodiment, the process of data sampling of the radar signal comprises the steps of:
as shown in fig. 2, in step S111, data sampling is performed on the radar signal to obtain a two-dimensional discretization parameter signal with respect to time and frequency, where an expression of the two-dimensional discretization parameter signal with respect to time and frequency is:
wherein, ws(n, k) are two-dimensional discretization parameters of time and frequency, n is a time domain discrete variable, k is a frequency domain discrete variable, n is a sampling point, and when the sampling point n belongs to a Z integer set, l only takes a value at an even number point; when N belongs to Z +1/2, l takes value only at odd points, N is the number of time and frequency units, j is the continuation,
is a continuous chirp signal of radar, s (n + l), s*And (n + l) is the discretized sampling signal and the corresponding complex conjugate signal.
Step S112, independently resampling the parameter set after the discrete change on the time-frequency plane to obtain a plurality of discretized sample data, dividing the samples with the same or similar doppler change into a group to obtain a plurality of groups of discretized data samples, and then performing a staged processing on the discretized data samples.
Preferably, the discrete variation of w is applied in time-frequency planesIndependent resampling of parameter set of (n, k) to obtain nt×nfSamples, e.g. in ntWith one sample in a group, i.e. with division of the echo signal into n1,n2…ntTotal t sub-time points, Doppler of each sample signalThe variations are approximately the same, which corresponds to a staged processing of the signal in the time domain.
Step S113, performing WVD (discrete smoothened Pseudo WVD, dsppwvd) on the discrete samples with similar doppler changes, where the WVD is wiener distribution, and can distribute the discrete samples in a time-frequency plane, where the time-frequency plane distribution signal is:
wherein DSPWVD (n.k) is time frequency distribution signal, g (u) is first real even window function, h (l) is second real even window function, n is number of sampling points, fcFor the sampling frequency, n ═ Tmfc，TmIs a chirp period.
In another embodiment, suppressing the cross terms of the multi-component signals further includes performing adaptive sliding window on the time-frequency plane distribution signals in order to suppress the cross terms of the multi-component signals, and the specific steps include:
and S114, carrying out window function weighting on the first real even window function and the second real even window function, and constructing a sliding window weight by adopting projection mapping of a time-frequency distribution signal on a frequency axis on a time-frequency domain as a parameter to realize self-adaptive control of the width of the sliding window. The sliding window weight, i.e. the window function weight, is calculated by the formula:
to Ws(n, k) the discrete sample stage time-frequency projections in each stage are summed and then the ratio is found, as shown below,
pi(n, k) is a frequency axis projection mapping parameter on the time-frequency plane, ws(n,k)maxFor a time-frequency two-dimensional discretization of the maximum value, w, of the parameter signals(n,k)minIs the minimum value of the time-frequency two-dimensional discretization parameter signal, and K is the frequency modulation slope. Then find maxk[pi(n,k)]pi(n，k) Maximum value and max of each k-value samplen{maxk[pi(n,k)]The time-frequency global maximum value is defined as follows:
step S115, obtaining a noise-filtered time-frequency distribution signal for filtering the residual isolated noise on the time-frequency plane, and performing threshold processing on the time-frequency plane distribution signal in the two-dimensional direction of time and Doppler frequency.
Firstly, setting two-dimensional statistics of a unit to be detected:
wherein the content of the first and second substances,
is an estimate of the average peak value in the time direction,
is an estimate of the average peak in the doppler direction.
If eta does not exceed the threshold omega determined by the constant false alarm rate, the eta is judged as noise, the exceeded retained original value, and the noise filtering time-frequency distribution signal is obtained by processing the following formula:
wherein, ω is the false alarm rate threshold, and ω is μRho n, mu is a threshold adjustment parameter determined by the false alarm probability, rho is the number of signal frequency units in the phase time, n is the number of sampling pointsi，kjThe time-frequency two-dimensional coordinates are respectively, eta is the two-dimensional statistic of the detection unit, L is the number of the reference units of the unit to be detected in the time direction, Y is the number of the reference units of the unit to be detected in the frequency direction, and DSPWVD (n.k) is a time-frequency distribution signal.
In step S120, the time-frequency distribution signal is subjected to wigner Hough transform to obtain a transform signal after discrete smooth pseudo-adaptive analysis, the signal dsppwvd (n.k) is subjected to Hough transform, frequency modulation signals on a time-frequency plane are subjected to discrete summation and aggregation, and then the wigner Hough transform is completed, firstly, the frequency modulation range is pre-estimated to reduce the operation time, and the transform process is as follows:
step S121, the frequency modulation range is
giAs frequency modulated estimate, gi＝(ki+1-ki-1)/nΔt，D(gi) To estimate the variance, D (g)i)＝48/(Sn4Δt4π2) (ii) a S is the signal-to-noise ratio, n is the number of signal sampling points, and Hough is Hough or Hall conversion.
Step S122, carrying out Virgenahoff transformation in a frequency modulation range, wherein the transformation formula is as follows:
step S123, carrying out Virgenaff transformation accumulation in the stage, wherein the transformation formula is as follows:
the peak neighborhood accumulation is to take the coordinates of different peaks as the center, perform summation accumulation in the sliding window neighborhood, so that the energy average value of the kernel function is transferred from the geometric center to the mass center to form energy accumulation, thereby improving the self-term distribution of the frequency modulation signal in a certain range of the peaks, and remodeling the signal time-frequency energy form, wherein the summation accumulation calculation formula in the sliding window neighborhood in the step S130 is as follows:
wherein, WHs(ki0,gj0) ' is the sum of the accumulated values in the neighborhood of the sliding window, gi0For peak initial tuning, ki0For peak initial discrete frequency ki1For peak initial discrete frequency center coordinates, gj1Is the center coordinate of the peak initial tuning frequency.
The method uses the time-frequency projection mapping parameters to perform self-adaptive sliding window width control so as to inhibit cross terms to complete filtering; self-term energy aggregation is completed on signal peaks by adopting a neighborhood accumulation method; the calculation amount and complexity are reduced, and the requirement on the input signal-to-noise ratio is lower; the method substantially utilizes the linear frequency modulation characteristic and the orderliness of signals and the oscillation characteristic and the disorder of cross terms and noise to further cause the spatial form difference of the distribution of the time-frequency domain energy of the signals, and has clear physical meaning and low technical cost; the method can be realized only by downloading the program to the general signal processing board, so the method is easy to popularize, only the programming is needed to be carried out on the general programmable signal processing board, the system structure does not need to be changed, the upgrading is convenient, and the popularization is facilitated.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A phase discretization Virgenah Hough transform time-frequency form self-reconstruction detection method for a high maneuvering weak target is characterized by comprising the following steps:
performing data sampling on radar signals to obtain discretization data samples, analyzing the discretization data samples through the Viger distribution, and distributing the energy of the radar signals in a time-frequency plane to obtain time-frequency plane distribution signals;
carrying out Hough transform on the time-frequency distribution signal to obtain a transform signal after self-adaptive analysis;
and taking the coordinates of different peaks of the transformed signal as a center, and performing summation accumulation in a sliding window neighborhood to ensure that the energy average value of the kernel function of the transformed signal is transferred to a mass center from a geometric center to form energy aggregation, so as to reconstruct the time-frequency energy shape of the transformed signal, improve the peak value of a weak signal and realize target detection.
2. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 1, characterized in that the data sampling process of the radar signal comprises the following steps:
carrying out data sampling on the radar signal to obtain a two-dimensional discretization parameter signal related to time and frequency;
independently resampling the parameter set after the discrete change on a time-frequency plane to obtain a plurality of discretization sample data, dividing the samples with the same or similar Doppler change into a group to obtain a plurality of groups of discretization data samples, and then carrying out staged processing on the discretization data samples.
3. The phase discretization Virgenahoff transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 1 or 2, characterized by further comprising:
performing self-adaptive sliding window on the time-frequency plane distribution signal to inhibit cross terms of multi-component signals;
and carrying out threshold processing on the time-frequency plane distribution signal in the two-dimensional direction of time and Doppler frequency so as to filter residual isolated noise on the time-frequency plane and obtain a noise-filtering time-frequency distribution signal.
4. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 3, characterized in that the expression of the two-dimensional discretization parameter signal of time and frequency is as follows:
wherein, ws(n, k) are two-dimensional discretization parameters of time and frequency, n is a time domain discrete variable, k is a frequency domain discrete variable, n is a sampling point, and when the sampling point n belongs to a Z integer set, l only takes a value at an even number point; when N belongs to Z +1/2, l takes value only at odd points, N is the number of time and frequency units, j is the continuation, s (t) is the continuous signal of the linear frequency modulation wave of the radar,
s (n + l) is the discretized sampling signal, s*(n + l) is a complex conjugate signal of the discretized sampling signal.
5. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 4, characterized in that the expression of the time-frequency plane distribution signal is as follows:
wherein DSPWVD (n.k) is time frequency distribution signal, g (u) is first real even window function, h (l) is second real even window function, n is number of sampling points, fcFor the sampling frequency, n ═ Tmfc，TmIs a chirp period.
6. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 5, characterized in that the self-adaptive sliding window process of the time-frequency plane distribution signal comprises the following steps:
firstly, adopting projection mapping of a time-frequency distribution signal on a frequency axis on a time-frequency domain as a parameter to construct a sliding window weight;
and then carrying out window function weighting on the first real even window function and the second real even window function so as to adaptively control the window width.
7. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 6, characterized in that the calculation formula of the sliding window weight is as follows:
therein, maxk[pi(n,k)]Is pi(n, k) maximum value of each k value sample, maxn{maxk[pi(n,k)]Is the time-frequency global maximum, pi(n, k) are the projection mapping parameters of the frequency axis on the time-frequency plane,
ws(n,k)maxfor a time-frequency two-dimensional discretization of the maximum value, w, of the parameter signals(n,k)minIs the minimum value of the time-frequency two-dimensional discretization parameter signal, and K is the frequency modulation slope.
8. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 7, characterized in that the noise filtering time-frequency distribution signal is obtained by processing according to the following formula:
where ω is the false alarm rate threshold, ω is μ ρ n, and μ is the threshold adjustment determined by the false alarm probabilityParameter, rho is the number of signal frequency units in the phase time, n is the number of sampling points, ni，kjRespectively time-frequency two-dimensional coordinates, eta is the two-dimensional statistic of the detection unit,
is an estimate of the average peak value in the time direction,
is an estimate of the average peak in the doppler direction,
l is the number of reference cells of the unit to be measured in the time direction, Y is the number of reference cells of the unit to be measured in the frequency direction, and DSPWVD (n.k) is a time-frequency distribution signal.
9. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 8, characterized in that the Hough transform process comprises the following steps:
firstly, carrying out Virgenaff transformation in a frequency modulation range, wherein the transformation formula is as follows:
then, carrying out Virgenhou transform accumulation in a stage, wherein the transform formula is as follows:
wherein the frequency modulation range is
giAs frequency modulated estimate, gi＝(ki+1-ki-1)/nΔt，WHs(k, g) is an in-phase Virgenahoff transform signal, WHi(k, g) is the Virgenahoff transform signal in the frequency modulation range, ki-1Initial frequency, k, preceding phase ii+1The initial frequency of the subsequent stage of phase i, Δ t is the sampling interval, D (g)i) To estimate the variance, D (g)i)＝48/(Sn4Δt4π2) (ii) a S is the signal-to-noise ratio, and n is the number of signal sampling points.
10. The phase discretization Virgenah transform time-frequency morphology self-reconstruction detection method of the high maneuvering weak target according to claim 9, characterized in that the summation accumulation calculation formula in the sliding window neighborhood is as follows:
wherein, WHs(ki0,gj0) ' is the sum of the accumulated values in the neighborhood of the sliding window, gi0For peak initial tuning, ki0For peak initial discrete frequency ki1For peak initial discrete frequency center coordinates, gj1Is the center coordinate of the peak initial tuning frequency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011227427.7A CN112462342B (en) | 2020-11-06 | 2020-11-06 | Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011227427.7A CN112462342B (en) | 2020-11-06 | 2020-11-06 | Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112462342A true CN112462342A (en) | 2021-03-09 |
| CN112462342B CN112462342B (en) | 2021-11-02 |
Family
ID=74826677
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011227427.7A Active CN112462342B (en) | 2020-11-06 | 2020-11-06 | Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112462342B (en) |
Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6115113A (en) * | 1998-12-02 | 2000-09-05 | Lockheed Martin Corporation | Method for increasing single-pulse range resolution |
| CN101916241A (en) * | 2010-08-06 | 2010-12-15 | 北京理工大学 | Method for identifying time-varying structure modal frequency based on time frequency distribution map |
| CN102158443A (en) * | 2010-11-19 | 2011-08-17 | 重庆邮电大学 | Method for inhibiting cross terms in time-frequency division of multi-component linear frequency modulation (LFM) signals |
| CN103675759A (en) * | 2013-11-27 | 2014-03-26 | 杭州电子科技大学 | Modified FRFT (fractional Fourier transform) maneuvering weak target detection method |
| CN103837863A (en) * | 2014-03-05 | 2014-06-04 | 中国人民解放军海军航空工程学院 | Distance-speed synchronous pull-off deception jamming recognition algorithm based on gradient projection |
| CN105007130A (en) * | 2015-06-12 | 2015-10-28 | 西安电子科技大学 | Method for detecting LFM signal under low signal-to-noise ratio |
| CN105467446A (en) * | 2014-09-04 | 2016-04-06 | 中国石油化工股份有限公司 | Self-adaptive optimal kernel time-frequency analysis method based on radial Gaussian kernel |
| US20160259849A1 (en) * | 2015-03-05 | 2016-09-08 | Tata Consultancy Services Limited | System and method for reducing size of raw data by performing time-frequency data analysis |
| CN106384076A (en) * | 2016-08-29 | 2017-02-08 | 湖南文理学院 | Sobel operator Wigner-Hough transform based gear fault feature extraction method |
| CN107843894A (en) * | 2017-09-30 | 2018-03-27 | 中国人民解放军战略支援部队航天工程大学 | A kind of ISAR imaging methods of compound movement target |
| WO2018082902A1 (en) * | 2016-11-02 | 2018-05-11 | Robert Bosch Gmbh | Method for determining a rotational angle position of a crankshaft of an internal combustion engine |
| US20180184907A1 (en) * | 2006-06-30 | 2018-07-05 | Koninklijke Philips N.V. | Mesh network personal emergency response appliance |
| CN108267724A (en) * | 2018-01-19 | 2018-07-10 | 中国人民解放军陆军装甲兵学院 | A kind of unknown object recognition methods of radar target recognition |
| CN108761530A (en) * | 2018-05-22 | 2018-11-06 | 闽南师范大学 | A kind of seismic signal spectral factorization method |
| CN108983288A (en) * | 2017-05-31 | 2018-12-11 | 中国石油化工股份有限公司 | Grease recognition methods based on time-frequency spectrum image characteristic analysis |
| CN109085614A (en) * | 2018-07-05 | 2018-12-25 | 南京航空航天大学 | Based on when-spectrum value smoothly with the GNSS interference characteristic discrimination method and system of segmentation |
| CN109284690A (en) * | 2018-08-30 | 2019-01-29 | 北京航天科颐技术有限公司 | Multi-component LFM signalt separation method based on Radon-Wigner transformation and REALX algorithm |
| CN109343020A (en) * | 2018-11-16 | 2019-02-15 | 南京理工大学 | A kind of S-transformation Time-Frequency Analysis Method based on improvement window function |
| CN109375159A (en) * | 2018-10-10 | 2019-02-22 | 中国人民解放军空军预警学院雷达士官学校 | Pure orientation Weighted Constraint total least square localization method |
| CN109521411A (en) * | 2018-12-19 | 2019-03-26 | 西安电子科技大学 | A kind of detection method of range extension target |
| CN110008438A (en) * | 2019-04-08 | 2019-07-12 | 中国科学院上海微系统与信息技术研究所 | A method of analysis linear frequency modulation signal |
| CN110046323A (en) * | 2019-03-25 | 2019-07-23 | 西安交通大学 | The quick calculation method of synchronous compression transformation and reconstruct |
| EP3553551A1 (en) * | 2018-04-10 | 2019-10-16 | Aptiv Technologies Limited | Method for the recognition of an object |
| CN110346785A (en) * | 2018-11-09 | 2019-10-18 | 西安电子科技大学 | Sea based on efficient Time-frequency Decomposition Weak target detecting method at a slow speed |
| US10491325B1 (en) * | 2017-03-22 | 2019-11-26 | Spatial Digital Systems, Inc. | Composite data transport and storage via wavefront multiplexing |
-
2020
- 2020-11-06 CN CN202011227427.7A patent/CN112462342B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6115113A (en) * | 1998-12-02 | 2000-09-05 | Lockheed Martin Corporation | Method for increasing single-pulse range resolution |
| US20180184907A1 (en) * | 2006-06-30 | 2018-07-05 | Koninklijke Philips N.V. | Mesh network personal emergency response appliance |
| CN101916241A (en) * | 2010-08-06 | 2010-12-15 | 北京理工大学 | Method for identifying time-varying structure modal frequency based on time frequency distribution map |
| CN102158443A (en) * | 2010-11-19 | 2011-08-17 | 重庆邮电大学 | Method for inhibiting cross terms in time-frequency division of multi-component linear frequency modulation (LFM) signals |
| CN103675759A (en) * | 2013-11-27 | 2014-03-26 | 杭州电子科技大学 | Modified FRFT (fractional Fourier transform) maneuvering weak target detection method |
| CN103837863A (en) * | 2014-03-05 | 2014-06-04 | 中国人民解放军海军航空工程学院 | Distance-speed synchronous pull-off deception jamming recognition algorithm based on gradient projection |
| CN105467446A (en) * | 2014-09-04 | 2016-04-06 | 中国石油化工股份有限公司 | Self-adaptive optimal kernel time-frequency analysis method based on radial Gaussian kernel |
| US20160259849A1 (en) * | 2015-03-05 | 2016-09-08 | Tata Consultancy Services Limited | System and method for reducing size of raw data by performing time-frequency data analysis |
| CN105007130A (en) * | 2015-06-12 | 2015-10-28 | 西安电子科技大学 | Method for detecting LFM signal under low signal-to-noise ratio |
| CN106384076A (en) * | 2016-08-29 | 2017-02-08 | 湖南文理学院 | Sobel operator Wigner-Hough transform based gear fault feature extraction method |
| WO2018082902A1 (en) * | 2016-11-02 | 2018-05-11 | Robert Bosch Gmbh | Method for determining a rotational angle position of a crankshaft of an internal combustion engine |
| US10491325B1 (en) * | 2017-03-22 | 2019-11-26 | Spatial Digital Systems, Inc. | Composite data transport and storage via wavefront multiplexing |
| CN108983288A (en) * | 2017-05-31 | 2018-12-11 | 中国石油化工股份有限公司 | Grease recognition methods based on time-frequency spectrum image characteristic analysis |
| CN107843894A (en) * | 2017-09-30 | 2018-03-27 | 中国人民解放军战略支援部队航天工程大学 | A kind of ISAR imaging methods of compound movement target |
| CN108267724A (en) * | 2018-01-19 | 2018-07-10 | 中国人民解放军陆军装甲兵学院 | A kind of unknown object recognition methods of radar target recognition |
| EP3553551A1 (en) * | 2018-04-10 | 2019-10-16 | Aptiv Technologies Limited | Method for the recognition of an object |
| CN108761530A (en) * | 2018-05-22 | 2018-11-06 | 闽南师范大学 | A kind of seismic signal spectral factorization method |
| CN109085614A (en) * | 2018-07-05 | 2018-12-25 | 南京航空航天大学 | Based on when-spectrum value smoothly with the GNSS interference characteristic discrimination method and system of segmentation |
| CN109284690A (en) * | 2018-08-30 | 2019-01-29 | 北京航天科颐技术有限公司 | Multi-component LFM signalt separation method based on Radon-Wigner transformation and REALX algorithm |
| CN109375159A (en) * | 2018-10-10 | 2019-02-22 | 中国人民解放军空军预警学院雷达士官学校 | Pure orientation Weighted Constraint total least square localization method |
| CN110346785A (en) * | 2018-11-09 | 2019-10-18 | 西安电子科技大学 | Sea based on efficient Time-frequency Decomposition Weak target detecting method at a slow speed |
| CN109343020A (en) * | 2018-11-16 | 2019-02-15 | 南京理工大学 | A kind of S-transformation Time-Frequency Analysis Method based on improvement window function |
| CN109521411A (en) * | 2018-12-19 | 2019-03-26 | 西安电子科技大学 | A kind of detection method of range extension target |
| CN110046323A (en) * | 2019-03-25 | 2019-07-23 | 西安交通大学 | The quick calculation method of synchronous compression transformation and reconstruct |
| CN110008438A (en) * | 2019-04-08 | 2019-07-12 | 中国科学院上海微系统与信息技术研究所 | A method of analysis linear frequency modulation signal |
Non-Patent Citations (6)
| Title |
|---|
| HONGWEI WANG: "Wigner-Hough transform based on slice’s entropy and its application to multi-LFM signal detection", 《JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS》 * |
| TANG SHIYUE: "The Detection of Multi Moving Targets for The Airborne SAR", 《IEEE XPLORE》 * |
| 潘方博: "基于EMD-RSPWVD算法SAR目标运动参数仿真分析与应用研究", 《遥感技术与应用》 * |
| 穆学禄: "基于Wigner-Hough变换的反辐射导弹检测技术", 《西安电子科技大学学报》 * |
| 苏岭东: "基于DSPWVD-TFPF的通信信号噪声消除算法研究", 《系统仿真学报》 * |
| 陈浩: "基宽带认知雷达中低截获概率波形设计理论与方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112462342B (en) | 2021-11-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111352102B (en) | Multi-target number detection method and device based on frequency modulation continuous wave radar | |
| Boashash | Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications | |
| CN107561508B (en) | Coherent accumulation detection method for uniformly accelerated moving target | |
| WO2022068097A1 (en) | Noise reduction method for improving frequency-modulated continuous wave radar target detection | |
| CN106353737B (en) | A kind of radar pressing type interference detection method based on full range band spectrum analysis | |
| US9991908B2 (en) | Blind source separation of signals having low signal-to-noise ratio | |
| KR100830360B1 (en) | System and method for wideband pre-detection signal processing for passive coherent location applications | |
| CN110133632B (en) | Composite modulation signal identification method based on CWD time-frequency analysis | |
| KR101294681B1 (en) | Apparatus and method for processing weather signal | |
| CN110346772A (en) | A kind of higher-frequency radar significantly ionosphere phase diameter Disturbance Rejection method | |
| CN111856406B (en) | Target detection method and device based on FMCW radar echo | |
| CN107229040B (en) | high-frequency radar target detection method based on sparse recovery space-time spectrum estimation | |
| CN112098970A (en) | Speed ambiguity resolving algorithm for traffic microwave detection and related equipment | |
| Jiang et al. | Adversarial interference mitigation for automotive radar | |
| CN112462342B (en) | Phase discretization Virgenahoff transformation time-frequency form self-reconstruction detection method for high maneuvering weak target | |
| CN114895248A (en) | Sinusoidal frequency modulation signal parameter estimation method, system and medium | |
| CN113116320A (en) | FMCW radar life signal detection method based on VMD | |
| CN111624558A (en) | De-Chirp technology-based SAR interference suppression method and device | |
| CN116699526A (en) | Vehicle millimeter wave radar interference suppression method based on sparse and low-rank model | |
| CN116794649A (en) | Clutter maneuvering target tracking method based on waveform selection | |
| CN115828552A (en) | Line spectrum purification method based on shore-based passive sonar and optimal path tracking algorithm | |
| CN115902791A (en) | S-band wave-measuring radar time Doppler spectrum-based sea wave inversion method and system | |
| CN116359851A (en) | Radar active interference detection and identification method and device based on converged network | |
| CN111983579A (en) | Method for eliminating radar speed ambiguity by using pulse repetition time of difference | |
| CN116400316B (en) | Method for processing echo signals in radar mutual interference scene |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |