CN114488105A - Radar target detection method based on motion characteristics and direction template filtering - Google Patents
Radar target detection method based on motion characteristics and direction template filtering Download PDFInfo
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- CN114488105A CN114488105A CN202210392817.2A CN202210392817A CN114488105A CN 114488105 A CN114488105 A CN 114488105A CN 202210392817 A CN202210392817 A CN 202210392817A CN 114488105 A CN114488105 A CN 114488105A
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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
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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
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
The invention relates to the technical field of radar target detection, and discloses a radar target detection method based on motion characteristics and direction template filtering, which comprises the following steps: step 1, performing target detection on a moving object through a radar to obtain a current frame frequency spectrum, and performing super-resolution on the current frame frequency spectrum to obtain a super-resolved frequency spectrum; step 2, performing motion estimation and motion compensation on the frequency spectrum obtained in the step 1 to correct the frequency spectrum to obtain a corrected frequency spectrum; step 3, filtering the frequency spectrum obtained in the step 2 by a direction template to obtain a filtered frequency spectrum so as to reduce the interference of the noise attached to the frequency spectrum; and 4, selecting a corresponding target detection algorithm according to the filtered frequency spectrum to complete target detection of the radar. The invention reduces the problem of missed detection and false detection caused by the interference of environmental clutter in the radar target detection in the prior art.
Description
Technical Field
The invention relates to the technical field of radar target detection, in particular to a radar target detection method based on motion characteristics and direction template filtering.
Background
The radar target detection technique is a technique for determining the presence or absence of a target by using a correlation method when clutter exists. The existing radar target detection technology can be roughly divided into:
(1) the parametric constant false alarm detection technology, such as a unit selection small constant false alarm technology, a unit selection large constant false alarm technology, an order statistic constant false alarm technology and the like, is suitable for the condition that a mathematical model of an interference signal is known. The constant false alarm detection technology is based on the principle that the average power level is estimated by a plurality of distance units adjacent to the left and right of a detection unit, and then the average power level is used as a basis to obtain a final detection threshold. The parametric constant false alarm detection technology is to know the statistical distribution of clutter and then design a corresponding constant false alarm detector to achieve the purpose of filtering the clutter. The parametric constant false alarm detection method has the problems that the independent peak problem cannot be solved, namely, the wrong target on the frequency spectrum can be extracted;
(2) the method is suitable for the condition that a mathematical model of an interference signal is unknown, and the detection defect of the nonparametric constant false alarm detection method is that loss is large, namely, a target is missed to be detected;
(3) the principle of the method is that a large number of radar frequency spectrograms are input into a detector through a statistical learning method, the statistical characteristics of target points are learned, and the radar target detector is trained. The problem with this approach is that there is a strong dependence on the input learning data, and if the radar is deployed in an unlearned scene, there may be situations where it is not possible to detect the target.
The radar target detection technology has the defects of target missing detection and more false detection.
Disclosure of Invention
The invention provides a radar target detection method based on motion characteristics and direction template filtering, which aims to solve the problem of missed detection and false detection caused by environmental clutter interference in radar target detection in the prior art.
The invention is realized by the following technical scheme:
a radar target detection method based on motion characteristics and direction template filtering comprises the following steps:
step 1, performing target detection on a moving object through a radar to obtain a current frame frequency spectrum, and performing super-resolution on the current frame frequency spectrum to obtain a super-resolved frequency spectrum;
step 2, performing motion estimation and motion compensation on the frequency spectrum obtained in the step 1 to correct the frequency spectrum to obtain a corrected frequency spectrum;
step 3, filtering the frequency spectrum obtained in the step 2 by a direction template to obtain a filtered frequency spectrum so as to reduce the interference of the noise attached to the frequency spectrum;
and 4, selecting a corresponding target detection algorithm according to the filtered frequency spectrum to complete target detection of the radar.
As an optimization, in step 1, the method for performing super-resolution on the current frame spectrum includes, but is not limited to, performing linear interpolation, nonlinear interpolation, and neural network-based super-resolution on the detected current frame spectrum.
As an optimization, in step 2, the specific steps of performing motion estimation and motion compensation on the frequency spectrum obtained in step 1 are as follows:
step 2.1, calculating the difference between the current frame frequency spectrum and the historical frame frequency spectrum to obtain a matching part of the current frame frequency spectrum and the historical frame frequency spectrum;
step 2.2, calculating the motion vector of each frequency spectrum point of the moving object in the current frame frequency spectrum;
step 2.3, storing the frequency spectrum of the current frame and the motion vector of the moving object corresponding to the frequency spectrum of the current frame;
and 2.4, correcting the current frame frequency spectrum by using the historical frame frequency spectrum and the historical motion vector of the moving object corresponding to the historical frame frequency spectrum through a weighting method.
As an optimization, in step 2.2, the motion vector includes the motion direction and the motion magnitude of the moving object.
As an optimization, in step 3, the specific steps of filtering the spectrum obtained in step 2 by the directional template to obtain a filtered spectrum are as follows:
step 3.1, counting clutter characteristics in the corrected current frame frequency spectrum;
3.2, selecting a corresponding filter according to the clutter characteristic;
and 3.3, outputting the current frame frequency spectrum filtered by the filter.
As an optimization, in step 3.1, the statistical method for counting the clutter characteristics in the modified current frame spectrum includes, but is not limited to, counting the clutter characteristics in the modified current frame spectrum by an autocorrelation function and a power spectral density function.
As an optimization, the clutter characteristics include, but are not limited to, gaussian clutter, clutter based on rayleigh distribution, clutter based on three-component scattering models.
As an optimization, in step 3.2, the filter includes, but is not limited to, a gaussian filter and an average filter.
As an optimization, in step 4, selecting a corresponding target detection algorithm according to the filtered spectrum specifically comprises the following steps: and selecting a corresponding target detection method for target detection according to the clutter characteristics carried by the filtered frequency spectrum.
As an optimization, the target detection method includes, but is not limited to, a constant false alarm detection method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention reduces the problem of missed detection and false detection caused by the interference of environmental clutter in the radar target detection in the prior art, not only utilizes the frequency spectrum data at the current moment, but also fully utilizes the historical frequency spectrum data, combines the motion characteristic of the moving target and the statistical characteristic of the environmental clutter, enhances the detection capability of the invention on the target, reduces the problems of false detection and missed detection, and provides a new algorithm for the radar target detection method.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
fig. 1 is a general flowchart of a radar target detection method based on motion characteristics and direction template filtering according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Examples
A radar target detection method based on motion characteristics and direction template filtering is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1, performing target detection on a moving object through a radar to obtain a current frame frequency spectrum, and performing super-resolution on the current frame frequency spectrum to obtain a super-resolved frequency spectrum. By carrying out super resolution on the frequency spectrum, the current frame frequency spectrum resolution of the detected moving object can be improved, the current frame frequency spectrum is more easily seen to be details, and a clearer basis is provided for the implementation of the subsequent steps.
In this embodiment, the method for performing super-resolution on the current frame spectrum includes, but is not limited to, performing linear interpolation, nonlinear interpolation, and neural network-based super-resolution on the detected current frame spectrum.
Taking a bicubic interpolation method of linear interpolation as an example, the formula can be expressed by formula 1:
meaning of formula: substituting different values of x to obtain W (x), which is a in formula (1)ij. X and y in formula (1) refer to x and y coordinates in the frequency spectrum respectively,
wherein, a is-0.5.
And 2, performing motion estimation and motion compensation on the frequency spectrum obtained in the step 1 to correct the frequency spectrum to obtain a corrected frequency spectrum. Motion estimation and motion compensation refer to techniques for correcting spectral data at the present time by using spectral data adjacent in the time domain. Motion estimation is to find the motion vector (i.e. the direction and speed of motion) of a moving object in a spectrum sequence transformed between the spectra of previous and next frames, and motion compensation is to apply the motion vector to the spectrum to synthesize the next spectrum. The method mainly comprises the following steps:
in the embodiment, step 2.1, calculating the difference between the current frame frequency spectrum and the historical frame frequency spectrum to obtain a matching part of the current frame frequency spectrum and the historical frame frequency spectrum; the purpose of obtaining the matching portion is to prove that the two are similar, related, or that the matching portion is the same object in different frames in the history frame and the current frame. The calculation method of the difference is the prior art, and includes, but is not limited to, calculating the mean square error, the root mean square error, the average absolute error, the standard deviation, etc. of the current frame spectrum and the historical frame spectrum.
Here, the historical frame spectrum may be a spectrum of a previous frame before a spectrum of the current frame, and the matching portion may be understood as a portion where the spectrum of the current frame coincides with the historical frame spectrum.
Step 2.2, calculating the motion vector of each frequency spectrum point of the moving object in the current frame frequency spectrum; the matching part of each spectral point in the current frame spectrum and each spectral point in the historical frame spectrum is found through the step 2.1, and the offset of the two matching parts is calculated, wherein the offset is a motion vector. The coordinates of the current frame are usually subtracted from the historical frame matching coordinates to obtain the offset in each direction.
Step 2.3, storing the frequency spectrum of the current frame and the motion vector of the moving object corresponding to the frequency spectrum of the current frame;
and 2.4, correcting the current frame frequency spectrum by using the historical frame frequency spectrum and the historical motion vector of the moving object corresponding to the historical frame frequency spectrum through a weighting method.
The correction is to perform a motion compensation, and a predicted value of the current frame frequency spectrum is obtained by using the historical frame frequency spectrum and the historical motion vector, that is, a predicted value is obtained by adding the motion vector to the historical frame frequency spectrum, that is, an adding operation is performed, then the predicted value and the current frequency spectrum value are used for weighting, and the weights can be set to be 0.5 respectively.
Because the spectrum value obtained by the sensor of the radar is not 100% correct, if historical data, namely historical frame spectrum and historical motion vectors (the history refers to previous frames or previous frames), are used, the reliability of the data obtained by integration is higher, and the false detection rate of the target is reduced.
Step 2.1 and step 2.4 call the output of step 1, and the historical frequency spectrum data that keep and historical motion vector data of the moving object are as input, step 2.1 is through calculating the difference of the frequency spectrum of the current frame and historical frame frequency spectrum, find out the matching part of the frequency spectrum of the current frame and historical frame frequency spectrum, and calculate the motion vector of the moving object in every frequency spectrum point in the frequency spectrum of the current frame through step 2.2, keep as the input of step 2.4 of the next frequency spectrum. And 2.4, correcting the spectrum data of the current frame by using the historical frame spectrum data and the motion vector data of the historical frame through a weighting method, and taking the spectrum after motion compensation as the input of the step 3.
And 3, filtering the frequency spectrum obtained in the step 2 by a direction template to obtain a filtered frequency spectrum so as to reduce the interference of the noise attached to the frequency spectrum. And 3, filtering the frequency spectrum image by a direction template, wherein the aim is to reduce clutter interference of the frequency spectrum image. The filtering of the direction template includes, but is not limited to, filtering methods such as gaussian filtering, mean filtering, and the like, and the filtering method specifically used is selected according to the calculated characteristics of the background clutter signal. Step 3 can be divided into the following steps:
step 3.1, counting clutter characteristics in the corrected current frame frequency spectrum;
3.2, selecting a corresponding filter according to the clutter characteristic;
and 3.3, outputting the current frame frequency spectrum filtered by the filter.
Wherein, the step 3.1 calls the data output in the step 2.4 as input, and the step 3.1 calculates the clutter characteristics of the spectrogram by a statistical method, including but not limited to statistics by an autocorrelation function, a power spectral density function and other methods. Taking the power spectral density as an example, the formula is as follows:
the meanings of the parameters in the formula (2) are as follows:
radar clutter signals
Is self-correlation function of
Is defined as:
power spectral density
Is defined as:
in the formula (2.2), T is the sample length,
is the delay time, f is the fluctuation frequency, and the two definitions are the basic basis for processing experimental data.
And 3.2, taking the output of the step 3.1 as input, classifying the clutter characteristics and selecting a filter corresponding to the clutter characteristics according to the clutter characteristics counted in the step 3.1, wherein the clutter characteristic classification comprises but is not limited to Gaussian clutter, clutter based on Rayleigh distribution, clutter based on a three-component scattering model and the like. The filter used in step 3.2 includes, but is not limited to, gaussian filtering, mean filtering, etc.
And 4, selecting a corresponding target detection algorithm according to the filtered frequency spectrum to complete target detection of the radar.
Specifically, a corresponding target detection method is selected for target detection according to clutter characteristics carried by the filtered frequency spectrum, step 4 takes the steps 3.3 and 3.1 as input, and step 4 selects a target detection algorithm with corresponding characteristics for target detection according to the clutter characteristics, so that the radar target detection function is completed. The target detection method includes, but is not limited to, a constant false alarm detection method which is adaptive according to environmental conditions. By combining the historical frame frequency spectrum and the historical motion vector, the conventional constant false alarm detection method can be adaptively adjusted according to the environmental condition, and the detection precision is higher.
The invention provides a series of methods, all process according to the new thought of regarding the frequency spectrum as the picture, so process to the frequency spectrum, have used like the super resolution of the picture, methods such as motion compensation that applies in the video codec, etc., meanwhile, the invention has used the information in the continuous time (namely historical frame) and adjacent space (namely adjacent point of present frequency spectrum point), to compensate the speed of the present point, the invention also chooses the more appropriate detector according to the environment situation self-adaptation, the adaptability is stronger.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A radar target detection method based on motion characteristics and direction template filtering is characterized by comprising the following steps:
step 1, performing target detection on a moving object through a radar to obtain a current frame frequency spectrum, and performing super-resolution on the current frame frequency spectrum to obtain a super-resolved frequency spectrum;
step 2, performing motion estimation and motion compensation on the frequency spectrum obtained in the step 1 to correct the frequency spectrum to obtain a corrected frequency spectrum;
step 3, filtering the frequency spectrum obtained in the step 2 by a direction template to obtain a filtered frequency spectrum so as to reduce the interference of the noise attached to the frequency spectrum;
and 4, selecting a corresponding target detection algorithm according to the filtered frequency spectrum to complete target detection of the radar.
2. The method for detecting radar target based on motion feature and direction template filtering according to claim 1, wherein in step 1, the method for performing super-resolution on the current frame frequency spectrum includes performing linear interpolation, nonlinear interpolation, and neural network based super-resolution on the detected current frame frequency spectrum.
3. The method for detecting radar target based on motion feature and direction template filtering according to claim 1, wherein in step 2, the specific steps of performing motion estimation and motion compensation on the frequency spectrum obtained in step 1 are as follows:
step 2.1, calculating the difference between the current frame frequency spectrum and the historical frame frequency spectrum to obtain a matching part of the current frame frequency spectrum and the historical frame frequency spectrum;
step 2.2, calculating the motion vector of each frequency spectrum point of the moving object in the current frame frequency spectrum;
step 2.3, storing the frequency spectrum of the current frame and the motion vector of the moving object corresponding to the frequency spectrum of the current frame;
and 2.4, correcting the current frame frequency spectrum by using the historical frame frequency spectrum and the historical motion vector of the moving object corresponding to the historical frame frequency spectrum through a weighting method.
4. The radar target detection method based on motion feature and direction template filtering of claim 1, wherein in step 2.2, the motion vector includes the motion direction and the motion magnitude of the moving object.
5. The method for detecting a radar target based on motion characteristics and direction template filtering according to claim 1, wherein in step 3, the specific step of filtering the direction template on the frequency spectrum obtained in step 2 to obtain a filtered frequency spectrum comprises:
step 3.1, counting clutter characteristics in the corrected current frame frequency spectrum;
3.2, selecting a corresponding filter according to the clutter characteristic;
and 3.3, outputting the current frame frequency spectrum filtered by the filter.
6. The method according to claim 5, wherein the statistical method for calculating the clutter characteristics in the modified current frame spectrum in step 3.1 comprises calculating the clutter characteristics in the modified current frame spectrum by an autocorrelation function and a power spectral density function.
7. The method of claim 5, wherein the clutter characteristics include Gaussian clutter, Rayleigh distribution based clutter, and three-component scattering model based clutter.
8. The method for detecting radar target based on motion feature and direction template filtering according to claim 5, wherein in step 3.2, the filter includes a Gaussian filter and a mean filter.
9. The radar target detection method based on motion feature and direction template filtering according to claim 1, wherein in step 4, selecting a corresponding target detection algorithm according to the filtered frequency spectrum specifically comprises the steps of: and selecting a corresponding target detection method for target detection according to the clutter characteristics carried by the filtered frequency spectrum.
10. The radar target detection method based on motion feature and direction template filtering according to claim 9, wherein the target detection method comprises a constant false alarm detection method.
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