CN113567946A - Real target and virtual image detection method for millimeter wave radar - Google Patents

Real target and virtual image detection method for millimeter wave radar Download PDF

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CN113567946A
CN113567946A CN202110817262.7A CN202110817262A CN113567946A CN 113567946 A CN113567946 A CN 113567946A CN 202110817262 A CN202110817262 A CN 202110817262A CN 113567946 A CN113567946 A CN 113567946A
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multipath
track
distance
millimeter wave
wave radar
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CN113567946B (en
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朱芃琦
尹学锋
陈卓钰
王萍
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Tongji University
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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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention relates to a method for detecting a real target and a virtual image of a millimeter wave radar, which is characterized by comprising the following steps: 1) acquiring instantaneous multipath component parameters of a millimeter wave radar channel; 2) using a multipath component aggregation algorithm to aggregate multipath component parameters into continuous distance domain tracks; 3) performing hyperbolic fitting on the distance domain trajectory to determine an optimal deterministic component; 4) and judging whether the optimal deterministic component is smaller than a set threshold value, if so, judging that the distance domain track is a single scattering track, and a real target object exists on the detection distance corresponding to each moment of the distance domain track, otherwise, judging that the distance domain track is a multiple scattering track, and a false image exists on the detection distance corresponding to each moment of the distance domain track. Compared with the prior art, the method has the advantages of high accuracy, high reliability and the like.

Description

Real target and virtual image detection method for millimeter wave radar
Technical Field
The invention relates to the field of wireless communication and environment perception based on a vehicle-mounted millimeter wave radar, in particular to a method for detecting a real target and a virtual image of a millimeter wave radar.
Background
With the rapid development of the fields of automatic driving and vehicle networking, the vehicle-mounted millimeter wave radar plays an important role in the perception of the vehicle to the environment. Compared with a vehicle-mounted sensor system based on a camera and a laser radar, the millimeter wave radar has the advantages of better weather influence resistance, capability of tracking the speed and distance of an object and low price. However, millimeter-wave radars also have some disadvantages in that the detected "target" includes an actually existing object and a virtual image generated by a multipath phenomenon of radio wave propagation. The problem is that the millimeter wave radar industry is concerned widely at present, and unnecessary trouble and danger can be caused in the driving process of the intelligent automobile. Multipath phenomenon causes self-interference of radar systems, and to solve this problem, it is necessary to have a clearer knowledge of the radar propagation channel.
In the field of radar, research on radar signal propagation channels is rare, more ideas for solving the virtual image problem are focused on physical modes such as filtering, the modes are limited in accuracy and relatively low in reliability, and accurate virtual image removal effect evaluation is difficult to carry out.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a real target and virtual image detection method of a millimeter wave radar, which has high accuracy and strong reliability.
The purpose of the invention can be realized by the following technical scheme:
a real target and virtual image detection method for a millimeter wave radar comprises the following steps:
1) acquiring instantaneous multipath component parameters of a millimeter wave radar channel;
2) using a multipath component aggregation algorithm to aggregate multipath component parameters into continuous distance domain tracks;
3) performing hyperbolic fitting on the distance domain trajectory to determine an optimal deterministic component;
4) and judging whether the optimal deterministic component is smaller than a set threshold value, if so, judging that the distance domain track is a single scattering track, and a real target object exists on the detection distance corresponding to each moment of the distance domain track, otherwise, judging that the distance domain track is a multiple scattering track, and a false image exists on the detection distance corresponding to each moment of the distance domain track.
Further, the step 1) comprises:
and carrying out frequency mixing and low-pass filtering operations on the millimeter wave radar signal to obtain a beat frequency signal, carrying out Fourier transform on the beat frequency signal to obtain channel impulse response, and obtaining the component parameters of channel instantaneous multipath according to the channel impulse response.
Further, the component parameters include time delay, doppler frequency and power.
Further, the expression of the beat signal is:
Figure BDA0003170621160000021
wherein A ismThe mth multipath corresponds to the complex amplitude of the beat signal, the m subscript parameters all correspond to the parameter of the mth multipath, v1Is the Doppler frequency, fRProportional to the propagation distance, t, for the frequency change caused by the propagation delaysAnd tfSlow and fast times, respectively.
Further, the expression of the channel impulse response is:
Figure BDA0003170621160000022
wherein, al,m、vl,mAnd τl,mThe amplitude, Doppler frequency and time delay of the mth path are respectively, delta (·) represents a Dirac function, and subscript l represents the ith chirp, and based on high-precision parameter estimation algorithms such as a space iteration generalized expectation-maximization algorithm, the time delay, Doppler frequency and power of each propagation multipath can be obtained and used as input quantities of a multipath track aggregation algorithm.
And further, acquiring the component parameters of the channel instantaneous multipath by a high-precision parameter estimation algorithm according to the channel impulse response.
Further, the high-precision parameter estimation algorithm is a space iteration-based generalized expectation-maximization algorithm.
Further, the step 2) comprises:
and (3) aggregating the multipath components with close comprehensive distances into a continuous multipath delay trajectory by using a multipath component aggregation algorithm, and carrying out scale transformation on the multipath delay trajectory to obtain a distance domain trajectory.
Further, the step 3) comprises:
obtaining a parameter sequence (d) to be estimatede,1,de,2,...de,Q)，deThe propagation distance from a first scattering point to a last scattering point on the millimeter wave radar channel propagation path is represented by Q, which is d to be estimatedeThe number of the particles;
note the book
Figure BDA0003170621160000023
RdAs a distance domain track, pair
Figure BDA0003170621160000024
Performing quadratic polynomial least square fitting to define a fitted sequence distance value Rf＝(rf,1,rf,2,...,rf,s) Distance value R of original sequencef＝(rf,1,rf,2,...,rf,s) S is the total fast beat number experienced by the track, and a fitting sequence R is calculatedfAnd the prosequence RdCoefficient of projection ρ (R) betweenf,Rd) The calculation formula is as follows:
Figure BDA0003170621160000031
calculate each deCorresponding projection coefficient ρ (R)f,Rd) Taking the maximum projection coefficient rho (R)f,Rd) Corresponding deAs the best deterministic component
Figure BDA0003170621160000032
Namely, it is
Figure BDA0003170621160000033
Further, whether the projection coefficient is larger than 0.95 is judged, if yes, the projection coefficient is kept, and if not, the projection coefficient is abandoned.
Compared with the prior art, the invention has the following beneficial effects:
the method extracts the channel instantaneous multipath component parameters from the beat frequency signal based on the prior information of geometric hypothesis, uses a multipath component aggregation algorithm to aggregate the multipath component parameters into continuous distance domain tracks, carries out hyperbolic fitting on the distance domain tracks, determines the optimal certainty component, namely the key parameter of the asymptote intersection point coordinate of the optimal fitting curve, effectively realizes the discrimination of real targets and false images by comparing the optimal certainty component with the real target or false images on the detection distance corresponding to the distance domain tracks with the set threshold, carries out more precise virtual image discrimination based on a channel multipath propagation mechanism, and has high accuracy and strong reliability.
Drawings
FIG. 1 is a schematic view of a radio wave propagation scenario of a vehicle-mounted radar;
FIG. 2 is a propagation map simulated string-level power distance spectrum;
FIG. 3 is a schematic diagram of a multipath component aggregation distance domain trajectory;
FIG. 4 is a graph comparing the optimal hyperbolic fitting result with the original trajectory;
FIG. 5 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in FIG. 1, the vehicle travels at a constant speed v along the x-axis with the vehicle car coordinate (x)0,y0) Two fixed scattering points S exist in space1(x1,y1) And S2(x2,y2) The propagation path of the radar channel is shown by an arrow, and is shown by Car-S1-S2Path is an example, d1Representing the first scattering point S experienced from the emitting end1(x1,y1) Distance of dnRepresents the last scattering point S2(x2,y2) Distance to the receiving end, dmThe representation shown represents the propagation distance from the first scattering point to the last scattering point, which in more complex cases may involve multiple scattering propagation of the wave in space, due to S1And S2Fixed in position, so that for a particular path, d moves as the vehicle continues to movemIs a constant value of d1And dnCan be expressed as:
Figure BDA0003170621160000041
Figure BDA0003170621160000042
according to Car-S1-S2The detection distance R obtained by the path0Comprises the following steps:
Figure BDA0003170621160000043
when considering a three-dimensional scene, only (y) is needed1-y0)2And (y)2-y0)2Instead of being (y)1-y0)2+(z1-z0)2And (y)2-y0)2+(z2-z0)2I.e., let x be discussed for convenience0、y0And z0All are 0, and can be discussed in detail in three cases:
(I) Single Scattering case
In the case of single scatter, the first and last scatter in the path occur at the same scatter point, x1＝x2，y1＝y2，d1＝dn，dm＝d0The detection distance R obtained at this time0Corresponding to the real scatterer, the calculation formula is:
Figure BDA0003170621160000044
in the R-t plane, the formula (4) can be expressed in the form of a hyperbola (single branch), specifically:
Figure BDA0003170621160000045
wherein the time t is used as an independent variable, and the distance R is detected0Is a dependent variable.
(ii) multiple scattering event and experiences the same first scattering point and last scattering point
At this time dmNot equal to 0, equation (3) can be expressed as:
Figure BDA0003170621160000046
Figure BDA0003170621160000047
Figure BDA0003170621160000048
this means that the detection range locus is located at an asymptote intersection
Figure BDA0003170621160000049
Hyperbola (single branch).
(iii) multiple scattering situation and the first scattering point and the last scattering point experienced are different
When the first and last scatter occur at different scatter points, x1≠x2，y1≠y2，dmNot equal to 0, at which time the distance R is detected0The calculation formula is as follows:
Figure BDA0003170621160000051
R1for detecting the distance R0The calculation formula is as follows:
Figure BDA0003170621160000052
in this case, the distance R is detected0The time-varying locus does not have a regular geometric shape and cannot be simplified into a hyperbolic form, but a lower bound R is obtained by a geometric feature analysis structure1，R1The time-varying locus being an asymptote intersection point
Figure BDA0003170621160000053
And for any time, R1Is constantly less than R0The procedure was demonstrated as follows:
according to the Cauchy inequality:
Figure BDA0003170621160000054
the demonstration process is as follows:
the key equation (11), namely certificate:
Figure BDA0003170621160000055
let a be y1，b＝y2，c＝x1-vt，d＝x2-vt, then the formula:
Figure BDA0003170621160000056
only need to prove:
Figure BDA0003170621160000057
only need to prove:
Figure BDA0003170621160000058
when ab + cd is equal to or greater than 0, equation (14) is equivalent to:
a2b2+c2d2+2abcd≤a2b2+c2d2+a2d2+b2c2 (16)
2abcd≤a2d2+b2c2 (17)
from the cauchy inequality, equation (17) is clearly true, with an equal sign if and only if ad is bc;
when ab + cd < 0, the formula (17) is apparently true, and the equal sign is not true.
In addition, R at time t → ∞ R0And has R1The same asymptotes are drawn, which means that if hyperbolic pairs are used for the trajectory R0Performing least square fitting to obtain the R coordinate of the asymptote intersection point of the fitting hyperbola R' which is not less than R1Of asymptote intersection R coordinates, i.e.
Figure BDA0003170621160000061
This inference can be verified by using a back-proof method, when an R is present0Is smaller than the R coordinate of the asymptote intersection point of the fitting hyperbola
Figure BDA0003170621160000062
Its residual function must not be optimal under the least squares criterion.
In summary, if a hyperbolic least square fitting is performed on a trajectory of the multipath detection distance changing with time, the R coordinate theory of the asymptote intersection point of the fitting trajectory is 0 for the multipath under the condition of single scattering; for the multipath under the condition of multiple scattering, the R coordinate of the asymptote intersection point of the fitting track is not less than that of the multipath under the condition of multiple scattering theoretically
Figure BDA0003170621160000063
And must be greater than 0. This conclusion can be used to discriminate between single-scatter and multiple-scatter multipath. The single scattering multipath corresponds to an actual target object in space, while the multiple scattering multipath generates a virtual image in the direction of the last scattering point relative to the receiving end, and the detection distance values of the virtual image do not correspond to real objects, so that the discrimination of the single scattering and the multiple scattering is equivalent to the discrimination of a real target and the virtual image.
A method for detecting a real target and a virtual image of a millimeter wave radar, as shown in fig. 5, includes:
1) acquiring instantaneous multipath component parameters of a millimeter wave radar channel;
2) using a multipath component aggregation algorithm to aggregate multipath component parameters into continuous distance domain tracks;
3) performing hyperbolic fitting on the distance domain trajectory to determine an optimal deterministic component;
4) and judging whether the optimal deterministic component is smaller than a set threshold value, if so, judging that the distance domain track is a single scattering track, and the distance domain track corresponds to a detection distance and has a real target object, otherwise, judging that the distance domain track is a multiple scattering track, and the distance domain track corresponds to a detection distance and has a false image.
The millimeter wave radar channel has the characteristic that a transmitting end and a receiving end are collocated, a multipath time delay track can be modeled as superposition of two (single branch) hyperbolas and a deterministic component, the two hyperbolas respectively correspond to a first hop and a last hop in the radio wave propagation process, and the deterministic component corresponds to the process between the two hyperbolas;
when the propagation multipath belongs to single scattering, the two hyperbolas are the same, and fitting can be performed by using the hyperbolas of which the intersection points of the asymptotes are positioned on a time axis, and the deterministic component is 0;
when the propagation multipath belongs to multiple scattering but the first scattering point and the last scattering point are the same, the two hyperbolas are the same, fitting can be performed by using the hyperbolas of which the intersection points of the asymptotes are positioned above the time axis, and the deterministic component is not 0;
when the propagation multipath belongs to multiple scattering and the first scattering point and the last scattering point are different, the two hyperbolas are different, the deterministic component is not 0, a hyperbola which is constantly smaller than the multipath distance domain track and the asymptote of which is the same as the distance domain track can be constructed, and the intersection point of the asymptote is positioned above the time axis, and the expression is used for fitting to construct a hyperbola expression, namely, the expression (10).
The step 1) comprises the following steps:
performing frequency mixing and low-pass filtering operations on the millimeter wave radar signal to obtain a beat frequency signal, and performing Fourier transform on the beat frequency signal in a fast time domain to obtain instantaneous multipath component parameters of a channel, including time delay, Doppler frequency and power;
in order to estimate the channel parameters at a specific time, a series of signal sampling and processing needs to be performed at this time, the series of channel sampling is called "snapshot" or "snapshot", for millimeter wave radar signals, signals sent by a radar are received by a receiving antenna after passing through a channel, and beat frequency signals can be obtained after frequency mixing and low-pass filtering operations, only the part in an anti-aliasing filtering pass band is considered, and the expression is:
Figure BDA0003170621160000071
wherein A ismThe mth multipath corresponds to the complex amplitude of the beat signal, the m subscript parameters all correspond to the parameter of the mth multipath, v1Is the Doppler frequency, fRProportional to the propagation distance, t, for the frequency change caused by the propagation delaysAnd tfSlow and fast times, respectively.
Performing Fourier transform on the beat frequency signal in a fast time domain to obtain a channel impulse response:
Figure BDA0003170621160000072
wherein, al,m、vl,mAnd τl,mAmplitude, Doppler frequency andand time delay, delta (·) represents a dirac function, subscript l represents the ith chirp, and the time delay, the Doppler frequency and the power of each propagation multipath can be obtained based on high-precision parameter estimation algorithms such as a space iteration generalized expectation-maximization algorithm and the like and serve as input quantities of a multipath track aggregation algorithm.
Detecting distance R0Proportional to the time delay of multipath, multiplying the time delay of multipath by the speed of light and dividing by two to obtain the detection distance R0
And 2) utilizing the spatial consistency of the multipath components belonging to the same multipath in three parameter domains of time delay, Doppler frequency and power, using a multipath component aggregation algorithm to aggregate the multipath components with close comprehensive distances into a continuous multipath time delay track, and carrying out scale transformation on the aggregated multipath time delay track to obtain a distance domain track R which is used as the input of a three-step fitting track judgment method.
Since the trajectories of the multipaths in the range domain can be approximately fitted in the form of hyperbolas, performing a least squares fitting directly on the hyperbolas would bring about a relatively high computational complexity, but it is necessary to ensure a sufficiently high accuracy so that the fitting results can be used for subsequent judgment. In practice, the amount of the liquid to be used,
Figure BDA0003170621160000081
can be expressed as the form of quadratic polynomial, to the distance track of radar channel when the vehicle straight line is gone, can use quadratic polynomial least square fit after carrying out the square to the distance track, carry out the square again, can reduce the computational complexity, consequently carry out quadratic polynomial least square fit to the square of track, can obtain final fitting curve to fitting quadratic polynomial evolution again. At the same time, dmIs unknown and requires a one-dimensional search.
The step 3) comprises the following steps:
31) let the sequence of parameters to be estimated be (d)e,1,de,2,...de,Q)，deIs d to be estimatedmQ is d to be estimatedeQuantity and record
Figure BDA0003170621160000082
RdA sequence of distance domain traces obtained for a certain aggregation, pair
Figure BDA0003170621160000088
Performing quadratic polynomial least square fitting to define a fitted sequence distance value Rf＝(rf,1,rf,2,...,rf,s) Distance value R of original sequencef＝(rf,1,rf,2,...,rf,s) S is the total fast beat number experienced by the track, and a fitting sequence R is calculatedfAnd the prosequence RdCoefficient of projection ρ (R) betweenf,Rd) The calculation formula is as follows:
Figure BDA0003170621160000083
32) circularly performing the step 31), and dePerforming one-dimensional search, and taking deEstimated value
Figure BDA0003170621160000084
As the best deterministic component.
Use of
Figure BDA0003170621160000085
Substitution of deAnd step 31) and step 32) are repeated to obtain a final fitting sequence of the distance domain track R
Figure BDA0003170621160000086
And step 3) is circulated, each distance domain track is fitted, and the distance domain track corresponding to each distance domain track is obtained
Figure BDA0003170621160000087
T is 1,2,. T, which is the number of trusted distance domain trajectories;
in step 3), the projection coefficient rho (R) is judgedfAnd R) whether the distance track is larger than 0.95, if so, the distance track is proved to be well fitted, the corresponding distance track is judged to be credible and is reserved, otherwise, the corresponding distance track is judged to be unreliable and is rejectedAnd (5) abandoning.
In the step 3), the survival time of each distance domain track is longer than 50 snapshots, and the track with the survival time shorter than 50 snapshots is considered to be unreliable.
In engineering practice, due to errors in links such as MPC estimation and aggregation, the set threshold in step 4) can be set according to actual conditions.
Under the condition of prior knowledge of the environment, the detection method provided by the embodiment is verified through propagation diagram simulation, and the propagation diagram simulation is a simulation mode capable of well restoring the radio wave propagation process.
The three-dimensional case is similar to the two-dimensional case, so for simplicity, the two scatter point coordinates are set in the two-dimensional plane only as (4,5), and (5,8), in m, and the vehicle is traveling along the y-axis at a constant speed of 5m/s, taking into account the time range of 0-3 s. A beat signal constructed through a propagation diagram is subjected to data processing to obtain a cascade time delay distance spectrum as shown in fig. 2, paths corresponding to single scattering and multiple scattering are marked in fig. 2, two tracks of the single scattering respectively reach the minimum distance and the maximum power at 1s and 1.6s, and tracks of the multiple scattering can be regarded as two tracks of the single scattering, and the two tracks of the single scattering are averaged in a distance dimension or translated along the distance dimension on the basis of the single scattering. Performing space iteration generalized expectation-maximization algorithm estimation on the simulation signal and aggregating the obtained multipath components to obtain corresponding distance-time trajectories as shown in fig. 3, it can be observed that, in the process of space iteration generalized expectation-maximization algorithm estimation and multipath component aggregation, two single scattering paths are aggregated into four trajectories, because the two paths are very close to each other in a time delay domain and a distance domain before and after the two paths intersect, estimation errors are caused, and the continuity of the aggregated trajectories is affected. This does not affect the performance of the subsequent steps.
The set threshold in step 4) is set to 0.5, i.e., the error generated in step 1) and step 2) is taken into consideration
Figure BDA0003170621160000091
Distance domain trajectories less than 0.5 are determined as single scatter trajectories, while trajectories greater than 0.5 are determined as single scatter trajectoriesConsidered as multiple scattering traces. Using step 3) to perform hyperbolic fitting on the trajectories in fig. 3 to obtain the optimal fitting effect of each curve as shown in fig. 4, wherein "o" represents a multipath component, the projection coefficient of each trajectory fitting is above 0.95, and t in fig. 41～t4Corresponding to
Figure BDA0003170621160000092
0, -0.3, 0.1, 0.2, respectively, close to 0, less than the threshold, and the remaining traces correspond to
Figure BDA0003170621160000093
All the values are greater than 3 and much greater than the threshold value, which proves the effectiveness of the detection method provided by the embodiment, and it is worth noting that the threshold value set in the step 4) is selected only for a specific situation, and in other situations, the threshold value needs to be reasonably adjusted according to the accuracy degree of the step 1) and the step 2).
The embodiment provides a method for detecting a real target and a virtual image of a millimeter wave radar, which is used for performing geometric assumption and modeling on time evolution of radar signal multipath components in different propagation modes based on a propagation mechanism and distinguishing by using different propagation mode trajectory geometric parameters. The method takes each multipath track as algorithm input, uses hyperbolic curve of undetermined coefficient to carry out least square fitting on the track, seeks the optimal undetermined coefficient, and effectively realizes the discrimination of real target and false image according to the optimal undetermined coefficient.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A method for detecting a real target and a virtual image of a millimeter wave radar is characterized by comprising the following steps:
1) acquiring instantaneous multipath component parameters of a millimeter wave radar channel;
2) using a multipath component aggregation algorithm to aggregate multipath component parameters into continuous distance domain tracks;
3) performing hyperbolic fitting on the distance domain trajectory to determine an optimal deterministic component;
4) and judging whether the optimal deterministic component is smaller than a set threshold value, if so, judging that the distance domain track is a single scattering track, and a real target object exists on the detection distance corresponding to each moment of the distance domain track, otherwise, judging that the distance domain track is a multiple scattering track, and a false image exists on the detection distance corresponding to each moment of the distance domain track.
2. The method for detecting the real target and the virtual image of the millimeter wave radar according to claim 1, wherein the step 1) comprises the following steps:
and carrying out frequency mixing and low-pass filtering operations on the millimeter wave radar signal to obtain a beat frequency signal, carrying out Fourier transform on the beat frequency signal to obtain channel impulse response, and obtaining the component parameters of channel instantaneous multipath according to the channel impulse response.
3. The method as claimed in claim 2, wherein the parameters of the components include time delay, Doppler frequency and power.
4. The method of claim 2, wherein the expression of the beat signal is as follows:
Figure FDA0003170621150000011
wherein A ismThe mth multipath corresponds to the complex amplitude of the beat signal, the m subscript parameters all correspond to the parameter of the mth multipath, v1Is the Doppler frequency, fRFrequency variation due to propagation delay, and propagation distanceProportional ratio, tsAnd tfSlow and fast times, respectively.
5. The method for detecting the real target and the virtual image of the millimeter wave radar as claimed in claim 2, wherein the expression of the channel impulse response is as follows:
Figure FDA0003170621150000012
wherein, al,m、vl,mAnd τl,mThe amplitude, Doppler frequency and time delay of the mth path are respectively, delta (·) represents a Dirac function, and subscript l represents the ith chirp, and based on high-precision parameter estimation algorithms such as a space iteration generalized expectation-maximization algorithm, the time delay, Doppler frequency and power of each propagation multipath can be obtained and used as input quantities of a multipath track aggregation algorithm.
6. The method for detecting the real target and the virtual image of the millimeter wave radar as claimed in claim 2, wherein the component parameters of the instantaneous multipath of the channel are obtained by a high-precision parameter estimation algorithm according to the impulse response of the channel.
7. The method for detecting the real target and the virtual image of the millimeter wave radar according to claim 6, wherein the high-precision parameter estimation algorithm is a space iteration-based generalized expectation-maximization algorithm.
8. The method for detecting the real target and the virtual image of the millimeter wave radar according to claim 1, wherein the step 2) comprises the following steps:
and (3) aggregating the multipath components with close comprehensive distances into a continuous multipath delay trajectory by using a multipath component aggregation algorithm, and carrying out scale transformation on the multipath delay trajectory to obtain a distance domain trajectory.
9. The method for detecting the real target and the virtual image of the millimeter wave radar according to claim 8, wherein the step 3) comprises the following steps:
obtaining a parameter sequence (d) to be estimatede,1,de,2,...de,Q)，deThe propagation distance from a first scattering point to a last scattering point on the millimeter wave radar channel propagation path is represented by Q, which is d to be estimatedeThe number of the particles;
note the book
Figure FDA0003170621150000021
RdAs a distance domain track, pair
Figure FDA0003170621150000022
Performing quadratic polynomial least square fitting to define a fitted sequence distance value Rf＝(rf,1,rf,2,...,rf,s) Distance value R of original sequencef＝(rf,1,rf,2,...,rf,s) S is the total fast beat number experienced by the track, and a fitting sequence R is calculatedfAnd the prosequence RdCoefficient of projection ρ (R) betweenf,Rd) The calculation formula is as follows:
Figure FDA0003170621150000023
calculate each deCorresponding projection coefficient ρ (R)f,Rd) Taking the maximum projection coefficient rho (R)f,Rd) Corresponding deAs the best deterministic component
Figure FDA0003170621150000024
Namely, it is
Figure FDA0003170621150000025
10. The method of claim 9, wherein the method determines whether the projection coefficient is greater than 0.95, if so, retains the deterministic component corresponding to the projection coefficient, otherwise, discards the deterministic component corresponding to the projection coefficient.
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