CN113835085B - Radar rapid elevation measuring method based on complex terrain compensation - Google Patents
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- 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
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Abstract
The invention discloses a radar rapid elevation measuring method based on complex terrain compensation, and belongs to the technical field of radar signal processing. The method adopts the reflection terrain digital map compensation method to compensate complex terrains, reduces the influence of echo passing through different path reflection effects, improves radar height measurement accuracy, and simultaneously adopts the high convergence compressed sensing technology to improve the convergence speed of the algorithm, and enhances the robustness and instantaneity of radar height measurement. The invention has good robustness and real-time performance and high height measurement precision.
Description
Technical Field
The invention relates to a radar rapid elevation measuring method based on complex terrain compensation, and belongs to the technical field of radar signal processing.
Background
The meter wave radar has better inhibition effect (effective frequency range of the stealth material is 1-20 GHz, effective coating thickness is 1/10-1/4 wavelength) on various stealth technologies such as stealth of appearance, coating wave absorption and the like due to longer wavelength, and the meter wave is positioned between microwaves and high frequency, has a certain diffraction effect, and has smaller attenuation than microwave bands. In addition, the antenna caliber (Max: 60-70 cm) of the anti-radiation missile (ARM) is at least larger than 1 wavelength in order to search and track the target radar. Therefore, the milwave radar has strong technical advantages in the aspects of detecting high-speed, high maneuvering, stealth targets and the capability of resisting anti-radiation missiles. It is an important means for combating stealth targets and is also an important component of a reverse-conduction early warning system.
Due to the characteristics of wide beam, longer wavelength, strong ground multipath reflection and the like, particularly when a low elevation target is detected, the elevation measurement has larger error due to the influence of target echo multipath reflection, topography fluctuation and ground (sea) surface reflection strong clutter, and effective height measurement cannot be performed. Therefore, the problem of high-precision, rapid and high-stability height measurement of the meter wave radar in a low-elevation multipath reflection environment is always a technical problem which is not well solved in the radar field.
Disclosure of Invention
The invention aims to provide a radar rapid height measurement method based on complex terrain compensation to improve the height measurement precision and real-time performance of a target DOA under the conditions of low signal-to-noise ratio and low elevation angle.
The technical scheme of the invention is as follows:
in order to achieve the aim of the invention, the technical idea scheme adopted is as follows: on the basis of complex terrain compensation, the sparsity of a target in space is utilized, the signal of a radiation source is sparsified and represented through space grid division, the signal and the space position of the signal are in one-to-one correspondence under the operation, a target DOA estimation model of quick compressed sensing is constructed, a greedy quick threshold iterative sparse reconstruction algorithm is utilized to recover sparse vectors, position angle information of the sparse vectors is extracted, DOA estimation of the target is obtained, and the target height is obtained through a triangular formula.
The method comprises the following specific steps:
the invention relates to a radar rapid elevation measuring method based on complex terrain compensation, which comprises the following steps:
step 1: channel correction:
obtaining the maximum value of the self-checking signal and the phase value corresponding to the maximum value through the self-checking BIT in the radar system, calculating a channel correction coefficient, correcting the received echo signal, and ensuring the consistency among channels;
step 2: judging whether the target is low or not:
according to the echo data detection, calculating the elevation angle of the target, and if the elevation angle is smaller than 9 degrees, the radar works in a low elevation angle area;
step 3: determining an elevation region of the radar search:
step 3.1, setting an angle search range: p= [0:0.5:20 ]. Times.pi/180;
step 3.2, constructing a weight matrix: ws=exp [ j2 pi d (1:M)' sink/λ ], where d is the array element spacing, M is the number of array elements, λ is the radar signal wavelength;
step 3.3, forming a spatial spectrum: ys=ws' ×rs, where rs is the data vector after the nth pulse pressure;
step 3.4, calculating an elevation angle ss corresponding to the peak value;
step 3.5, calculating the upper and lower limits rse and rse of the target elevation angle: if the elevation angle ss is less than 9 °, rse 1=0.4 °, rse 2=9°; otherwise rse1 =ss-4 °, rse 2=min (20 °, ss+43);
step 4: compensating complex terrains by adopting a reflection terrains digital map compensation method; the method for compensating the complex terrain by adopting the reflection terrain digital map compensation method comprises the following steps of:
inverting the fluctuation height of the reflection point according to the height of the receiving array element, the target distance and the height information, correcting the complex terrain reflection model by the reflected fluctuation height of the reflection point, obtaining an array receiving signal by using the corrected complex terrain reflection model, and carrying out correlation maximum value processing on the array receiving signal and the preprocessed target echo signal;
step 5: extracting a target signal:
assume that K far-field signals are incident to the radar linear array, and the incident angle is theta k The target signal received by the array is: y=as r +n，
In the method, in the process of the invention,
A＝[α 1 (ω 0 ) α 2 (ω 0 ) … α K (ω 0 )]，
wherein A is M x K dimension array popular matrix, sr is K x 1 dimension vector signal, τ mk ＝x m sinθ k /c，x m To receive the channel position, θ k Representing the azimuth angle of the kth signal, n being M x 1-dimensional standard Gaussian white noise;
c is the speed of light; k is a target sequence number, and K is the total number of actual targets; omega 0 The carrier frequency is the target echo signal carrier frequency; alpha 1 (ω 0 ) For the steering vector of object 1, α 2 (ω 0 ) A steering vector for object 2; alpha K (ω 0 ) A steering vector for the target K;
step 6: performing space division processing on the target signal:
dividing the space-180 ° to 180 ° into θ= { θ 1 ，θ 2 ，…，θ N Building a mathematical model of the angle of arrival DOA estimate as:
y＝φs+n
wherein phi is a signal sparse M multiplied by N dimensional array manifold matrix, and s= [ s ] 1 ，s 2 ，…，s N ] T N is N multiplied by 1-dimensional sparse signals, N is Gaussian white noise, N is the number of potential targets, K is the total number of actual targets, N > K, and in s, only elements at K positions where targets actually exist are nonzero, and other N-K positions are zero; θ is the spatial azimuth angle range of the target, and T is the transposition;
step 7: roughly measuring the elevation angle of a target signal by using a digital beam forming technology DBF to obtain an initial elevation angle beta of the target, and further obtaining an elevation angle airspace omega where the target is located;
calculating 3dB beamwidth: δ=0.886λ/Md, obtaining the elevation airspace Ω obtained by the target angle:
Ω＝[β-δ，β+δ]
wherein lambda is the wavelength of radar signals, d is the interval between array elements, and M is the number of array elements;
step 8: dividing an elevation airspace omega into P parts, and obtaining an elevation airspace matrix theta by P > M:
Θ＝[β s ，β s +Δβ，…，β e ]
wherein beta is s =β - δ and β e Let p=30, and the airspace division width Δβ=δ/30;
step 9: calculating a beam transformation matrix B in an elevation space domain:
wherein,,representing an angle of incidence beta i A steering vector at the time; step 10: and pre-white speech processing is carried out on the beam transformation matrix B, and the steps are as follows:
step 10.1, according to the quadrature matrix q=b and the diagonal matrix Σ= (B) H B) -1 Obtaining a whitening transformation matrix T through pre-whitening processing:
T＝(B H B) -1/2 B H
wherein H represents a conjugation device;
step 10.2, projecting the target signal y to the beam transformation matrix T to obtain the measurement signal z:
z＝Ty＝Tφs+Tn
wherein n is Gaussian white noise, s is a space domain sparse signal, and T is a whitening transformation matrix;
step 11: compressive sampling is carried out to obtain an F multiplied by 1 dimension observation signal x:
x＝ψz＝ψTφs+φTn；
step 12: based on the observed signal x and the whitened transformation matrix T, a Greedy fast threshold iterative algorithm Greedy FITS is utilized to passIterating to obtain an estimated value of the spatial domain sparse signal
Wherein I 1 Representing vector 1 norm, s.t as constraint 2 Representing a vector 2 norm, wherein sigma is a noise standard deviation;
step 13: defining a target angle range θ= [ θ ] 1 ，θ 2 ，…，θ N ]，According to the estimated value +.>The elements of the range of the target angle theta are in one-to-one correspondence with the elements of the range of the target angle theta to obtain a target elevation angle measurement result theta d1 ：
θ d1 ＝(d 1 -1)×180°/(N-1)
Wherein d 1 Representing an estimateSubscripts of elements other than 0;
step 14: from elevation angle measurement result theta d1 And calculating the target height by a triangular formula:
H＝Rsin(θ d1 )。
further, in step 12, the Greedy rapid threshold iterative algorithm Greedy FITSA specifically includes the following steps:
step 12.1, initializing, setting simulation parameters gamma 0 ＝1,ν＞1,ξ＜1；
Step 12.2, iterative computation of x k+1 ；
Wherein lambda is the wavelength of the radar signal,F(r k )＝||r k || 2 ，/>representation pair F (r) k ) The differentiation is performed so that, I 2 Representing a vector 2 norm;
step 12.3, restarting, if (r k -x k+1 ) T (x k+1 -x k ) Not less than 0, r k ＝x k ；
Step 12.4, ensure convergence, if it meets ||x k+1 -x k ||≥ν||x 1 -x 0 | then γ=max (ζγ, 1);
in step 12.5 of the process, if | I x k+1 -x k If < epsilon, stop x k+1 And outputting the output, otherwise, repeating the step 12.2, the step 12.3 and the step 12.4 in sequence.
Advantageous effects
(1) The invention adopts the reflection terrain digital map compensation method to compensate complex terrain, reduces the influence of echo passing through different path reflection effects, and improves the height measurement precision;
(2) According to the invention, the measurement signal is compressed by using the observation signal, so that the operation amount is reduced, and the radar height measurement performance is improved;
(3) The invention adopts a high-convergence compressed sensing technology, shortens the restarting interval and the oscillation period of the algorithm by a greedy rapid convergence threshold iterative algorithm, improves the convergence speed of the algorithm, enhances the robustness and the instantaneity of radar height measurement, is beneficial to engineering realization, and has the advantages of good robustness and instantaneity and high height measurement precision.
Drawings
FIG. 1 is a flow chart of an implementation of a radar fast elevation measurement method based on complex terrain compensation according to the present invention;
FIG. 2 is a diagram of a complex terrain reflection model;
FIG. 3 is a flow chart of a reflective terrain digital map compensation method;
FIG. 4 is a comparison of the convergence speed of a Greedy fast threshold iterative algorithm (Greedy Fista);
FIG. 5 is a complex terrain compensation result;
FIG. 6 is a graph showing the height measurement result of the method according to the present invention;
FIG. 7 is a high accuracy result of the present invention.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
Referring to the processing flow-out figure 1, the steps of a specific implementation scheme of the radar rapid elevation measurement method based on the complex terrain compensation are as follows:
step 1: channel correction
And acquiring the maximum value of BIT signals and a phase value corresponding to the maximum value through self-checking BIT in the radar system, calculating a channel correction coefficient, correcting received echo signals, and ensuring consistency among channels.
Step 2: judging whether the target is low-altitude
And detecting and calculating the elevation angle of the target according to the echo data, if the elevation angle is smaller than 9 degrees, the radar works in a low elevation angle area, otherwise, the conventional correlation method is used for measuring the height.
Step 3: radar search elevation region determination
1) Setting an angle search range: p= [0:0.5:20] ×pi/180;
2) Constructing a weight matrix: ws=exp [ j2 pi d (1:M)' sink/λ ], where d is the array element spacing and M is the number of array elements;
3) Forming a spatial spectrum: ys=ws' ×rs, where rs is the data vector after the nth pulse pressure;
4) Calculating an elevation angle ss corresponding to the peak value;
5) An upper limit rse and a lower limit rse2 of the target elevation angle are calculated: if ss < 9 °, rse 1=0.4 °, rse 2=9°; otherwise rse1 =ss-4 °, rse2 =min (20 °, ss+4°).
Step 4: compensation for complex terrain
Inverting the fluctuation height h of the reflection point according to the information such as the height of the receiving array element, the target distance, the height and the like by utilizing certain echo data d Reflection point relief height h to be inverted d And correcting the complex terrain reflection model, obtaining an array receiving signal by using the corrected complex terrain reflection model, and carrying out correlation maximum value taking processing on the array receiving signal and the preprocessed target echo signal so as to obtain an estimated real target height, thereby reducing the influence of echo passing through different path reflection effects and improving the height measurement precision. The complex terrain reflection model is shown in fig. 2; the flow chart of the reflection topography digital map compensation method is shown as o in figure 3
Step 5: target signal extraction
Assume that K far-field signals are incident to the radar linear array, and the incident angle is theta k The target signal received by the array is:
y＝As r +n
in the method, in the process of the invention,
A＝[α 1 (ω 0 ) α 2 (ω 0 ) … α K (ω 0 )]，
wherein A is M x K dimension array popular matrix, s r For a vector signal of dimension Kx1, τ mk ＝x m sinθ k /c，x m To receive the channel position, θ k Representing the azimuth angle of the kth signal, n being M x 1-dimensional standard Gaussian white noise;
c is the speed of light; k is a target sequence number, and K is the total number of actual targets; omega 0 The carrier frequency is the target echo signal carrier frequency; alpha 1 (ω 0 ) For the steering vector of object 1, α 2 (ω 0 ) A steering vector for object 2; alpha K (ω 0 ) Is the steering vector of the target K.
Step 6: spatial division of a target signal
The space (-180 ° to 180 °) to be considered is divided into θ= { θ 1 ，θ 2 ，…，θ N In order to embody the sparsity of the target signals, the potential target number is much larger than the actual target number, namely N > K, thus constructing a signal sparse M multiplied by N-dimensional array manifold matrix phi and N multiplied by 1-dimensional sparse signals s= [ s ] 1 ，s 2 ，…，s N ] T Only the elements at the K positions in s where the target actually exists are non-zero, while the other N-K positions are all zero. Wherein θ is the spatial azimuth angle range where the target is located, and T is the transpose.
The mathematical model of the angle of arrival DOA estimate is:
y＝φs+n
wherein phi is a signal sparse M multiplied by N dimensional array manifold matrix, and s= [ s ] 1 ，s 2 ，…，s N ] T N is the number of potential targets, K is the total number of actual targets, N > K, and in s, only the elements at K positions where targets actually exist are nonzero, and the other N-K positions are zero; θ is the spatial azimuth angle range where the target is located, and T is the transpose.
Step 7: roughly measuring the elevation angle of the target signal by using the method in the step 3 to obtain the initial elevation angle beta of the target, and further obtaining the elevation angle airspace omega of the target;
calculating 3dB beamwidth: δ=0.886λ/Md, further the airspace Ω obtained for the target angle:
Ω＝[β-δ，β+δ]
wherein lambda is the radar signal wavelength, d is the array element spacing, and M is the array element number.
Step 8: dividing the elevation space domain into P parts, P > M, obtaining a space matrix Θ
Θ＝[β s ，β s +Δβ，…，β e ]
Wherein beta is s =β - δ and β e =β+δ represents nullThe left and right boundaries of the domain, δ is 3dB beamwidth, and p=30, and the spatial division width Δβ=δ/30.
Step 9: calculating a beam transformation matrix B in a space domain
Wherein,,representing an angle of incidence beta i A steering vector at that time.
Step 10: pre-white speech processing of beam-switching matrix B
(1) According to the orthogonal matrix q=b and the diagonal matrix Σ= (B H B) -1 Obtaining a whitening transformation matrix T through pre-whitening processing:
T＝(B H B) -1/2 B H
wherein H represents a conjugation means.
(2) Projecting the target signal y to the beam transformation matrix T to obtain the measurement signal z:
z＝Ty＝Tφs+Tn
where n is white gaussian noise and s is a spatial domain sparse signal. The measurement signal z is a signal obtained by compression sampling the observation signal, and includes a noise compression signal and a target compression signal.
Step 11: compressive sampling is carried out to obtain an F multiplied by 1 dimension observation signal x:
x＝ψz＝ψTφs+φTn
the observation signal x is a signal after the target signal is subjected to white pre-processing, and includes a noise signal and a target sparse signal.
Step 12: based on the observation signal x and the whitened transformation matrix T, a Greedy fast threshold iterative algorithm Greedy FISTA is utilized to passIterating to obtain an estimated value of the spatial domain sparse signal
Wherein I 1 Representing vector 1 norm, s.t as constraint 2 The vector 2 norm is represented, and σ is the noise standard deviation. The greedy fast threshold iterative algorithm specifically comprises the following steps:
step 12.1, initializing, setting simulation parameters gamma 0 ＝1,ν＞1,ξ＜1；
Step 12.2, iterative computation of x k+1 ；
Wherein lambda is the wavelength of the radar signal,F(r k )＝||r k || 2 ，/>representation pair F (r) k ) The differentiation is performed so that, I 2 Representing a vector 2 norm;
step 12.3, restarting, if (r k -x k+1 ) T (x k+1 -x k ) Not less than 0, r k ＝x k ；
Step 12.4, ensure convergence, if it meets ||x k+1 -x k ||≥ν||x 1 -x 0 | then γ=max (ζγ, 1);
in step 12.5 of the process, if | I x k+1 -x k If < epsilon, stop x k+1 And outputting the result that otherwise the steps 12.2 and 12 are repeatedAnd step 3, step 12.4.
Step 13: defining a target angle range θ= [ θ ] 1 ，θ 2 ，…，θ N ]，According to the estimated value +.>The elements of the range of the target angle theta are in one-to-one correspondence with the elements of the range of the target angle theta to obtain a target elevation angle measurement result theta d1 ：
θ d1 ＝(d 1 -1)×180°/(N-1)
Wherein d 1 Representing an estimateSubscripts of elements other than 0.
Step 14: based on the angle measurement result theta d1 And calculating the target height by a triangular formula:
H＝Rsin(θ d1 )。
the calculation simulation analysis result of the invention is as follows:
1) Greedy fast threshold iterative algorithm Greedy FISTA convergence speed simulation
Simulation parameters: gamma ray 0 =1, ν > 1, ζ < 1, recovery error ε=10 -14 ；
Simulation results: compared with other quick threshold iterative algorithms, the greedy quick convergence threshold iterative algorithm has the advantages of quick convergence speed, small oscillation period, sparse recovery precision which can be achieved by only 100 times of iteration times, and the simulation result of fig. 4 shows that the method provided by the invention has small operand and strong instantaneity.
2) Complex terrain compensation simulation
Simulation parameters: carrier frequency: 145MHz; number of antenna array elements: 16, the array element distance d is 1m, the antenna frame is 4.73m, the amplitude fluctuation between channels is 0.5dB, the phase fluctuation is 10 degrees, the array inclination angle error is 0.1 degree, the topography fluctuation standard deviation is 2m, the target height is 12000m, and the acting distance is 90km to 210km. The detection signal to noise ratio is 15dB.
Simulation results: the height measurement error before compensation is 523m, and the height measurement error after compensation is 311m, and as can be known from the simulation result of fig. 5, the reflection topography digital map compensation method provided by the invention can effectively compensate the influence of the undulating topography on the height measurement precision, and the height measurement precision is greatly improved.
3) Simulation of height measurement stability and precision
Simulation parameters: carrier frequency: 145MHz; number of antenna array elements: 16, array element spacing is 1m, antenna frame height is 4.73m, amplitude fluctuation between channels is 0.5dB, phase fluctuation is 10 degrees, array inclination angle error is 0.1 degree, topography fluctuation standard deviation is 2m, target height is 8000m, and acting distance is 10km to 200km. The elevation angle ss is smaller than 5 degrees, and the signal-to-noise ratio of the signal is-5 dB to 15dB.
Simulation results: in the 200km range, the height measurement mean value difference is smaller than 0.05 degrees, the standard deviation is smaller than 0.13 degrees, and the simulation results of fig. 6 and 7 show that the method provided by the invention has the characteristics of good robustness, high precision, small operand and the like compared with other methods.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present invention disclosed in the embodiments of the present invention should be covered by the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (2)
1. A radar rapid elevation measuring method based on complex terrain compensation is characterized by comprising the following steps:
step 1: channel correction:
obtaining the maximum value of the self-checking signal and the phase value corresponding to the maximum value through the self-checking BIT in the radar system, calculating a channel correction coefficient, correcting the received echo signal, and ensuring the consistency among channels;
step 2: judging whether the target is low or not:
according to the echo data detection, calculating the elevation angle of the target, and if the elevation angle is smaller than 9 degrees, the radar works in a low elevation angle area;
step 3: determining an elevation region of the radar search:
step 3.1, setting an angle search range: p= [0:0.5:20] ×pi/180;
step 3.2, constructing a weight matrix: ws=exp [ j2 pi d (1:M)' sink/λ ], where d is the array element spacing, M is the number of array elements, λ is the radar signal wavelength;
step 3.3, forming a spatial spectrum: ys=ws' ×rs, where rs is the data vector after the nth pulse pressure;
step 3.4, calculating an elevation angle ss corresponding to the peak value;
step 3.5, calculating the upper and lower limits rse and rse of the target elevation angle: if the elevation angle ss is less than 9 °, rse 1=0.4 °, rse 2=9°; otherwise rse1 =ss-4 °, rse 2=min (20 °, ss+4 °);
step 4: compensating complex terrains by adopting a reflection terrains digital map compensation method; the method for compensating the complex terrain by adopting the reflection terrain digital map compensation method comprises the following steps of:
inverting the fluctuation height of the reflection point according to the height of the receiving array element, the target distance and the height information, correcting the complex terrain reflection model by the reflected fluctuation height of the reflection point, obtaining an array receiving signal by using the corrected complex terrain reflection model, and carrying out correlation maximum value processing on the array receiving signal and the preprocessed target echo signal;
step 5: extracting a target signal:
assume that K far-field signals are incident to the radar linear array, and the incident angle is theta k The target signal received by the array is y=as r +n，
In the method, in the process of the invention,
A＝[α 1 (ω 0 ) α 2 (ω 0 ) … α K (ω 0 )]，
wherein A is M x K dimension array popular matrix, s r For a vector signal of dimension Kx1, τ mk ＝x m sinθ k /c，x m To receive the channel position, θ k Representing the azimuth angle of the kth signal, n being M x 1-dimensional standard Gaussian white noise; c is the speed of light; k is a target sequence number, and K is the total number of actual targets; omega 0 The carrier frequency is the target echo signal carrier frequency; alpha 1 (ω 0 ) For the steering vector of object 1, α 2 (ω 0 ) A steering vector for object 2; alpha K (ω 0 ) A steering vector for the target K;
step 6: performing space division processing on the target signal:
dividing the space-180 ° to 180 ° into θ= { θ 1 ，θ 2 ，…，θ N Building a mathematical model of the angle of arrival DOA estimate as:
y＝φs+n
wherein phi is a signal sparse M multiplied by N dimensional array manifold matrix, and s= [ s ] 1 ，s 2 ，…，s N ] T N is N multiplied by 1-dimensional sparse signals, N is Gaussian white noise, N is the number of potential targets, K is the total number of actual targets, N > K, and in s, only elements at K positions where targets actually exist are nonzero, and other N-K positions are zero; θ is the spatial azimuth angle range of the target, and T is the transposition;
step 7: roughly measuring the elevation angle of a target signal by using a digital beam forming technology DBF to obtain an initial elevation angle beta of the target, and further obtaining an elevation angle airspace omega where the target is located;
calculating 3dB beamwidth: δ=0.886λ/Md, obtaining the elevation airspace Ω obtained by the target angle:
Ω＝[β-δ,β+δ]
wherein lambda is the wavelength of radar signals, d is the interval between array elements, and M is the number of array elements;
step 8: dividing an elevation airspace omega into P parts, and obtaining an elevation airspace matrix theta by P > M:
Θ＝[β s ，β s +Δβ，…，β e ]
wherein beta is s =β - δ and β e Let p=30, and the airspace division width Δβ=δ/30;
step 9: calculating a beam transformation matrix B in an elevation space domain:
wherein,,representing an angle of incidence beta i A steering vector at the time;
step 10: and pre-white speech processing is carried out on the beam transformation matrix B, and the steps are as follows:
step 10.1, according to the quadrature matrix q=b and the diagonal matrix Σ= (B) H B) -1 Obtaining a whitening transformation matrix T through pre-whitening processing:
T＝(B H B) -1/2 B H
wherein H represents a conjugation device;
step 10.2, projecting the target signal y to the beam transformation matrix T to obtain the measurement signal z:
z＝Ty＝Tφs+Tn
wherein n is Gaussian white noise, s is a space domain sparse signal, and T is a whitening transformation matrix;
step 11: compressive sampling is carried out to obtain an F multiplied by 1 dimension observation signal x:
x＝ψz＝ψTφs+φTn；
step 12: based on the observed signal x and the whitened transformation matrix T, a Greedy fast threshold iterative algorithm Greedy FITS is utilized to passIterating to obtain an estimated value of the spatial domain sparse signal +.>
Wherein I 1 Representing vector 1 norm, s.t as constraint 2 Representing a vector 2 norm, wherein sigma is a noise standard deviation;
step 13: defining a target angle range θ= [ θ ] 1 ，θ 2 ，…，θ N ]，According to the estimated value +.>The elements of the range of the target angle theta are in one-to-one correspondence with the elements of the range of the target angle theta to obtain a target elevation angle measurement result theta d1 ：
θ d1 ＝(d 1 -1)×180°/(N-1)
Wherein d 1 Representing an estimateSubscripts of elements other than 0;
step 14: from elevation angle measurement result theta d1 And calculating the target height by a triangular formula:
H＝Rsin(θ d1 )。
2. the method for rapid elevation measurement of radar of claim 1, wherein in step 12, the Greedy rapid threshold iterative algorithm Greedy FITSA comprises the following specific steps:
step 12.1, initializing, setting simulation parameters gamma 0 ＝1,ν＞1,ξ＜1；
Step 12.2, iterative computation of x k+1 ；
Wherein lambda is the wavelength of the radar signal,F(r k )＝||r k || 2 ，/>representation pair F (r) k ) The differentiation is performed so that, I 2 Representing a vector 2 norm;
step 12.3, restarting, if (r k -x k+1 ) T (x k+1 -x k ) Not less than 0, r k ＝x k ；
Step 12.4, ensure convergence, if it meets ||x k+1 -x k ||≥ν||x 1 -x 0 | then γ=max (ζγ, 1);
in step 12.5 of the process, if | I x k+1 -x k If < epsilon, stop x k+1 And outputting the output, otherwise, repeating the step 12.2, the step 12.3 and the step 12.4 in sequence.
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