CN112904289B - Airborne non-forward side looking array radar steady clutter suppression method based on diagonal loading - Google Patents
Airborne non-forward side looking array radar steady clutter suppression method based on diagonal loading Download PDFInfo
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Abstract
The invention discloses a method for suppressing steady clutter of an airborne non-forward looking array radar based on diagonal loading, which can be used for the airborne non-forward looking array radar to perform the steady clutter suppression. The invention has the following implementation steps: obtaining clutter data; calculating a sampling covariance matrix of airborne non-forward looking array radar clutter by using a maximum likelihood estimation formula; calculating a clutter degree of freedom estimation value; generating a diagonal loading factor; optimizing a sampling covariance matrix of airborne non-front side view array radar clutter through diagonal loading to obtain a covariance matrix after diagonal loading; and (4) clutter suppression. According to the method, the diagonal loading factor of the clutter covariance matrix of the airborne non-side looking array radar is calculated according to the characteristic spectrum distribution characteristic of the covariance matrix, and the robustness of clutter suppression can be improved through diagonal loading.
Description
Technical Field
The invention belongs to the technical field of communication, and further relates to an airborne non-forward looking array radar steady clutter suppression method based on diagonal loading in the technical field of radar signal processing. The invention can be used for the airborne non-front side view array radar to perform stable clutter suppression.
Background
The airborne radar is one of the most important sensors in modern battlefields, has a long visual distance due to the fact that an airplane is used as a platform, can be flexibly and quickly deployed in a required place to play important tasks of warning, commanding and the like, and is widely valued. However, when the airborne radar works in a downward looking mode, due to the movement of the carrier, the speeds of the ground clutter in different directions relative to the carrier are different, so that the clutter spectrum is expanded seriously, the clutter presents a strong space-time coupling characteristic, and the clutter suppression performance is influenced seriously. For the airborne non-forward looking array radar, the clutter space-time coupling characteristic is non-linear, and the difficulty of clutter suppression is higher, so how to effectively suppress the clutter is a key technical problem faced by the airborne non-forward looking array radar.
The university of electronic technology provides a clutter suppression method in a patent technology 'airborne radar clutter suppression method based on emission space-time weight optimization and KA-STAP' (application number: 201710498403.7, and authorization number: CN 107255797B). According to the method, on the basis of the traditional KA-STAP, the available degree of freedom of a radar transmitting end is fully utilized, the space-time weight vector is designed to be transmitted to suppress the main lobe and the side lobe clutter according to an array structure and clutter prior information, the relatively complete clutter information estimation prior covariance matrix is reserved while the peak energy of the received clutter is reduced, and finally the estimated covariance matrix is loaded by using the constructed prior covariance matrix, and the space-time optimal weight vector of the receiving STAP filter is obtained through calculation. The method can better improve the clutter suppression performance. However, the method has the disadvantage that the clutter suppression performance is unstable due to the fact that the diagonal loading factor is obtained through an empirical value in the process of constructing the prior covariance matrix.
The patent technology of the university of west ampere electronic technology "an airborne radar clutter suppression method based on covariance matrix estimation" (application number: 201610256596.0, application publication number: CN105929371 a) provides a clutter suppression method for an airborne non-orthophoria radar. The method comprises the steps of firstly calculating a covariance matrix corresponding to each range gate-Doppler channel clutter data block, then normalizing covariance matrix elements to obtain a weighting coefficient, further obtaining a weighted covariance matrix, and finally calculating an optimal covariance matrix through iteration. The method can better inhibit the clutter by fully utilizing the clutter data. However, the method has the defects that the iterative optimization covariance matrix is utilized, the calculated amount is large, the operation complexity is high, and the real-time clutter suppression is difficult to perform.
Disclosure of Invention
The invention aims to provide a method for suppressing the steady clutter of an airborne non-front side looking array radar based on diagonal loading aiming at the defects of the prior art, which is used for solving the problem that the diagonal loading factor of the airborne non-front side looking array radar is difficult to determine.
The technical scheme for realizing the purpose of the invention is as follows: the large eigenvalue and the small eigenvalue of the clutter covariance matrix are distinguished through clutter freedom degrees, the diagonal loading factor is determined by using the small eigenvalue, and the robust clutter suppression can be realized through the covariance matrix after diagonal loading.
The specific steps for realizing the purpose of the invention comprise the following steps:
(1) Obtaining clutter data:
representing clutter of the airborne non-front side view array radar to be suppressed as a two-dimensional matrix of NxK and L, wherein N represents the number of array elements of a receiving array of the airborne non-front side view array radar, K represents the number of coherent pulses of each pulse repetition period, and L represents the number of range gate samples of sampling clutter data;
(2) Calculating a sampling covariance matrix of airborne non-forward looking array radar clutter by using a maximum likelihood estimation formula;
(3) Calculating clutter freedom degree estimation value:
calculating a clutter degree of freedom estimation value by using the configuration parameters of the airborne non-forward looking array radar receiving array according to the following formula:
wherein gamma represents clutter freedom degree estimated value, lambda represents working wavelength of airborne non-side looking array radar, and L a The aperture length of the airborne non-forward side-looking array radar receiving array is represented, V represents the movement speed of the airborne, and T represents w Denotes the coherent processing time, T w ＝(K-1)T r ，T r Representing the repetition period of the receiving pulse of the airborne non-positive side view array radar, and alpha representing the space-time coupling characteristic of the airborne non-positive side view array radar;
(4) Generating a diagonal loading factor:
(4a) Carrying out characteristic decomposition on a sampling covariance matrix of airborne non-forward looking array radar clutter to obtain N multiplied by K characteristic values, and carrying out descending order arrangement on all the characteristic values;
(4b) Forming a large characteristic value set by all characteristic values with the serial numbers less than or equal to gamma in the characteristic value sequence after descending order, forming a small characteristic value set by all characteristic values with the serial numbers greater than gamma, and taking the average value of all characteristic values of the small characteristic value set as a diagonal loading factor;
(5) Optimizing a sampling covariance matrix of airborne non-front side view array radar clutter through diagonal loading to obtain a covariance matrix after diagonal loading;
(6) The clutter signals for each range gate are suppressed according to the following equation:
wherein, y l Representing the value of the ith range gate in the sampling clutter data block after clutter suppression, L representing the serial number of the range gate, L =1,2, …, L, the superscript-1 representing inversion operation, s representing the space-time guide vector pointed by the main lobe of the airborne non-orthophoria array radar receiving array,
s s representing the space domain guide vector pointed by the main lobe of the airborne non-forward side-looking array radar receiving array,
denotes the kronecker operation, s t Representing the time domain guide vector pointed by the main lobe of the airborne non-forward side-looking array radar receiving array,
representing the covariance matrix after diagonal loading.
Compared with the prior art, the invention has the following advantages:
firstly, the clutter suppression method and the clutter suppression device generate the diagonal loading factor by utilizing the clutter freedom degree estimation value, and solve the problem that the clutter suppression performance is unstable due to the fact that the diagonal loading factor is obtained through an empirical value in the prior art, so that the clutter suppression robustness can be improved.
Secondly, the sampling covariance matrix of the airborne non-front side view array radar clutter is optimized through diagonal loading, and the problems that the iterative optimization covariance matrix is utilized, the calculated amount is large, the operation complexity is high, and real-time clutter suppression is not easy to perform in the prior art are solved, so that real-time clutter suppression can be performed through changing diagonal loading factors in real time.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the effect of yaw angle on the distribution of eigenvalues of a covariance matrix of airborne radar in accordance with the present invention;
FIG. 3 is a diagram illustrating the influence of the array elements of the present invention on the distribution of eigenvalues of the covariance matrix of airborne radar clutter;
FIG. 4 is a graph illustrating the effect of pulse count on the distribution of eigenvalues of the airborne radar clutter covariance matrix according to the present invention;
FIG. 5 is a graph of the effect of the noise ratio of the present invention on the eigenvalue distribution of the airborne radar clutter covariance matrix;
FIG. 6 is a graph of the output result of the improvement factor of the invention with Doppler frequency change after simulated experiment clutter suppression.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The specific steps of the present invention will be further described with reference to the accompanying figure 1:
and step 1, obtaining clutter data.
The clutter of the airborne non-forward looking array radar to be suppressed is represented as a two-dimensional matrix of NxK and L, wherein N represents the array element number of the airborne non-forward looking array radar receiving array, K represents the coherent pulse number of each pulse repetition period, and L represents the sample number of a range gate of sampling clutter data.
Step 2, utilizing a maximum likelihood estimation method to calculate a sampling covariance matrix of airborne non-side looking array radar clutter:
wherein,
a sampling covariance matrix representing airborne non-positive side view array radar clutter, sigma representing a summation operation, x l And the clutter vector corresponding to the ith distance gate in the sampled clutter data block is represented, and the superscript H represents the conjugate transposition operation. In theory, if the covariance matrix is accurately known, a good clutter suppression effect can be obtained, but actually, the characteristics of the clutter are unknown, the covariance matrix can only be estimated from the range gate reference sample data, and the estimation value of the covariance matrix is used for replacing the true covariance matrix, so that the clutter suppression performance is reduced.
And 3, calculating a clutter freedom degree estimation value.
For an airborne non-forward looking array radar system, the clutter freedom degree not only depends on the space domain aperture bandwidth product and the time domain time-width bandwidth product, but also is related to the space-time coupling characteristic. The clutter freedom estimate may be calculated according to the following equation:
wherein gamma represents clutter freedom degree estimated value, lambda represents working wavelength of airborne non-side looking array radar, and L a The aperture length of the airborne non-forward side-looking array radar receiving array is represented, V represents the movement speed of the airborne, and T represents w Denotes the coherent processing time, T w ＝(K-1)T r ，T r The repetition period of the receiving pulse of the airborne non-orthographic side-looking array radar is represented, and alpha represents the space-time coupling characteristic of the airborne non-orthographic side-looking array radar.
And 4, generating a diagonal loading factor.
And (3) performing characteristic decomposition on the sampling covariance matrix of airborne non-side looking array radar clutter to obtain N multiplied by K characteristic values, and performing descending order arrangement on all the characteristic values.
And forming a large characteristic value set by all characteristic values with the serial numbers less than or equal to gamma in the characteristic value sequence after descending order, forming a small characteristic value set by all characteristic values with the serial numbers greater than gamma, and taking the average value of all characteristic values of the small characteristic value set as a diagonal loading factor.
The values of the space-time coupling characteristic α of the airborne non-positive side-view array radar in step 3 are further described with reference to fig. 2, 3, 4, and 5.
According to configuration parameters of the airborne non-positive side view array radar system, values of the first two items in the clutter freedom degree gamma expression can be obtained, but the value of the space-time coupling characteristic alpha is unknown, so that the value of the space-time coupling characteristic alpha of the airborne non-positive side view array radar in the step 3 is further described with reference to the attached drawings. Different characteristic value distribution diagrams shown in fig. 2 to 5 are obtained by setting different yaw angles, array element numbers, pulse numbers and noise-to-noise ratios. The included angle between the moving direction of the airborne machine and the receiving array of the airborne radar is called a yaw angle.
FIG. 2 is a distribution diagram of clutter covariance matrix eigenvalues for different yaw angles. The abscissa in fig. 2 represents the serial number of the characteristic value, and the ordinate represents the magnitude of the characteristic value in decibels. The curve marked with a pentagonal symbol in fig. 2 represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °, and the eigenvalue distribution curve is obtained by connecting the eigenvalues sorted in descending order in step 4. The curve marked with a circle in fig. 2 represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 15 °. The curve marked with an asterisk in fig. 2 represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 30 °. The curves marked by the following triangular symbols in fig. 2 represent clutter covariance matrix eigenvalue distribution curves corresponding to a yaw angle of 45 °. The curves marked with cross symbols in fig. 2 represent clutter covariance matrix eigenvalue distribution curves corresponding to a yaw angle of 60 °. The curves marked with square symbols in fig. 2 represent the clutter covariance matrix eigenvalue distribution curves corresponding to a yaw angle of 75 °. The curve marked by a diamond symbol in fig. 2 represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 2 indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the value is 39. The curve marked by the second vertical line in fig. 2 indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", and the value is 50.
The number of eigenvalues between the "first inflection point" and the "second inflection point" in fig. 2 is the value of α, and it can be seen that the number of eigenvalues between the "first inflection point" and the "second inflection point" of the curves corresponding to different yaw angles is approximately 11. It is shown that the yaw angle has no influence on the value of alpha.
Fig. 3 is a clutter covariance matrix eigenvalue distribution diagram corresponding to different array element numbers of an airborne radar receiving array. Fig. 3 (a) is a clutter covariance matrix eigenvalue distribution diagram corresponding to the time when the number N =8 of array elements of the airborne radar receiving array is 0 °,45 ° and 90 °, wherein the abscissa in fig. 3 (a) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibel. The curve indicated by a pentagonal symbol in fig. 3 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the following triangular symbol in fig. 3 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 3 (a) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 3 (a) indicates the sequence number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", which is 39. The curve marked by the second vertical line in fig. 3 (a) indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", which is 50.
Fig. 3 (b) is a clutter covariance matrix eigenvalue distribution diagram corresponding to the time when the number of array elements N =16 of the airborne radar receiving array is 0 °,45 ° and 90 ° respectively, the abscissa in fig. 3 (b) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibel. The curve indicated by a pentagonal symbol in fig. 3 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the following triangular symbol in fig. 3 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 3 (b) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 3 (b) indicates the sequence number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the value is 47. The curve marked by the second vertical line in FIG. 3 (b) indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", which is 58.
In fig. 3 (a), when the number N =8 of the array elements of the airborne radar receiving array, the number of the eigenvalues between the "first inflection point" and the "second inflection point" is the value of α, and it can be seen that the number of the eigenvalues between the "first inflection point" and the "second inflection point" of the curves corresponding to different yaw angles is approximately 11. In fig. 3 (b), when the number N =16 of the array elements of the airborne radar receiving array, the number of the eigenvalues between the "first inflection point" and the "second inflection point" is the value of α, and it can be seen that the number of the eigenvalues between the "first inflection point" and the "second inflection point" of the curves corresponding to different yaw angles is approximately 11. The number of the array elements of the airborne radar receiving array has no influence on the value of alpha.
FIG. 4 is a distribution diagram of clutter covariance matrix eigenvalues corresponding to different numbers of coherent pulses. Fig. 4 (a) is a clutter covariance matrix eigenvalue distribution diagram corresponding to the yaw angles of 0 °,45 °, and 90 ° when the number of coherent pulses K =32, and the abscissa in fig. 4 (a) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibel. The curve indicated by a pentagonal symbol in fig. 4 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the lower triangular symbol in fig. 4 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 4 (a) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve indicated by the first vertical line in fig. 4 (a) indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the numerical value is 39. The curve marked by the second vertical line in fig. 4 (a) indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", which is 50.
Fig. 4 (b) is a clutter covariance matrix eigenvalue distribution diagram corresponding to the case where the yaw angle is 0 °,45 °, and 90 ° respectively when the number of coherent pulses K =64, and the abscissa in fig. 4 (b) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibels. The curve indicated by a pentagonal symbol in fig. 4 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the lower triangular symbol in fig. 4 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 4 (b) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 4 (b) indicates the serial number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the value is 71. The curve marked by the second vertical line in fig. 4 (b) represents the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", and the numerical value is 82.
When the number of coherent pulses K =32 in fig. 4 (a), the number of eigenvalues between the "first inflection point" and the "second inflection point" is α, and it can be seen that the number of eigenvalues between the "first inflection point" and the "second inflection point" of the curves corresponding to different yaw angles is approximately 11. In fig. 4 (b), when the number of coherent pulses K =64, the number of eigenvalues between the "first inflection point" and the "second inflection point" is a value α, and it can be seen that the number of eigenvalues between the "first inflection point" and the "second inflection point" of the curve corresponding to different yaw angles is approximately 11. It is shown that the number of coherent pulses has no influence on the value of alpha.
FIG. 5 is a distribution diagram of eigenvalues of the clutter covariance matrix corresponding to different clutter ratios. Fig. 5 (a) is a clutter covariance matrix eigenvalue distribution diagram corresponding to yaw angles of 0 °,45 °, and 90 ° when the noise-to-noise ratio CNR =45dB, and the abscissa in fig. 5 (a) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibel. The curve indicated by a pentagonal symbol in fig. 5 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the lower triangular symbol in fig. 5 (a) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 5 (a) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 5 (a) indicates the sequence number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the value is 39. The curve marked by the second vertical line in fig. 5 (a) indicates the index of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", which is 50.
Fig. 5 (b) is a clutter covariance matrix eigenvalue distribution diagram corresponding to yaw angles of 0 °,45 °, and 90 ° when the noise-to-noise ratio CNR =55dB, and the abscissa in fig. 5 (b) represents the serial number of the eigenvalue, and the ordinate represents the magnitude of the eigenvalue, and the unit is decibel. The curve indicated by a pentagonal symbol in fig. 5 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 0 °. The curve indicated by the following triangular symbol in fig. 5 (b) represents a clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 45 °. The curve marked with a diamond symbol in fig. 5 (b) represents the clutter covariance matrix eigenvalue distribution curve corresponding to a yaw angle of 90 °. The curve marked by the first vertical line in fig. 5 (b) indicates the sequence number of the clutter covariance matrix eigenvalue corresponding to the "first inflection point", and the value is 39. The curve marked by the second vertical line in fig. 5 (b) represents the serial number of the clutter covariance matrix eigenvalue corresponding to the "second inflection point", and the value is 50.
When the noise-to-noise ratio CNR =45dB in fig. 5 (a), the number of eigenvalues between the "first inflection point" and the "second inflection point" is α, and it can be seen that the number of eigenvalues between the "first inflection point" and the "second inflection point" of the curve corresponding to different yaw angles is approximately 11. In fig. 5 (b), when the noise-to-noise ratio CNR =55dB, the number of eigenvalues between the "first inflection point" and the "second inflection point" is α, and it can be seen that the number of eigenvalues between the "first inflection point" and the "second inflection point" of the curve corresponding to different yaw angles is approximately 11. Indicating that the noise ratio has no effect on the value of alpha.
From the above analysis, it can be concluded that: the values of the yaw angle, the array element number, the pulse number and the noise-to-noise ratio have no influence on the value of alpha, so that the value of alpha is 11 for the airborne non-normal view finding array radar.
And 5, optimizing a sampling covariance matrix of airborne non-front side view array radar clutter by diagonal loading, and obtaining the covariance matrix after diagonal loading by using the following formula:
where σ denotes the diagonal loading factor and I denotes the identity matrix.
Step 6, according to the following formula, suppressing clutter signals of each range gate:
wherein, y l Representing the value of the ith range gate in the sampling clutter data block after clutter suppression, L representing the serial number of the range gate, L =1,2, …, L, the superscript-1 representing inversion operation, s representing the space-time guide vector pointed by the main lobe of the airborne non-orthophoria array radar receiving array,
s s representing the space domain guide vector pointed by the main lobe of the airborne non-forward side-looking array radar receiving array,
denotes the kronecker operation, s t Representing the time domain guide vector pointed by the main lobe of the airborne non-forward side-looking array radar receiving array,
representing the covariance matrix, s, after diagonal loading s And s t The specific expression of (a) is as follows:
wherein j represents an imaginary unit symbol, e represents exponential operation with a natural constant as a base, pi represents a circumferential rate, d represents an array element interval of an airborne non-frontal side view array radar receiving antenna, cos represents cosine operation, theta represents an azimuth angle pointed by a main lobe of the airborne non-frontal side view array radar receiving array,
representing the directional pitch angle of the main lobe of the airborne non-normal side-looking array radar receiving array, the superscript T representing the transposition operation, theta p Indicating yaw angle, f prf Representing the repetition frequency of the received pulse of the airborne non-frontal side view array radar.
The effect of the present invention is further explained by combining the simulation experiment as follows:
1. simulation experiment conditions are as follows:
the hardware platform of the simulation experiment of the invention is as follows: the processor is an Intel (R) Core (TM) i7-9700 CPU, the main frequency is 3.00GHz, and the memory is 16GB.
The software platform of the simulation experiment of the invention is as follows: windows 10 operating system and MATLAB R2018a.
The parameters of the simulation experiment of the invention are as follows: the transmitting end and the receiving end of the airborne non-front side view array radar both adopt 8 multiplied by 8 half-wavelength equidistant area arrays, when the receiving end receives data, a sub-array is synthesized by a plane array antenna in a pitching direction, and the whole plane array antenna is equivalent to an 8-element equidistant linear array, so the number of array elements of the airborne non-front side view array radar receiving array is N =8, the working wavelength of the radar is lambda =0.32m, the array element interval d =0.16m, the number of coherent pulses of each pulse repetition period K =32, the flying height of the carrier is H =6Km, the moving speed of the carrier is V =120m/s, and the pulse repetition frequency f is f prf =1500Hz, sampling rate f s =4MHz, signal-to-noise ratio SNR =0dB, noise-to-noise ratio CNR =45dB, yaw angle θ p ＝45°。
2. Simulation content and result analysis thereof:
the simulation experiment of the invention is to adopt the invention to simulate the improvement factor, the improvement factor is defined as the ratio of the output signal noise-plus-noise ratio and the input signal noise-plus-noise ratio, and is used for measuring the clutter suppression capability of the clutter suppression algorithm. After clutter suppression using the present invention
A plurality of frequency points are selected within the range, and the values of the improvement factors corresponding to each frequency point are connected to obtain an improvement factor curve, and the result is shown in fig. 6. The effect of the present invention will be further described with reference to the simulation diagram of fig. 6.
Figure 6 shows a graph of the improvement factor obtained with the present invention as a function of the normalized doppler frequency. The horizontal axis in fig. 6 represents the normalized doppler frequency, and the vertical axis in fig. 6 represents the improvement factor in decibels.
As can be seen from the simulation results of FIG. 6, the improvement factor of the present invention is about 68dB, which is expressed by the formula CNR +10 × log 10 The optimal value of the improvement factor under the ideal condition obtained by (N multiplied by K) calculation is about 69dB, so that the method can be used for the airborne non-forward side looking array radar to carry out robust clutter suppression.
The simulation result verifies the correctness, the effectiveness and the reliability of the invention.
Claims (5)
1. A method for suppressing steady clutter of an airborne non-front side looking array radar based on diagonal loading is characterized in that a diagonal loading factor is generated by using a clutter freedom degree estimated value, and a sampling covariance matrix of the airborne non-front side looking array radar clutter is optimized through the diagonal loading, and the method comprises the following steps:
(1) Obtaining clutter data:
representing clutter of the airborne non-front side view array radar to be suppressed as a two-dimensional matrix of NxK and L, wherein N represents the number of array elements of a receiving array of the airborne non-front side view array radar, K represents the number of coherent pulses of each pulse repetition period, and L represents the number of range gate samples of sampling clutter data;
(2) Calculating a sampling covariance matrix of airborne non-forward looking array radar clutter by using a maximum likelihood estimation formula;
(3) Calculating clutter degree of freedom estimation values:
calculating a clutter degree of freedom estimation value by using the configuration parameters of the airborne non-forward looking array radar receiving array according to the following formula:
wherein gamma represents clutter freedom degree estimated value, lambda represents working wavelength of airborne non-side looking array radar, and L a The aperture length of the airborne non-forward side-looking array radar receiving array is represented, V represents the movement speed of the airborne, and T represents w Denotes the coherent processing time, T w ＝(K-1)T r ，T r Representing the repetition period of the receiving pulse of the airborne non-positive side view array radar, and alpha representing the space-time coupling characteristic of the airborne non-positive side view array radar;
(4) Generating a diagonal loading factor:
(4a) Performing characteristic decomposition on a sampling covariance matrix of airborne non-side looking array radar clutter to obtain N multiplied by K characteristic values, and performing descending order arrangement on all the characteristic values;
(4b) Forming a large characteristic value set by all characteristic values with the serial numbers less than or equal to gamma in the characteristic value sequence after descending order, forming a small characteristic value set by all characteristic values with the serial numbers greater than gamma, and taking the average value of all characteristic values of the small characteristic value set as a diagonal loading factor;
(5) Optimizing a sampling covariance matrix of airborne non-side looking array radar clutter through diagonal loading to obtain a covariance matrix after diagonal loading;
(6) The clutter signals for each range gate are suppressed according to the following equation:
wherein, y l Representing the ith range gate in a block of sampled clutter dataThe value after clutter making, L represents the serial number of a range gate, L =1,2, …, L, the superscript-1 represents the inversion operation, s represents the space-time guide vector pointed by the main lobe of the airborne non-orthographic view array radar receiving array,
s s representing the space domain guide vector pointed by the main lobe of the airborne non-obverse side-looking array radar receiving array,
denotes the kronecker operation, s t Representing the time domain guide vector pointed by the main lobe of the airborne non-obverse side-looking array radar receiving array,
represents the covariance matrix after diagonal loading, x l And representing a clutter vector corresponding to the ith distance gate in the sampled clutter data block.
2. The method for suppressing robust clutter according to claim 1, wherein the maximum likelihood estimation formula in step (2) is as follows:
wherein,
a sampling covariance matrix representing airborne non-positive side view array radar clutter, sigma representing a summation operation, x l And representing a clutter vector corresponding to the ith distance gate in the sampling clutter data block, and the superscript H represents conjugate transposition operation.
3. The method for suppressing robust clutter according to claim 2, wherein the covariance matrix after diagonal loading in step (5) is obtained by the following formula:
where σ denotes the diagonal loading factor and I denotes the identity matrix.
4. The method for suppressing the robust clutter according to the claim 1, wherein the spatial steering vector pointed by the main lobe of the receiving array of the airborne non-forward looking array radar in the step (6) is as follows:
wherein s is s Representing the airspace guide vector pointed by the main lobe of the airborne non-obverse side view array radar receiving array, e representing the exponential operation with a natural constant e as the base, j representing an imaginary number unit symbol, pi representing a circumferential rate, d representing the array element spacing of the airborne non-obverse side view array radar receiving antenna, cos representing the cosine operation, theta representing the azimuth angle pointed by the main lobe of the airborne non-obverse side view array radar receiving array,
and the pitch angle indicating the main lobe direction of the airborne non-forward side-looking array radar receiving array is represented, and the superscript T represents transposition operation.
5. The method for suppressing robust clutter according to claim 4, wherein the time-domain steering vector pointed by the mainlobe of the airborne non-forward looking array radar receiving array in step (6) is as follows:
wherein s is t Time domain guide vector theta representing main lobe direction of airborne non-normal side-looking array radar receiving array p Representing the included angle between the moving direction of the carrier and the receiving array of the non-positive side-looking array radar of the carrier, f prf Representing the repetition frequency of the received pulse of the airborne non-frontal side view array radar.
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