CN112904297B - Method for forming and estimating angle of split-dimension self-adaptive monopulse beam - Google Patents
Method for forming and estimating angle of split-dimension self-adaptive monopulse beam Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The invention discloses a method for forming a split-dimensional self-adaptive monopulse beam and estimating an angle. The method utilizes the idea of split-dimension single pulse beam forming to decompose a two-dimensional matrix area array into a one-dimensional equivalent linear array with two dimensions of rows and columns. And judging whether the interference is in the main lobe or not by a method of calculating a guide vector correlation coefficient corresponding to the interference subspace and the beam direction. And then, the minimum distortion and the maximum measurable angle area of the main lobe are used as targets, the main lobe interference and the side lobe interference are suppressed in a proper dimension, and meanwhile, the linearity of a monopulse ratio curve is ensured by using a constraint self-adaptive monopulse method. And finally, respectively calculating the Cronecker products of the two dimension weights to obtain two-dimensional sum and difference beam weights and corresponding directional patterns. Simulation verification results show that the method has higher angle measurement precision.
Description
Technical Field
The invention belongs to the field of digital beam forming, and particularly relates to a method for forming a split-dimensional self-adaptive monopulse beam and estimating an angle.
Background
Modern radars face complex and various interferences in electronic warfare, different interferences can enter from side lobes or main lobes of beams, even appear in the main lobes and the side lobes at the same time, and if the interferences cannot be effectively inhibited, the tracking effect of a target can be greatly affected. In order to ensure the effect of target angle estimation and tracking of the radar, the method has important significance for developing a target angle estimation method under the condition of main lobe and side lobe interference.
The angle estimation of the target usually adopts a monopulse angle measurement method, and conventional adaptive beam forming can generate null at the interference angle when main lobe interference exists, but the distortion of the main lobe is inevitably caused, and further the distortion of a monopulse curve is caused. Zhou Bilei, li Rongfeng et al propose a subarray level sum-difference and auxiliary beam joint adaptive monopulse algorithm. The method is based on a subarray dimension reduction method, and utilizes a high gain difference wave beam obtained by subarray synthesis and auxiliary wave beams which are synthesized by a plurality of subarrays and point to a plurality of sidelobe interference directions to cancel main-sidelobe interference. Theoretical analysis and simulation results show that the method can effectively inhibit the interference of main and auxiliary lobes and ensure the single pulse angle measurement capability, and solves the problem caused by the multiple array elements of the phased array system radar by utilizing the method of sub-array dimension reduction processing. Chen Xinzhu and the like respectively perform one-dimensional self-adaptive beam forming and one-dimensional difference beam forming on the uniform rectangular area array row and the uniform rectangular area array column, so that not only can the interference of a main lobe and a side lobe be inhibited in one dimension, but also the linearity of a single pulse ratio curve of the other-dimensional difference beam is ensured, but when the interference angle is the same as that of a target in one dimension, the angle measurement performance of the other dimension is deteriorated.
Disclosure of Invention
The present invention aims to solve the above-mentioned problems of the prior art and provide a method for forming a single pulse beam and estimating an angle by using a split-dimensional adaptive method.
The technical solution for realizing the purpose of the invention is as follows: a method of split-dimensional adaptive monopulse beamforming and angle estimation, the method comprising the steps of:
step 1, for a uniform rectangular area array of M multiplied by N array elements, any array element is taken to receive signals;
step 2, carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe, if not, executing the step 3, otherwise, executing the step 4;
step 3, calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
step 4, calculating the v-dimension static weight and u-dimension constraint self-adaptive sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
step 5, any row of array element receiving signals are taken, characteristic decomposition is carried out on the row of array element receiving signals, whether the u-dimensional angle of the side lobe interference is in the main lobe or not is judged, if not, the step 6 is executed, otherwise, the step 7 is executed;
step 6, calculating u-dimensional self-adaptive weights and v-dimensional static sum and difference beam weights, and further obtaining the sum and difference beam weights of the v-dimensional angle measurement;
step 7, calculating u-dimensional static weights and v-dimensional constraint self-adaptive sum and difference beam weights, and further obtaining sum and difference beam weights of v-dimensional angle measurement;
and 8, obtaining a u-dimensional angle measurement result by utilizing the u-dimensional angle measurement sum and difference beam weights, and obtaining a v-dimensional angle measurement result by utilizing the v-dimensional angle measurement sum and difference beam weights.
A system for split-dimensional adaptive monopulse beamforming and angle estimation, said system comprising, in order:
the first signal extraction module is used for receiving signals from any array element for the uniform rectangular area array of M multiplied by N array elements;
the first judging module is used for carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe or not, if not, executing the first calculating module, otherwise, executing the second calculating module;
the first calculation module is used for calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weight so as to obtain the u-dimension angle measurement sum and difference beam weight, and then executing the second signal extraction module;
the second calculation module is used for calculating the v-dimensional static weight and the u-dimensional constraint self-adaptive sum and difference beam weight so as to obtain the sum and difference beam weights of the u-dimensional angle measurement, and then executing the second signal extraction module;
the second signal extraction module is used for receiving signals from any row of array elements for the uniform rectangular area array of the M multiplied by N array elements;
the second judging module is used for carrying out characteristic decomposition on the row array element received signals, judging whether the u-dimensional angle of the side lobe interference is in the main lobe or not, if not, executing the third calculating module, otherwise, executing the fourth calculating module;
the third calculation module is used for calculating the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing the fifth calculation module;
the fourth calculation module is used for calculating the u-dimensional static weight and the v-dimensional constraint self-adaptive sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing a fifth calculation module;
and a fifth calculation module, which obtains a u-dimensional angle measurement result by using the u-dimensional angle measurement sum and difference beam weights, and obtains a v-dimensional angle measurement result by using the v-dimensional angle measurement sum and difference beam weights.
Compared with the prior art, the invention has the remarkable advantages that: 1) The main side lobe interference can be restrained; 2) The constraint self-adaptive monopulse algorithm is utilized to ensure the linearity of a monopulse ratio curve; 3) A larger measurable angular region within the main lobe; 4) Has higher angle measurement precision.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a diagram of a rectangular area array model in one embodiment.
Figure 2 is a flow diagram of u-dimensional angle measurement monopulse and difference beamforming in one embodiment.
FIG. 3 is a diagram of simulation parameters for simulation condition 1 in one embodiment.
Fig. 4 is a single pulse sum and difference beam pattern of simulation condition 1 in one embodiment, wherein the patterns (a) to (d) are a u-dimensional angular sum beam pattern, a u-dimensional angular difference beam pattern, a v-dimensional angular sum beam pattern, and a v-dimensional angular difference beam pattern, respectively.
FIG. 5 is a graph of the results of angle estimation for simulation condition 1 in one embodiment.
FIG. 6 is a graph of the angle estimation root mean square error versus the signal to noise ratio for simulation condition 1 in one embodiment.
FIG. 7 is a diagram of simulation parameters for simulation condition 2 in one embodiment.
FIG. 8 is a one-dimensional constrained adaptive monopulse and differential beam pattern versus monopulse ratio graph simulating condition 2 in one embodiment, where graph (a) is a one-dimensional and differential beam pattern for u-dimensional goniometry and graph (b) is a monopulse ratio graph for u-dimensional goniometry.
Fig. 9 is a single pulse sum and difference beam pattern of simulation condition 2 in one embodiment, wherein the patterns (a) to (d) are a u-dimensional angular sum beam pattern, a u-dimensional angular difference beam pattern, a v-dimensional angular sum beam pattern, and a v-dimensional angular difference beam pattern, respectively.
FIG. 10 is a graph of the results of angle estimation for simulation condition 2 in one embodiment.
FIG. 11 is a graph of the root mean square error of the angle estimate versus the signal to noise ratio for simulation condition 2 in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is only for descriptive purposes, and is not to be construed as indicating or implying relative importance or implying that the number of technical features indicated is indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
In one embodiment, the invention provides a method for forming a split-dimensional adaptive monopulse beam and estimating an angle, which comprises the following steps:
first, single pulse and difference beam forming of u-dimensional angle measurement is performed (as shown in fig. 2):
step 1, for a uniform rectangular area array (shown in figure 1) of M multiplied by N array elements, any array element is taken to receive signals;
step 2, carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe, if not, executing the step 3, otherwise, executing the step 4;
step 3, calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
step 4, calculating the v-dimension static weight and u-dimension constraint self-adaptive sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
next, monopulse and difference beamforming of v-dimensional goniometry is performed:
step 5, any row of array element receiving signals are taken, characteristic decomposition is carried out on the row of array element receiving signals, whether the u-dimensional angle of the side lobe interference is in the main lobe or not is judged, if not, the step 6 is executed, otherwise, the step 7 is executed;
step 6, calculating u-dimensional self-adaptive weights and v-dimensional static sum and difference beam weights, and further obtaining the sum and difference beam weights of the v-dimensional angle measurement;
step 7, calculating u-dimensional static weights and v-dimensional constraint self-adaptive sum and difference beam weights, and further obtaining sum and difference beam weights of v-dimensional angle measurement;
and 8, obtaining a u-dimensional angle measurement result by utilizing the u-dimensional angle measurement sum and difference beam weights, and obtaining a v-dimensional angle measurement result by utilizing the v-dimensional angle measurement sum and difference beam weights.
In order to ensure the performance of monopulse angle measurement on the premise of restraining the interference of main side lobes and side lobes, the invention utilizes the thought of split-dimensional monopulse beam forming to decompose a rectangular area array into a one-dimensional equivalent linear array with two dimensions of rows and columns, carries out interference suppression, monopulse and difference beam forming on the one-dimensional equivalent linear array, solves the problem of the deterioration of the angle measurement performance of the other dimension when the interference angle of the side lobes is the same as the target in one dimension, and has higher angle measurement precision.
Further, in one embodiment, the step 2 of performing feature decomposition on the array element received signal, and determining whether the v-dimension angle of the sidelobe interference is within the main lobe includes:
step 2-1, for the array elementSampling covariance matrix of received signalAnd (3) performing characteristic decomposition to obtain:
wherein lambda is i Represents the ith eigenvalue, u i Representing the feature vector corresponding to the ith feature value, U s Representing a signal subspace, Λ s Representing a matrix of eigenvalues corresponding to the subspace of the signal, U n Representing noise subspace, Λ n Representing a characteristic value matrix corresponding to the noise subspace, wherein M represents the number of characteristic values;
step 2-2, the eigenvalues are arranged in descending order to obtain lambda 1 ≥λ 2 ≥…≥λ P+Q ≥λ P+Q+1 …≥λ M Wherein lambda is 1 …λ P+Q For P+Q major-sidelobe interference corresponding larger eigenvalues, lambda P+Q+1 …λ M As the characteristic value corresponding to noise, u k K=1, 2, …, p+q, which is the eigenvector corresponding to the main-sidelobe interference;
step 2-3, calculating the corresponding steering vector a of the beam direction v (v 0 ) Eigenvector u corresponding to main-sidelobe interference k Is a correlation coefficient of (a):
step 2-4, judging whether the following conditions are met:
|ρ(u k ,a v (v 0 ))|≥η
wherein η is a constant;
if so, the v-dimensional angle representing the sidelobe interference is within the main lobe, otherwise, the v-dimensional angle is not within the main lobe.
Further, in one embodiment, the calculating the v-dimension adaptive weights and the u-dimension static sum and difference beam weights in step 3 further obtains the sum and difference beam weights of the u-dimension angle measurement, specifically includes:
step 3-1, calculating the self-adaptive weight of the v-dimension inhibition main lobe and side lobe interference, and the u-dimension static sum and difference beam weight by using sampling covariance matrix inversion, i.e. an SMI method;
and 3-2, respectively calculating the Cronecker product of the v-dimensional self-adaptive weight and the u-dimensional static sum and difference beam weights to obtain the sum and difference beam weights of the u-dimensional angle measurement.
Further, in one embodiment, the calculating the sum and difference beam weights of the v-dimensional static weights and the u-dimensional constraint in step 4 further obtains the sum and difference beam weights of the u-dimensional angle measurement, and the specific process includes:
step 4-1, obtaining v-dimensional static weight, namely the corresponding steering vector a of beam pointing v (v 0 )；
Step 4-2, calculating u-dimensional constraint self-adaptive sum and difference beam weights by using a constraint self-adaptive monopulse beam forming algorithm, namely a CAM algorithm, and specifically: any row of array element receiving signals are taken, and a u-dimensional constraint self-adaption and beam weight w are calculated by using an SMI method ∑ Then calculating u-dimensional constraint self-adaptive difference beam weights by a constraint difference sum ratio method;
the calculation process of the u-dimensional constraint self-adaptive difference beam weight is as follows:
(1) Determining a point u in the main lobe to be constrained by utilizing a theoretical angle measurement curve con With corresponding theoretical monopulse ratio y u The method comprises the steps of carrying out a first treatment on the surface of the Assuming there are 2m+1 constraint points, then:
wherein u is 0 Representing the position of the beam pointing, deltau 1 …Δu m Representing the positions of m constraint points, K 1 …K m Representing theoretical monopulse ratio of corresponding positions of m constraint points;
the constraint matrix C and the corresponding response f are thus obtained:
wherein a is u Representing the guiding vector corresponding to the u-dimensional one-dimensional equivalent linear array,
(2) The following optimization problem is solved by using a linear constraint least variance method, namely an LCMV method:
obtaining the optimal weight of the differential beam, namely u-dimensional constraint self-adaptive differential beam weight w Δ The method comprises the following steps:
and 4-3, respectively calculating the Cronecker product of the v-dimensional static weight and the u-dimensional constraint self-adaptive sum and difference beam weight to obtain the sum and difference beam weights of the u-dimensional angle measurement.
Further, in one embodiment, in step 5, any one of the line array element receiving signals is selected, and feature decomposition is performed on the line array element receiving signals to determine whether the u-dimensional angle of the sidelobe interference is within the main lobe, and the specific process includes:
step 5-1, sampling covariance matrix of the row array element receiving signalAnd (3) performing characteristic decomposition to obtain:
wherein lambda is i Represents the ith eigenvalue, u i Representing the feature vector corresponding to the ith feature value, U s Representing a signal subspace, Λ s Representing a matrix of eigenvalues corresponding to the subspace of the signal, U n Representing noise subspace, Λ n Representing a characteristic value matrix corresponding to the noise subspace, wherein N represents the number of characteristic values;
step 5-2, the eigenvalues are arranged in descending order to obtain lambda 1 ≥λ 2 ≥…≥λ P+Q ≥λ P+Q+1 …≥λ N Wherein lambda is 1 …λ P+Q For P+Q major-sidelobe interference corresponding larger eigenvalues, lambda P+Q+1 …λ N As the characteristic value corresponding to noise, u k K=1, 2, …, p+q, which is the eigenvector corresponding to the main-sidelobe interference;
step 5-3, calculating the corresponding steering vector a of the beam direction u (u 0 ) Eigenvector u corresponding to main-sidelobe interference k Is a correlation coefficient of (a):
step 5-4, judging whether the following conditions are met:
|ρ(u k ,a u (u 0 ))|≥η
wherein η is a constant;
if so, the u-dimensional angle representing the sidelobe interference is in the main lobe, otherwise, the u-dimensional angle representing the sidelobe interference is not in the main lobe.
Further, in one embodiment, the calculating u-dimensional adaptive weights and v-dimensional static sum and difference beam weights in step 6 further obtains sum and difference beam weights of v-dimensional angle measurement, which specifically includes:
step 6-1, calculating the self-adaptive weight of u-dimensional inhibition main lobe and side lobe interference, and v-dimensional static sum and difference beam weights by using sampling covariance matrix inversion, i.e. an SMI method;
and 6-2, respectively calculating the Cronecker product of the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights to obtain the sum and difference beam weights of the v-dimensional angle measurement.
Further, in one embodiment, the step 7 of calculating the u-dimensional static weight and the v-dimensional constraint adaptive sum and difference beam weights further obtains the sum and difference beam weights of the v-dimensional angle measurement, and the specific process includes:
step 7-1, obtaining u-dimensional static weight, namely the corresponding steering vector a of beam pointing u (u 0 )；
Step 7-2, calculating v-dimensional constraint self-adaptive sum and difference beam weights by using a constraint self-adaptive monopulse beam forming algorithm, namely a CAM algorithm, and specifically: any array element is taken to receive signals, and the v-dimension constraint self-adaption and the beam weight w are calculated by using an SMI method ∑ Then calculating the beam weight of the v-dimensional constraint self-adaptive difference by a constraint difference sum ratio method;
the calculation process of the v-dimension constraint self-adaptive difference beam weight is as follows:
(1) Determining a point v in the main lobe to be constrained by using a theoretical angle measurement curve con With corresponding theoretical monopulse ratio y v The method comprises the steps of carrying out a first treatment on the surface of the Assuming there are 2m+1 constraint points, then:
in the formula, v 0 Representing the position of the beam pointing, deltav 1 …Δv m Representing the positions of m constraint points, K 1 …K m Representing theoretical monopulse ratio of corresponding positions of m constraint points;
the constraint matrix C and the corresponding response f are thus obtained:
wherein a is v Representing a guiding vector corresponding to a one-dimensional equivalent linear array of v dimension;
(2) The following optimization problem is solved by using a linear constraint least variance method, namely an LCMV method:
obtaining the optimal weight of the differential beam, namely v-dimension constraint self-adaptive differential beam weight w Δ The method comprises the following steps:
and 7-3, respectively calculating the Cronecker product of the u-dimensional static weight and the v-dimensional constraint self-adaptive sum and difference beam weights to obtain the sum and difference beam weights of the v-dimensional angle measurement.
In one embodiment, a system for split-dimensional adaptive monopulse beamforming and angle estimation is provided, the system comprising:
the first signal extraction module is used for receiving signals from any array element for the uniform rectangular area array of M multiplied by N array elements;
the first judging module is used for carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe or not, if not, executing the first calculating module, otherwise, executing the second calculating module;
the first calculation module is used for calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weight so as to obtain the u-dimension angle measurement sum and difference beam weight, and then executing the second signal extraction module;
the second calculation module is used for calculating the v-dimensional static weight and the u-dimensional constraint self-adaptive sum and difference beam weight so as to obtain the sum and difference beam weights of the u-dimensional angle measurement, and then executing the second signal extraction module;
the second signal extraction module is used for receiving signals from any row of array elements for the uniform rectangular area array of the M multiplied by N array elements;
the second judging module is used for carrying out characteristic decomposition on the row array element received signals, judging whether the u-dimensional angle of the side lobe interference is in the main lobe or not, if not, executing the third calculating module, otherwise, executing the fourth calculating module;
the third calculation module is used for calculating the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing the fifth calculation module;
the fourth calculation module is used for calculating the u-dimensional static weight and the v-dimensional constraint self-adaptive sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing a fifth calculation module;
and a fifth calculation module, which obtains a u-dimensional angle measurement result by using the u-dimensional angle measurement sum and difference beam weights, and obtains a v-dimensional angle measurement result by using the v-dimensional angle measurement sum and difference beam weights.
For specific limitations of the system for forming and estimating the angle of the split-dimensional adaptive monopulse beam, reference may be made to the above limitation of the method for forming and estimating the angle of the split-dimensional adaptive monopulse beam, which is not repeated herein. The various modules in the above described fractal dimension adaptive monopulse beamforming and angle estimation system may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
As a specific example, in one embodiment, the present invention is described in further detail. In this embodiment:
simulation condition 1: consider the case where the angle of the sidelobe canceling in both dimensions is not within the main lobe. The array element number is 16 multiplied by 16, and the array element interval is half wavelength. The angle of beam pointing is azimuth angle 0 degree, pitch angle 0 degree, the angle of target is azimuth angle-2 degree, pitch angle 0 degree, and signal to noise ratio is 0dB. The angle of the interference signal 1 is 3 degrees of azimuth angle, 4 degrees of pitch angle and the dry-to-noise ratio is 30dB. The angle of the interference signal 2 is 20 degrees in azimuth angle, 20 degrees in pitch angle and 20dB in dry-to-noise ratio. The specific parameters are shown in fig. 3.
The invention discloses a method for forming a split-dimensional self-adaptive monopulse wave beam and estimating an angle, which comprises the following steps:
the first step: any array element is taken to receive signals.
And a second step of: and carrying out characteristic decomposition on the received signal, and judging that the v-dimensional angle of the obtained side lobe interference is not in the main lobe.
And a third step of: interference suppression is performed in the v dimension: and obtaining the self-adaptive weight of the v-dimension inhibition main lobe and side lobe interference by using a sampling covariance matrix inversion method, and obtaining static sum and difference beam weights in the u-dimension. And then, the Cronecker product of the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weights is calculated respectively to obtain the sum and difference beam weights of the u-dimension angle measurement, and the corresponding directional diagrams are shown in fig. 4 (a) and 4 (b).
Fourth step: any row of array elements is taken to receive signals.
Fifth step: and carrying out characteristic decomposition on the received signal, and judging that the u-dimensional angle of the obtained side lobe interference is not in the main lobe.
Sixth step: interference suppression is performed in the u dimension: and obtaining the self-adaptive weight of the u-dimensional inhibition main lobe and the side lobe interference by using a sampling covariance matrix inversion method, and obtaining the static sum and difference beam weights in the v-dimension. And then, the Cronecker product of the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights is calculated respectively to obtain the sum and difference beam weights of the v-dimensional angle measurement, and the corresponding directional diagrams are shown in fig. 4 (c) and 4 (d).
Seventh step: two-dimensional monopulse angle measurement is carried out by using the obtained 4 sum and difference beams, the result is shown in figure 5, and the relation between the root mean square error of angle estimation obtained by changing the signal to noise ratio and the signal to noise ratio is shown in figure 6.
Simulation condition 2: consider the case where the sidelobe interference has a one-dimensional angle within the main lobe and the same as the dimension is at the target angle. The array element number is 16 multiplied by 16, and the array element interval is half wavelength. The angle of beam pointing is azimuth angle 0 degree, pitch angle 0 degree, the angle of target is azimuth angle-5 degree, pitch angle 0 degree, and signal to noise ratio is 0dB. The angle of the interference signal 1 is 3 degrees of azimuth angle, 4 degrees of pitch angle and the dry-to-noise ratio is 30dB. The angle of the interference signal 2 is 20 degrees in azimuth, 0 degrees in pitch and 20dB in dry-to-noise ratio. The specific parameters are shown in fig. 7.
The first step: any array element is taken to receive signals.
And a second step of: and carrying out characteristic decomposition on the received signal, and judging that the v-dimensional angle of the obtained side lobe interference is in the main lobe.
And a third step of: interference suppression is performed in the u dimension: the adaptive sum and difference beam weights of the u-dimensional suppressed main lobe and side lobe interference are obtained by using a constraint adaptive monopulse method, a corresponding one-dimensional directional diagram and monopulse ratio curve is shown in fig. 8, and a static weight is obtained in the v-dimension. And respectively calculating the Kronecker product of the u-dimensional constraint self-adaptive single pulse sum and difference beam weight and the v-dimensional static weight to obtain the sum and difference beam weights of the u-dimensional angle measurement, wherein the corresponding direction diagrams are shown in fig. 9 (a) and 9 (b).
Fourth step: any row of array elements is taken to receive signals.
Fifth step: and carrying out characteristic decomposition on the received signal, and judging that the u-dimensional angle of the obtained side lobe interference is not in the main lobe.
Sixth step: interference suppression is performed in the u dimension: and obtaining the self-adaptive weight of the u-dimensional inhibition main lobe and the side lobe interference by using a sampling covariance matrix inversion method, and obtaining the static sum and difference beam weights in the v-dimension. And then the kronecker product of the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights is calculated respectively to obtain the sum and difference beam weights of the v-dimensional angle measurement, and the corresponding directional diagrams are shown in fig. 9 (c) and 9 (d).
Seventh step: two-dimensional monopulse angle measurement is carried out by using the obtained 4 sum and difference beams, the result is shown in figure 10, and the relation between the root mean square error of angle estimation obtained by changing the signal to noise ratio and the signal to noise ratio is shown in figure 11.
The accuracy of the angle estimation is seen in fig. 5, 6, 10 and 11, and the accuracy of the method for forming the split-dimensional self-adaptive monopulse beam and estimating the angle is verified.
The foregoing description of the embodiments is provided to facilitate the understanding and use of the invention by those skilled in the art. It will be apparent to those skilled in the art that various modifications can be readily made to these embodiments and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above-described embodiments, and those skilled in the art, based on the disclosure of the present invention, should make improvements and modifications without departing from the scope of the present invention.
Claims (8)
1. A method for split-dimensional adaptive monopulse beamforming and angle estimation, said method comprising the steps of:
step 1, for a uniform rectangular area array of M multiplied by N array elements, any array element is taken to receive signals;
step 2, carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe, if not, executing the step 3, otherwise, executing the step 4;
step 3, calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
step 4, calculating the v-dimension static weight and u-dimension constraint self-adaptive sum and difference beam weights, and further obtaining the u-dimension angle measurement sum and difference beam weights;
step 5, any row of array element receiving signals are taken, characteristic decomposition is carried out on the row of array element receiving signals, whether the u-dimensional angle of the side lobe interference is in the main lobe or not is judged, if not, the step 6 is executed, otherwise, the step 7 is executed;
step 6, calculating u-dimensional self-adaptive weights and v-dimensional static sum and difference beam weights, and further obtaining the sum and difference beam weights of the v-dimensional angle measurement;
step 7, calculating u-dimensional static weights and v-dimensional constraint self-adaptive sum and difference beam weights, and further obtaining sum and difference beam weights of v-dimensional angle measurement;
and 8, obtaining a u-dimensional angle measurement result by utilizing the u-dimensional angle measurement sum and difference beam weights, and obtaining a v-dimensional angle measurement result by utilizing the v-dimensional angle measurement sum and difference beam weights.
2. The method for forming and estimating angle of split-dimensional adaptive monopulse beam according to claim 1, wherein in step 2, the characteristic decomposition is performed on the array element received signal to determine whether the v-dimensional angle of the sidelobe interference is within the main lobe, and the specific process includes:
step 2-1, sampling covariance matrix of the array element receiving signalAnd (3) performing characteristic decomposition to obtain:
wherein lambda is i Represents the ith eigenvalue, u i Representing the feature vector corresponding to the ith feature value, U s Representing a signal subspace, Λ s Representing a matrix of eigenvalues corresponding to the subspace of the signal, U n Representing noise subspace, Λ n Representation ofA characteristic value matrix corresponding to the noise subspace, wherein M represents the number of characteristic values;
step 2-2, the eigenvalues are arranged in descending order to obtain lambda 1 ≥λ 2 ≥…≥λ P+Q ≥λ P+Q+1 …≥λ M Wherein lambda is 1 …λ P+Q For P+Q major-sidelobe interference corresponding larger eigenvalues, lambda P+Q+1 …λ M As the characteristic value corresponding to noise, u k K=1, 2, …, p+q, which is the eigenvector corresponding to the main-sidelobe interference;
step 2-3, calculating the corresponding steering vector a of the beam direction v (v 0 ) Eigenvector u corresponding to main-sidelobe interference k Is a correlation coefficient of (a):
step 2-4, judging whether the following conditions are met:
|ρ(u k ,a v (v 0 ))|≥η
wherein η is a constant;
if so, the v-dimensional angle representing the sidelobe interference is within the main lobe, otherwise, the v-dimensional angle is not within the main lobe.
3. The method for forming and estimating angles of split-dimensional adaptive monopulse beams according to claim 1 or 2, wherein the step 3 of calculating v-dimensional adaptive weights and u-dimensional static sum and difference beam weights to obtain u-dimensional angle measurement sum and difference beam weights specifically comprises:
step 3-1, calculating the self-adaptive weight of the v-dimension inhibition main lobe and side lobe interference, and the u-dimension static sum and difference beam weight by using sampling covariance matrix inversion, i.e. an SMI method;
and 3-2, respectively calculating the Cronecker product of the v-dimensional self-adaptive weight and the u-dimensional static sum and difference beam weights to obtain the sum and difference beam weights of the u-dimensional angle measurement.
4. The method for forming and estimating angles of a split-dimensional adaptive monopulse beam according to claim 3, wherein in the step 4, the sum and difference beam weights of the v-dimensional static weights and the u-dimensional constraint are calculated, and further the sum and difference beam weights of the u-dimensional angle measurement are obtained, and the specific process includes:
step 4-1, obtaining v-dimensional static weight, namely the corresponding steering vector a of beam pointing v (v 0 )；
Step 4-2, calculating u-dimensional constraint self-adaptive sum and difference beam weights by using a constraint self-adaptive monopulse beam forming algorithm, namely a CAM algorithm, and specifically: any row of array element receiving signals are taken, and a u-dimensional constraint self-adaption and beam weight w are calculated by using an SMI method ∑ Then calculating u-dimensional constraint self-adaptive difference beam weights by a constraint difference sum ratio method;
the calculation process of the u-dimensional constraint self-adaptive difference beam weight is as follows:
(1) Determining a point u in the main lobe to be constrained by utilizing a theoretical angle measurement curve con With corresponding theoretical monopulse ratio y u The method comprises the steps of carrying out a first treatment on the surface of the Assuming there are 2m+1 constraint points, then:
wherein u is 0 Representing the position of the beam pointing, deltau 1 …Δu m Representing the positions of m constraint points, K 1 …K m Representing theoretical monopulse ratio of corresponding positions of m constraint points;
the constraint matrix C and the corresponding response f are thus obtained:
wherein a is u Representing the guiding vector corresponding to the u-dimensional one-dimensional equivalent linear array,
(2) The following optimization problem is solved by using a linear constraint least variance method, namely an LCMV method:
obtaining the optimal weight of the differential beam, namely u-dimensional constraint self-adaptive differential beam weight w Δ The method comprises the following steps:
and 4-3, respectively calculating the Cronecker product of the v-dimensional static weight and the u-dimensional constraint self-adaptive sum and difference beam weight to obtain the sum and difference beam weights of the u-dimensional angle measurement.
5. The method for forming and estimating angle of split-dimensional adaptive monopulse beam according to claim 1, wherein in step 5, any row of array element receiving signals is taken, characteristic decomposition is performed on the row of array element receiving signals, and whether u-dimensional angle of side lobe interference is in main lobe is determined, and the specific process includes:
step 5-1, sampling covariance matrix of the row array element receiving signalAnd (3) performing characteristic decomposition to obtain:
wherein lambda is i Represents the ith eigenvalue, u i Representing the feature vector corresponding to the ith feature value, U s Representing a signal subspace, Λ s Representing a matrix of eigenvalues corresponding to the subspace of the signal, U n Representing noise subspace, Λ n Representing a characteristic value matrix corresponding to the noise subspace, wherein N represents the number of characteristic values;
step 5-2, the eigenvalues are arranged in descending order to obtain lambda 1 ≥λ 2 ≥…≥λ P+Q ≥λ P+Q+1 …≥λ N Wherein lambda is 1 …λ P+Q For P+Q major-sidelobe interference corresponding larger eigenvalues, lambda P+Q+1 …λ N As the characteristic value corresponding to noise, u k K=1, 2, …, p+q, which is the eigenvector corresponding to the main-sidelobe interference;
step 5-3, calculating the corresponding steering vector a of the beam direction u (u 0 ) Eigenvector u corresponding to main-sidelobe interference k Is a correlation coefficient of (a):
step 5-4, judging whether the following conditions are met:
|ρ(u k ,a u (u 0 ))|≥η
wherein η is a constant;
if so, the u-dimensional angle representing the sidelobe interference is in the main lobe, otherwise, the u-dimensional angle representing the sidelobe interference is not in the main lobe.
6. The method for forming and estimating angles of a split-dimensional adaptive monopulse beam according to claim 5, wherein step 6 is for calculating u-dimensional adaptive weights and v-dimensional static sum and difference beam weights to obtain v-dimensional angle measurement sum and difference beam weights, and comprises:
step 6-1, calculating the self-adaptive weight of u-dimensional inhibition main lobe and side lobe interference, and v-dimensional static sum and difference beam weights by using sampling covariance matrix inversion, i.e. an SMI method;
and 6-2, respectively calculating the Cronecker product of the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights to obtain the sum and difference beam weights of the v-dimensional angle measurement.
7. The method for forming and estimating angles of split-dimensional adaptive monopulse beams according to claim 1, wherein the step 7 is characterized in that the step of calculating u-dimensional static weights and v-dimensional constraint adaptive sum and difference beam weights to obtain v-dimensional angle measurement sum and difference beam weights comprises the following steps:
step 7-1, obtaining u-dimensional static weight, namely the corresponding steering vector a of beam pointing u (u 0 )；
Step 7-2, calculating v-dimensional constraint self-adaptive sum and difference beam weights by using a constraint self-adaptive monopulse beam forming algorithm, namely a CAM algorithm, and specifically: any array element is taken to receive signals, and the v-dimension constraint self-adaption and the beam weight w are calculated by using an SMI method ∑ Then calculating the beam weight of the v-dimensional constraint self-adaptive difference by a constraint difference sum ratio method;
the calculation process of the v-dimension constraint self-adaptive difference beam weight is as follows:
(1) Determining a point v in the main lobe to be constrained by using a theoretical angle measurement curve con With corresponding theoretical monopulse ratio y v The method comprises the steps of carrying out a first treatment on the surface of the Assuming there are 2m+1 constraint points, then:
in the formula, v 0 Representing the position of the beam pointing, deltav 1 …Δv m Representing the positions of m constraint points, K 1 …K m Representing theoretical monopulse ratio of corresponding positions of m constraint points;
the constraint matrix C and the corresponding response f are thus obtained:
wherein a is v Representing a guiding vector corresponding to a one-dimensional equivalent linear array of v dimension;
(2) The following optimization problem is solved by using a linear constraint least variance method, namely an LCMV method:
obtaining the optimal weight of the differential beam, namely v-dimension constraint self-adaptive differential beam weight w Δ The method comprises the following steps:
and 7-3, respectively calculating the Cronecker product of the u-dimensional static weight and the v-dimensional constraint self-adaptive sum and difference beam weights to obtain the sum and difference beam weights of the v-dimensional angle measurement.
8. A split-dimensional adaptive monopulse beamforming and angle estimation system implementing the method of any of claims 1 to 7, said system comprising, in order:
the first signal extraction module is used for receiving signals from any array element for the uniform rectangular area array of M multiplied by N array elements;
the first judging module is used for carrying out characteristic decomposition on the array element received signals, judging whether the v-dimensional angle of the sidelobe interference is in the main lobe or not, if not, executing the first calculating module, otherwise, executing the second calculating module;
the first calculation module is used for calculating the v-dimension self-adaptive weight and the u-dimension static sum and difference beam weight so as to obtain the u-dimension angle measurement sum and difference beam weight, and then executing the second signal extraction module;
the second calculation module is used for calculating the v-dimensional static weight and the u-dimensional constraint self-adaptive sum and difference beam weight so as to obtain the sum and difference beam weights of the u-dimensional angle measurement, and then executing the second signal extraction module;
the second signal extraction module is used for receiving signals from any row of array elements for the uniform rectangular area array of the M multiplied by N array elements;
the second judging module is used for carrying out characteristic decomposition on the row array element received signals, judging whether the u-dimensional angle of the side lobe interference is in the main lobe or not, if not, executing the third calculating module, otherwise, executing the fourth calculating module;
the third calculation module is used for calculating the u-dimensional self-adaptive weight and the v-dimensional static sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing the fifth calculation module;
the fourth calculation module is used for calculating the u-dimensional static weight and the v-dimensional constraint self-adaptive sum and difference beam weights, further obtaining the sum and difference beam weights of the v-dimensional angle measurement, and then executing a fifth calculation module;
and a fifth calculation module, which obtains a u-dimensional angle measurement result by using the u-dimensional angle measurement sum and difference beam weights, and obtains a v-dimensional angle measurement result by using the v-dimensional angle measurement sum and difference beam weights.
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