CN112882007A - Single-pulse multi-target super-resolution angle measurement implementation method based on sparse array radar - Google Patents

Single-pulse multi-target super-resolution angle measurement implementation method based on sparse array radar Download PDF

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CN112882007A
CN112882007A CN202110038771.XA CN202110038771A CN112882007A CN 112882007 A CN112882007 A CN 112882007A CN 202110038771 A CN202110038771 A CN 202110038771A CN 112882007 A CN112882007 A CN 112882007A
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王明江
闻映红
张金宝
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Beijing Jiaotong University
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    • 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
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Abstract

The invention provides a monopulse multi-target super-resolution angle measurement implementation method based on a sparse array radar. The method comprises the following steps: performing one-dimensional range profile constant false alarm detection on target echo signals of multiple channels of the monopulse sparse array radar to obtain array signals of distance units where the targets are located, and constructing angle observation vectors of the targets according to the array signals of the distance units where the targets are located; constructing an angle measurement dictionary matrix by using working parameters of the sparse array radar system, and constructing an angle observation matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar; based on the angle observation vector and the angle observation matrix of the target, the phase delay vectors of different targets are reconstructed and imaged by using an optimization algorithm, so that multi-target super-resolution and angle measurement of target echo signals are realized. The method can perform angle resolution and accurate measurement on a plurality of targets with similar distances and angles in the wave beam from radar single pulse echo data.

Description

Single-pulse multi-target super-resolution angle measurement implementation method based on sparse array radar
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a monopulse multi-target super-resolution angle measurement implementation method based on a sparse array radar.
Background
Array radars have wide application in the field of object detection. In the radar detection technology, the range image of a target is limited by range resolution, and the angle resolution capability is limited by the radar beam width, so the high-resolution technology of dense multiple targets is a difficult point in the field of radar detection.
At present, the target detection and angle measurement method based on the array radar in the prior art mainly includes a constant false alarm detection technology based on a one-dimensional range profile, a beam scanning method based on an array, and the like. The target detection and angle measurement methods have certain defects in the dense multi-target detection. The constant false alarm detection technology based on the one-dimensional distance image cannot realize the resolution of a plurality of targets with similar distances in the wave beam; the array-based beam scanning method cannot accurately measure a plurality of targets with similar angles, and when the distance between the plurality of targets is smaller than a distance unit interval and the angle interval is smaller than half-power beam width, the method cannot accurately distinguish and measure the angles of the plurality of targets in the space.
In summary, how to realize super-resolution and accurate angle measurement of multiple spatially dense targets based on an array radar detection system is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a method for realizing single-pulse multi-target super-resolution angle measurement based on a sparse array radar, which is used for realizing multi-target resolution and angle measurement of radar single-pulse echo data.
In order to achieve the purpose, the invention adopts the following technical scheme.
A monopulse multi-target super-resolution angle measurement realization method based on a sparse array radar comprises the following steps:
performing one-dimensional range profile constant false alarm detection on target echo signals of multiple channels of the monopulse sparse array radar to obtain array signals of distance units where the targets are located, and constructing angle observation vectors of the targets according to the array signals of the distance units where the targets are located;
constructing an angle measurement dictionary matrix by using working parameters of the sparse array radar system, and constructing an angle observation matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar;
based on the angle observation vector of the target and the angle observation matrix, the phase delay vectors of different targets are subjected to reconstruction imaging by utilizing an optimization algorithm, so that multi-target super-resolution and angle measurement of the target echo signal are realized.
Preferably, the one-dimensional range profile constant false alarm detection is performed on the target echo signals of multiple channels of the monopulse sparse array radar to obtain the array signal of the range unit where each target is located, and an angle observation vector of the target is constructed according to the array signal of the range unit where the target is located, including:
carrying out digital beam synthesis on target echo signals of multiple channels of the monopulse sparse array radar, and carrying out matched filtering on echo data after the digital beam synthesis to obtain target one-dimensional range profile data;
and performing one-dimensional distance CFAR constant false alarm detection on the echo data of each distance unit in the one-dimensional range profile data of the target, detecting the targets at different distances, and obtaining the array signals of the distance units where the targets are located.
And constructing an angle observation vector of the target by using the detected array signals of the target distance units.
Preferably, the angular observation vector of the target is represented as:
S(k)＝[s1(k) s2(k) … sP(k)] (5)
in the formula (1), sp(k) The method is characterized in that the k-th target detected by the one-dimensional range profile CFAR is in range unit complex signal data of the P-th channel of the sparse array radar, P is 1, …, P is less than N, and N is the number of the array radar full array channels.
Preferably, the constructing an angle measurement dictionary matrix by using the working parameters of the sparse array radar system includes:
and constructing an angle measurement dictionary matrix based on the array radar according to the full-array radar array arrangement, the working wavelength, the number of full-array sub-arrays, the distance between adjacent sub-arrays and the angle measurement resolution interval corresponding to the sparse array radar system. The angle measurement dictionary matrix is represented as an N × M matrix:
Figure BDA0002894648500000031
in the formula (2), the reaction mixture is,
Figure BDA0002894648500000032
is an angle thetamOf the guide vector, thetamAnd (M-N/2) delta theta, wherein M is the number of the space angle partitions, delta theta is the space angle partition interval, d is the distance between two adjacent subarrays of the array radar, and lambda is the radar working frequency.
Preferably, the constructing the angle observation matrix by using the angle measurement dictionary matrix and the working parameters of the sparse array radar includes:
constructing a corresponding angle observation matrix by using the angle measurement dictionary matrix and the array arrangement position and the sub-array number of the sparse array radar, wherein the angle observation matrix is expressed as follows:
A＝[Φ1 Φ2 … ΦP]T (7)
in formula (3) [ ·]TRepresenting the transpose of the matrix,. phipThe P-th row vector of the angle-measuring dictionary matrix Φ is denoted, P1, …, P < N.
Preferably, the reconstructing and imaging the phase delay vectors of different targets by using an optimization algorithm based on the angle observation vector of the target and the angle observation matrix to realize multi-target super-resolution and angle measurement of the target echo signal includes:
reconstructing and solving projection coefficients of phase delay vectors of different targets under observation matrix base vectors by using a target angle observation vector and an angle observation matrix of the sparse array radar through an optimization algorithm;
at a minimum of1And (3) solving the following optimization model by using an optimization algorithm according to the angle observation matrix A and the angle observation vector S (k) of the sparse array radar by taking the norm as a criterion:
Figure BDA0002894648500000033
in the formula (4), the reaction mixture is,
Figure BDA0002894648500000041
the target backscattering coefficient estimated value of each angle unit in space and argmin are the minimum value of the taking function according to the obtained optimal solution
Figure BDA0002894648500000042
And distinguishing each target at the kth distance unit to obtain the angle of each target.
According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention provides a dense multi-target distinguishing and angle measuring method based on the sparse array radar, and the method can realize angle distinguishing and accurate measurement on a plurality of targets with similar beam internal distances and angles in radar single pulse echo data.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a method for implementing multi-target super-resolution angle measurement based on a sparse array radar according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an observation model of a sparse array radar for spatially dense multiple targets according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating echo path delays of arrays of targets in a beam according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a one-dimensional range profile after pulse compression is performed on single-pulse multi-target echo data based on a sparse array radar according to an embodiment of the present invention;
fig. 5 is a schematic diagram of angle measurement dictionary matrix data constructed by using operating parameters of a sparse array radar system according to an embodiment of the present invention;
fig. 6 is a schematic diagram of angle observation matrix data constructed by using operating parameters of a sparse array radar system according to an embodiment of the present invention;
FIG. 7 is a graph of angle observation data and angle reconstruction results of a single-target sparse array radar for detecting the algorithm according to the embodiment of the present invention; the left graph is angle observation data of a single target in the sparse array radar distance unit; the right image is a result schematic diagram of angle reconstruction imaging of a single target by using an optimization algorithm;
fig. 8 is a diagram of angle observation data and angle reconstruction results of a dual-target sparse array radar for detecting the same distance of the algorithm according to the embodiment of the present invention; the left graph is angle observation data of double targets in the sparse array radar distance unit; and the right image is a result schematic diagram of angle reconstruction imaging of the two targets by utilizing an optimization algorithm.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
An implementation schematic diagram of a method for implementing single-pulse multi-target super-resolution angle measurement based on a sparse array radar is shown in fig. 1, and the method comprises the following processing steps:
step 101: and performing one-dimensional range profile constant false alarm detection on the target echo, and constructing an angle observation vector of the target by using each array signal of the range unit where the target is located.
One-dimensional range images are range projections of the reflected echo intensities of targets at different distances in space from the radar. The one-dimensional distance image CFAR detection is carried out on the target echo, and strong reflection targets with different space distances can be distinguished. Meanwhile, the CFAR is used for detecting each array data of the distance unit where the target is located, and an angle observation vector of the target is constructed.
Fig. 2 is a schematic view of an observation model of a sparse array radar for spatially dense multiple targets according to an embodiment of the present invention. Firstly, carrying out digital beam synthesis on target echo signals of a plurality of channels of the monopulse sparse array radar, and improving the signal-to-noise ratio of detection data; performing matched filtering on the echo data after the digital beam synthesis to obtain target one-dimensional range profile data; then, one-dimensional distance CFAR (Constant False Alarm Rate) detection is performed on the echo data of each range cell in the target one-dimensional range image data, and targets at different distances are detected to obtain array signals of the range cells where the targets are located.
Constructing an angle observation vector of the target by using each array signal of the detected distance unit where the target is located, wherein the angle observation vector of the target can be expressed as:
S(k)＝[s1(k) s2(k) … sP(k)] (9)
in the formula (1), sp(k) The method is characterized in that the k-th target detected by the one-dimensional range profile CFAR is in range unit complex signal data of the P-th channel of the sparse array radar, P is 1, …, P is less than N, and N is the number of the array radar full array channels.
Step 102: and constructing an angle measurement dictionary matrix by using the working parameters of the sparse array radar system, and constructing an angle observation matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar.
And constructing an angle measurement dictionary matrix based on the array radar according to the full-array radar array arrangement, the working wavelength, the number of full-array sub-arrays, the distance between adjacent sub-arrays and the angle measurement resolution interval corresponding to the sparse array radar system. The angle measurement dictionary matrix may be represented as an N × M matrix:
Figure BDA0002894648500000071
in the formula (2), the reaction mixture is,
Figure BDA0002894648500000072
is an angle thetamOf the guide vector, thetamAnd (M-N/2) delta theta, wherein M is the number of the space angle partitions, delta theta is the space angle partition interval, d is the distance between two adjacent subarrays of the array radar, and lambda is the radar working frequency.
Then, an angle observation matrix is constructed by utilizing the angle measurement dictionary matrix and the array arrangement position and the number of sub-arrays of the sparse array radar, and the angle observation matrix can be expressed as follows:
A＝[Φ1 Φ2 … ΦP]T (11)
in formula (3) [ ·]TRepresenting the transpose of the matrix,. phipThe P-th row vector of the angle-measuring dictionary matrix Φ is denoted, P1, …, P < N.
Step 103: and based on the target angle observation vector and the angle observation matrix, reconstructing and imaging the target angle by using an optimization algorithm to realize multi-target super-resolution and angle measurement.
Fig. 3 is a schematic diagram of echo path delays of arrays of targets in a beam according to an embodiment of the present invention. When the distances of a plurality of targets in a beam are the same and the space angles are close, radar one-dimensional range images of the plurality of targets are overlapped on a range unit, and at the moment, the one-dimensional range images cannot distinguish the plurality of targets with the same distance. Meanwhile, because the angle interval of the multiple targets is smaller than the radar beam width, the multiple targets cannot be distinguished depending on the radar beam width. In this case, since the phase delays of the echoes of the targets at different angles to the respective arrays of the radar are different, the projections of the phase differences of the echoes of the different targets in the respective arrays under the basis vectors of the system measurement dictionary matrix are also different. Based on the properties, by utilizing a target angle observation vector and an angle observation matrix of the sparse array radar and adopting an optimization algorithm to reconstruct and solve projection coefficients of phase delay vectors of different targets under a base vector of the observation matrix, a plurality of targets with the same distance and similar angles can be super-resolved, and the accurate measurement of each target angle can be realized.
In particular, with a minimum of1And (3) solving the following optimization model by using an optimization algorithm according to the angle observation matrix A and the angle observation vector S (k) of the sparse array radar by taking the norm as a criterion:
Figure BDA0002894648500000081
in the formula (4), the reaction mixture is,
Figure BDA0002894648500000082
the target backscattering coefficient estimated value of each angle unit in space and argmin are the minimum value of the taking function according to the obtained optimal solution
Figure BDA0002894648500000083
The targets at the kth range cell can be resolved and the precise angle of each target can be obtained.
The technical solution of the present invention will be further described in detail with reference to the following specific examples.
Example 1
Fig. 4 is a schematic diagram of one-dimensional range profile data of a sparse array radar for checking an algorithm according to an embodiment of the present invention. The working frequency of the sparse array radar is 9.2GHz, the bandwidth of a transmitting signal is 3MHz, the beam width is 0.45 degrees, and 3 target distance radars in the beam are respectively as follows: [10, 15] km, target azimuth angles are: [1.0, 1.3, 0.9] °. As can be seen from the figure, the 1 st and 2 nd targets cannot be resolved by the one-dimensional range image due to the same range, while the 3 rd target can be resolved from the two previous targets by the one-dimensional range image.
FIG. 5 is a data diagram of an angle measurement dictionary matrix constructed using full-array radar system parameters, where the number of full-array radar sub-arrays is 128, the dictionary matrix angle range is [ -5,5] °, and the angle division interval is 0.1 °.
Fig. 6 is an angle observation matrix constructed by using the array arrangement of the sparse array radar system, wherein the number of the sparse array radar sub-arrays is 16, and is 1/8 of the number of the full array radar sub-arrays.
FIG. 7 is a schematic diagram of a target angle image obtained by utilizing the sparse array radar angle measurement data and reconstruction of the distance unit where the 3 rd target is located. The left graph is an angle observation vector data graph of the full-array radar and the sparse-array radar to the target, and it can be seen from fig. 7 that the sparse-array radar is 1/8 of the observation data volume of the full-array radar. The right image is the angle image data of the target obtained by reconstruction, and as can be seen from fig. 7, since there is only one target at the distance unit, there is only one peak in the angle reconstructed image, and the position of the peak is the angle accurate value of the target.
FIG. 8 is a schematic diagram of angle measurement data of the sparse array radar using the distance units where the 1 st and 2 nd targets are located and a target angle image obtained through reconstruction. The left graph is an angle observation vector data graph of the distance unit of the full-array radar and the sparse-array radar, and the right graph is an angle data graph of each target at the distance unit obtained through reconstruction. As can be seen from fig. 8, since the range unit includes two targets 1 and 2, two peaks appear in the reconstructed angle image, the two targets can be clearly resolved, and the position of each peak is the angle accurate value corresponding to the two targets. The sparse array radar only utilizes the data volume of the full array radar 1/8 to realize the angle super-resolution and accurate measurement of two targets with the same distance and the angle smaller than the beam width.
In summary, the embodiments of the present invention provide a dense multi-target resolving and angle measuring method based on a sparse array radar, which can perform angle resolving and accurate measurement on multiple targets with similar distances and angles in a beam from radar single-pulse echo data.
By the method, the data volume of the array radar system can be greatly reduced, and super-resolution and accurate angle measurement can be performed on a plurality of targets with the same distance and similar angles in the wave beam.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A monopulse multi-target super-resolution angle measurement realization method based on a sparse array radar is characterized by comprising the following steps:
performing one-dimensional range profile constant false alarm detection on target echo signals of multiple channels of the monopulse sparse array radar to obtain array signals of distance units where the targets are located, and constructing angle observation vectors of the targets according to the array signals of the distance units where the targets are located;
constructing an angle measurement dictionary matrix by using working parameters of the sparse array radar system, and constructing an angle observation matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar;
based on the angle observation vector of the target and the angle observation matrix, the phase delay vectors of different targets are subjected to reconstruction imaging by utilizing an optimization algorithm, so that multi-target super-resolution and angle measurement of the target echo signal are realized.
2. The method of claim 1, wherein the performing one-dimensional range profile constant false alarm detection on the target echo signals of the multiple channels of the monopulse sparse array radar to obtain the array signal of the range unit where each target is located, and constructing the angle observation vector of the target according to the array signal of the range unit where the target is located comprises:
carrying out digital beam synthesis on target echo signals of multiple channels of the monopulse sparse array radar, and carrying out matched filtering on echo data after the digital beam synthesis to obtain target one-dimensional range profile data;
and performing one-dimensional distance CFAR constant false alarm detection on the echo data of each distance unit in the one-dimensional range profile data of the target, detecting the targets at different distances, and obtaining the array signals of the distance units where the targets are located.
And constructing an angle observation vector of the target by using the detected array signals of the target distance units.
3. The method of claim 2, wherein the angular observation vector of the target is represented as:
S(k)＝[s1(k) s2(k) … sP(k)] (1)
in the formula (1), sp(k) The method is characterized in that the k-th target detected by the one-dimensional range profile CFAR is in range unit complex signal data of the P-th channel of the sparse array radar, P is 1, …, P is less than N, and N is the number of the array radar full array channels.
4. The method of claim 2, wherein constructing the angle measurement dictionary matrix using the operating parameters of the sparse array radar system comprises:
and constructing an angle measurement dictionary matrix based on the array radar according to the full-array radar array arrangement, the working wavelength, the number of full-array sub-arrays, the distance between adjacent sub-arrays and the angle measurement resolution interval corresponding to the sparse array radar system. The angle measurement dictionary matrix is represented as an N × M matrix:
Figure FDA0002894648490000021
in the formula (2), the reaction mixture is,
Figure FDA0002894648490000022
is an angle thetamOf the guide vector, thetamAnd (M-N/2) delta theta, wherein M is the number of the space angle partitions, delta theta is the space angle partition interval, d is the distance between two adjacent subarrays of the array radar, and lambda is the radar working frequency.
5. The method of claim 4, wherein the constructing the angle observation matrix by using the angle measurement dictionary matrix and the operating parameters of the sparse array radar comprises:
constructing a corresponding angle observation matrix by using the angle measurement dictionary matrix and the array arrangement position and the sub-array number of the sparse array radar, wherein the angle observation matrix is expressed as follows:
A＝[Φ1 Φ2 … ΦP]T (3)
in formula (3) [ ·]TRepresenting the transpose of the matrix,. phipThe P-th row vector of the angle-measuring dictionary matrix Φ is denoted, P1, …, P < N.
6. The method according to any one of claims 1 to 5, wherein the performing reconstruction imaging on the phase delay vectors of different targets by using an optimization algorithm based on the angle observation vectors of the targets and the angle observation matrix to achieve multi-target super-resolution and angle measurement of the target echo signals comprises:
reconstructing and solving projection coefficients of phase delay vectors of different targets under observation matrix base vectors by using a target angle observation vector and an angle observation matrix of the sparse array radar through an optimization algorithm;
at a minimum of1And (3) solving the following optimization model by using an optimization algorithm according to the angle observation matrix A and the angle observation vector S (k) of the sparse array radar by taking the norm as a criterion:
Figure FDA0002894648490000031
in the formula (4), the reaction mixture is,
Figure FDA0002894648490000032
the target backscattering coefficient estimated value of each angle unit in space and argmin are the minimum value of the taking function according to the obtained optimal solution
Figure FDA0002894648490000033
And distinguishing each target at the kth distance unit to obtain the angle of each target.
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