CN110609271B - Beam sidelobe suppression method based on spatial apodization - Google Patents
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Abstract
The invention discloses a beam sidelobe suppression method based on space apodization, and belongs to the technical field of high-frequency multi-beam sonar signal processing. Firstly, receiving beam domain data after beam forming processing; calculating a weighting coefficient; weighting each beam data; and sequentially arranging all the wave beams and outputting the processed result. According to the invention, through carrying out nonlinear weighting on the beam domain data after beam forming, the left adjacent beam and the right adjacent beam are compared in real time to obtain the minimum energy value as a weighting coefficient, so that the side lobe level of the beam is suppressed, the width of the mainlobe is compressed, the azimuth resolution of a sonar target is effectively improved, and a high-resolution image is obtained; the method can suppress the sidelobe level to 60dB and simultaneously compress the main lobe width, is simple and can be realized in real time.
Description
Technical Field
The invention relates to the technical field of high-frequency multi-beam sonar signal processing, in particular to a beam sidelobe suppression method based on space apodization.
Background
High-frequency multi-beam sonar is an important instrument for detecting marine targets at present, particularly for detecting small targets, is mainly applied to accurate positioning and classification of close-range targets in the sea, and therefore a high-resolution sonogram is needed. However, since the sidelobe caused by the beam forming is too high, the acoustic pattern is blurred, and a plurality of targets cannot be distinguished, the sidelobe suppression processing is required.
There are two main approaches to sidelobe suppression at present: one is windowing weighting processing, which is simple to implement, but the main lobe is widened while side lobes are suppressed, so that the azimuth resolution of an acoustic image is reduced, the acoustic image still cannot distinguish a plurality of adjacent targets, and the targets cannot be accurately positioned and classified; the other method is adaptive weighting processing, which can inhibit side lobes under the condition of not widening the main lobe as much as possible, but has complex calculation method and is not suitable for real-time processing.
Disclosure of Invention
The invention aims to provide a beam sidelobe suppression method based on spatial apodization, which widens a main lobe by using the traditional fixed window function weighting processing, reduces the azimuth resolution of an acoustic image and solves the problem that an adaptive weighting processing method is complex.
In order to solve the technical problem, the invention provides a beam sidelobe suppression method based on spatial apodization, which comprises the following steps:
receiving the beam domain data after the beam forming processing;
calculating a weighting coefficient;
weighting each beam data;
and sequentially arranging all the wave beams and outputting the processed result.
Optionally, the calculating the weighting factor includes:
the windowed image format is represented as:
g ω (m)＝g(m)+ω(m)[g(m-k)+g(m+k)] (1)
wherein m is the sampling point, g ω (m) is an image numerical value of an m-th sampling point after processing, g (m) is an image numerical value of the m-th sampling point before processing, k is the number of sampling points at intervals, 1 is taken from the impulse response image, a specific calculation in the beam domain image is shown in a formula (2), omega (m) is a weighting coefficient, when omega =0, the weighting coefficient corresponds to a rectangular window, the side lobe is highest at the moment, and the main lobe is narrowest in width; when omega =0.5, the side lobe is maximally inhibited and the main lobe width is widened by 2 times corresponding to a Hanning window; when omega =0.43, the method corresponds to a Hamming window, and is a compromise method of main lobe broadening and side lobe suppression;
and (3) corresponding the beam domain data to the impulse response image, and calculating the number k of adjacent beams needing to be compared, wherein the formula is as follows:
k＝bf_num*(λ/N/d)*(180/π/θ) (2)
wherein, bf _ num is the total number of the wave beams after the wave beams are formed, lambda is the wavelength, N is the number of the array elements, d is the interval of the array elements, and theta is the open angle range of the wave beam domain;
calculating a weighting coefficient for each beam data:
the mth filtered output pixel value energy is:
|I w (m)| 2 ＝|I(m)+ω(m)[I(m-k)+I(m+k)]| 2 (3)，
I w (m) is the pixel value energy of the mth sampling point output by filtering, I (m) is the pixel value energy of the mth sampling point before filtering, k is the number of sampling points at intervals, and the value is 1; by solving for
Obtaining unconstrained optimal parameters to minimize equation (3):
ω(m)＝-I(m)/[I(m-k)+I(m+k)] (4)
ω (m) is a weighting coefficient of each beam data.
Optionally, the weighting processing on each beam data includes:
constraining omega (m) to the interval 0,0.5]And (3) processing the output data I ω (m) has the form:
optionally, the sequentially arranging the beams and outputting the processed result includes:
and weighting each beam data to obtain the result after each beam is processed, and sequentially arranging and outputting each beam to obtain beam domain data which inhibits side lobes and compresses the width of a main lobe.
The invention provides a beam sidelobe suppression method based on spatial apodization. Firstly, receiving beam domain data after beam forming processing; calculating a weighting coefficient; weighting each beam data; and sequentially arranging all the wave beams and outputting the processed result. According to the invention, through carrying out nonlinear weighting on the beam domain data after beam forming, the left adjacent beam and the right adjacent beam are compared in real time to obtain the minimum energy value as a weighting coefficient, so that the side lobe level of the beam is suppressed, the width of the mainlobe is compressed, the azimuth resolution of a sonar target is effectively improved, and a high-resolution image is obtained; the method can suppress the sidelobe level to 60dB and simultaneously compress the main lobe width, is simple and can be realized in real time.
Drawings
FIG. 1 is a schematic flow chart of a beam sidelobe suppression method based on spatial apodization according to the present invention;
fig. 2 is a schematic diagram comparing three weighting processes.
Detailed Description
The following describes a beam sidelobe suppression method based on spatial apodization in detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Example one
The invention provides a beam sidelobe suppression method based on spatial apodization, the flow of which is shown in figure 1, comprising the following steps:
receiving the beam domain data after the beam forming processing;
calculating a weighting coefficient;
weighting each beam data;
and sequentially arranging all the wave beams and outputting the processed result.
Specifically, the first step receives data:
receiving the beam domain data after the beam forming processing;
calculating a weighting coefficient:
the traditional windowed image form can be uniformly expressed as:
g ω (m)＝g(m)+ω(m)[g(m-k)+g(m+k)] (1)
wherein m is the sampling point, g ω (m) is an image numerical value of an m-th sampling point after processing, g (m) is an image numerical value of the m-th sampling point before processing, k is the number of sampling points at intervals, 1 is taken from the impulse response image, a specific calculation in the beam domain image is shown in a formula (2), omega (m) is a weighting coefficient, when omega =0, the weighting coefficient corresponds to a rectangular window, the side lobe is highest at the moment, and the main lobe is narrowest in width; omega =0.5 corresponds to Hanning window with side lobesThe main lobe width is widened by 2 times when the maximum suppression is achieved; when omega =0.43, the method corresponds to a Hamming window, and is a compromise method of main lobe broadening and side lobe suppression; the spatial apodization algorithm is to process each pixel point of an original complex image by adopting different omega weighting functions, because the impulse responses of the different weighting functions are different from each other, the weighting results are also different from each other, and then the energy minimum value is selected from the images processed by different weighting as the result.
On the basis of the above, the beam domain data is corresponding to the impulse response image, so the number k of adjacent beams to be compared is first calculated, and the formula is as follows:
k＝bf_num*(λ/N/d)*(180/π/θ) (2)
wherein, bf _ num is the total number of the formed wave beams, λ is the wavelength, N is the number of array elements, d is the interval of the array elements, and θ is the open angle range of the wave beam domain;
the weighting coefficients for each beam data are then calculated:
the mth filtered output pixel value energy is:
|I w (m)| 2 ＝|I(m)+ω(m)[I(m-k)+I(m+k)]| 2 (3)，
I w (m) is the pixel value energy of the mth sampling point output by filtering, I (m) is the pixel value energy of the mth sampling point before filtering, k is the number of sampling points at intervals, and the value is 1; by solving for
Obtaining an unconstrained optimal parameter to minimize equation (3):
ω(m)＝-I(m)/[I(m-k)+I(m+k)] (4)
ω (m) is a weighting coefficient of each beam data.
Step three, weighting treatment is carried out:
after the weighting coefficients are obtained, weighting processing is performed on each beam data. Constraining omega (m) to the interval 0,0.5]And (3) processing the output data I ω (m) has the form:
step four, sending data:
the result of each beam processing is obtained by performing weighting processing on each beam data, and each beam is sequentially arranged and output, so as to obtain beam domain data in which a side lobe is suppressed and a main lobe width is compressed, and the result is shown in fig. 2. In fig. 2, three types of unweighted processing, chebyshev weighting processing and spatial apodization weighting processing are performed, it is obvious that although chebyshev weighting reduces the sidelobe level to 30dB, the mainlobe is widened, and spatial apodization weighting reduces the sidelobe level to 60dB and simultaneously compresses the width of the mainlobe, so that the image obtains higher azimuth resolution and is more beneficial to accurate positioning and classification of small targets.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (3)
1. A beam sidelobe suppression method based on spatial apodization is characterized by comprising the following steps:
receiving the beam domain data after the beam forming processing;
calculating a weighting coefficient;
weighting each beam data;
arranging all the wave beams in sequence and outputting a processed result;
calculating the weighting coefficients includes:
the windowed image form is represented as:
g ω (m)＝g(m)+ω(m)[g(m-k)+g(m+k)] (1)
wherein m is a sampling point, g ω (m) is the image value of the m-th sampling point after processing, g (m) is the image value of the m-th sampling point before processing, k is the number of sampling points apart, 1 is taken in the impulse response image, the specific calculation in the beam domain image is shown in formula (2), ω (m) is the weighting coefficient of each beam data, when ω =0, the weighting coefficient corresponds to a rectangular window, and the method is characterized in thatThe time sidelobe is highest, and the main lobe is narrowest in width; when omega =0.5, the side lobe is maximally inhibited and the main lobe width is widened by 2 times corresponding to a Hanning window; when omega =0.43, the method corresponds to a Hamming window, and is a compromise method of main lobe broadening and side lobe suppression;
and (3) corresponding the beam domain data to the impulse response image, and calculating the number k of adjacent beams needing to be compared, wherein the formula is as follows:
k＝bf_num*(λ/N/d)*(180/π/θ) (2)
wherein, bf _ num is the total number of the formed wave beams, λ is the wavelength, N is the number of array elements, d is the interval of the array elements, and θ is the open angle range of the wave beam domain;
calculating a weighting coefficient for each beam data:
the mth filtered output pixel value energy is:
|I w (m)| 2 ＝|I(m)+ω(m)[I(m-k)+I(m+k)]| 2 (3)，
I w (m) is the pixel value energy of the mth sampling point output by filtering, I (m) is the pixel value energy of the mth sampling point before filtering, k is the number of sampling points at intervals, and the value is 1; by solving for
Obtaining unconstrained optimal parameters to minimize equation (3):
ω(m)＝-I(m)/[I(m-k)+I(m+k)] (4)
ω (m) is a weighting coefficient of each beam data.
2. The method of spatial apodization based beam sidelobe suppression according to claim 1, wherein the weighting processing for each beam data includes:
constraining omega (m) to the interval 0,0.5]And (3) processing the output data I ω (m) has the form:
3. the method of spatial apodization based beam sidelobe suppression according to claim 2, wherein arranging the respective beams in sequence and outputting the processed results comprises:
and weighting each beam data to obtain the result after each beam is processed, and sequentially arranging and outputting each beam to obtain beam domain data which inhibits side lobes and compresses the width of a main lobe.
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