CN112269164A - Weak target positioning method based on interference structure matching processing under deep sea reliable acoustic path - Google Patents
Weak target positioning method based on interference structure matching processing under deep sea reliable acoustic path 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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
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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
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
- G01S15/06—Systems determining the position data of a target
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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
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
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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
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention relates to a weak target positioning method based on interference structure matching processing under a deep sea reliable acoustic path, aiming at the practical problem that when a sound source is passively positioned and received, the multi-path interference characteristic is submerged by noise when the signal-to-noise ratio is low and the sound source depth cannot be accurately estimated by utilizing the multi-path interference characteristic, multi-beam output power spectrums are spliced so as to extract the multi-path interference characteristic submerged by the noise. And according to the phenomenon that propagation delay difference between the sea surface sound source direct wave and the sea surface reflected wave is sensitive along with the change of depth, the characteristic that a matched filter is sensitive to frequency mismatch is utilized, and the power spectrum of the received signal and the copy power spectrum are subjected to matched filtering processing, so that sound source position information is obtained under the condition of low signal-to-noise ratio. By utilizing the weak target passive positioning method based on the interference structure matching processing, the target depth and the horizontal distance can be estimated under the condition of lower signal to noise ratio.
Description
Technical Field
The invention belongs to underwater acoustic array signal processing, and relates to a weak target positioning method based on interference structure matching processing under a deep sea reliable acoustic path.
Background
The deep sea Reliable Acoustic Path (RAP) is a channel of acoustic propagation that is characteristic of deep sea. The RAP condition is that the transducer is located below the critical depth of the deep sea (where the speed of sound is equal to the speed of sound near the sea surface), when an acoustic propagation path from the sea surface to the transducer is formed. This acoustic propagation path is not affected by sea-surface effects or sea-bottom interactions, the propagating signal is stable and reliable, and is therefore called a reliable acoustic path (Rui D, Kun-De Y, Yuan-Liang M, et al. A reliable acoustic path: Physical properties and a source localization method [ J ]. Chinese Physics B,2012,21(12): 124301.).
At present, the existing method places a receiving hydrophone in deep sea and passively detects the underwater target near the sea surface by extracting RAP multipath interference structure characteristics. However, in many cases, the radiation noise level of the target to be detected is low, so that the signal-to-noise ratio at the deep sea receiving hydrophones is low, and the multipath interference characteristics are easily submerged by the noise. At the moment, the underwater target detection problem is changed into a weak target detection problem, and due to the excessively low signal-to-noise ratio, the existing method is difficult to effectively extract the frequency of the interference structure, so that the detection performance is reduced.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides a weak target positioning method based on interference structure matching processing under a deep-sea reliable acoustic path.
Technical scheme
A weak target positioning method based on interference structure matching processing under a deep sea reliable acoustic path is characterized by comprising the following steps:
step 1: arranging a single multi-element vertical array below a critical depth to obtain the frequency domain broadband beam output of the vertical array and obtain the radiation noise of a target acquired by the multi-element vertical array, wherein the length of an acquired time domain signal is 60-600 seconds;
dividing the collected time domain signal into M subsections, and carrying out Fourier transform on each subsection to obtain M subsections frequency spectrums;
Kmfl＝[x1,m fl,x2,m fl,x3,m fl,...,xN,m fl]T
wherein, Km(fl) For each array elementM (M ═ 1, 2.. M) th sub-segment spectrum intermediate frequency point flValue of (a), xnm(fl) Represents the m-th sub-segment frequency spectrum middle frequency point f of the N (N is 1, 2.. N) array elementlThe value of (A) is the number of array elements (N)]TRepresenting a transpose;
the time length range of each subsection is 4-30 seconds, adjacent subsections are overlapped in time, the overlapping ratio range is defined as the time length of the overlapped part divided by the time length of a single subsection, and the range is 0-75%;
performing broadband multi-beam processing on the frequency spectrums of all the subsections by using a linear array frequency domain beam forming method, and performing beam scanning in a pitching direction so as to obtain frequency domain broadband beam output at a plurality of pitching angles;
pm(fl,θ)＝[w(fl,θ)]HKm(fl)
wherein p ism(flAnd theta) represents the beam scanning pitch angle theta and the frequency f in the m-th section of frequency spectrumlBeam output of, w (f)l,θ)＝[1,eikdcosθ,ei2kdcosθ,...,ei(N-1)kdcosθ]TIs a weight vector of conventional beamforming]HRepresenting conjugate transpose, d is the array element spacing, k is 2 pi fl/c0Is wave number, c0Is the reference sound speed used by the beamformer;
then the output spectrum of the mth sub-segment beam with the steering angle theta is:
pm(θ)＝[pm(f1,θ),pm(f2,θ),...,pm(fL,θ)]T，
wherein, L represents the number of frequency points;
multiplying the output points of the multi-section frequency domain broadband wave beams at each pitching angle by the conjugate vectors of the output points to obtain a multi-section power spectrum:
Sm(θ)＝pm(θ)⊙pm *(θ)＝[pm *(f1,θ),pm *(f2,θ),...,pm *(fL,θ)]T，(4)
wherein S ism(θ) indicates an output power spectrum where the scanning pitch angle of the mth sub-segment beam is θ, which indicates a dot product;
splicing the power spectrums of the adjacent time periods to obtain a longer power spectrum, denoted by S (theta), as a power spectrum of a received signal on the beam:
S(θ)＝[p1(θ)T,p2(θ)T,...,pM(θ)T]T；
and uniformly dividing grids in the horizontal distance and depth direction in a detection range, calculating the time delay difference delta tau at the geometric center of the vertical receiving array when a target is positioned on each grid point, and obtaining copy power spectrums of the grid points by selecting the same receiving signals in frequency band ranges:
where f is frequency, H (f, theta, z)a) To assume a target depth zaAngle of pitch thetaaThe copy power spectrum calculated by the time-sound field software,
the step B is used for performing matched filtering processing on the A and outputting the maximum value;
the output result at this grid point is t (z)a,θa) So as to obtain a vertical angle-depth fuzzy map;
taking any depth in the detection range, dividing the horizontal distance in the detection range, and calculating the pitch angle average value theta of the direct wave at the geometric center of the vertical receiving array and the sea surface reflected wave when the target is positioned at each horizontal distance grid through sound field software in an off-line manner, thereby obtaining the corresponding relation between the pitch angle and the horizontal distance:
wherein: thetadRepresenting the pitch angle, theta, of the direct wavesrRepresenting the pitch angle of the sea surface reflection wave;
converting the pitching angle into horizontal depth to obtain a horizontal distance-depth positioning fuzzy map, wherein the output result of each grid point is t (z)a,ra)；
And finally, obtaining a positioning result by searching a peak value:
(rreal,sreal)＝arg{max[t(za,θa)]}
wherein arg { max [, [ alpha ] ]]And represents the output result of the corresponding grid point at the maximum value. r isrealRepresenting the true horizontal distance, srealRepresenting the true depth.
Advantageous effects
The invention provides a weak target positioning method based on interference structure matching processing under a deep sea reliable acoustic path. And aiming at the power spectrum spliced on each wave beam, utilizing sound field software to calculate the power spectrum of the received signals at different depths of the wave beam angle in an off-line manner, and using the simulated power spectrum at different depths to perform matched filtering processing on the power spectrum spliced on the wave beam angle to obtain the maximum matched output value at different depths. And finally, the distribution of the maximum value on the pitch angle-depth is converted into the distribution on the horizontal distance-depth, and the horizontal distance and depth result of the target is obtained by searching the peak value, so that the purpose of positioning the underwater target under the lower signal-to-noise ratio is achieved.
In the invention, because the single multi-element vertical array is arranged below the critical depth, the broadband beam output of the frequency domain of the vertical array is obtained. The number of elements of the vertical array ranges from 8 to 128, the target radiation noise is collected by using the multi-element vertical array, and the length of the collected time domain signal is 60 seconds to 600 seconds. And dividing the acquired time domain signal into a plurality of subsections, wherein the time length range of each subsection is 4-30 seconds, adjacent subsections are overlapped in time, and the overlapping ratio range is defined as the time length of the overlapped part divided by the time length of a single subsection and ranges from 0% to 75%. The vertical array frequency domain broadband beam forming is in the prior art, and Fourier transform is carried out on each sub-segment to obtain a frequency spectrum. And performing broadband multi-beam processing on the frequency spectrums of all the subsections by using a linear array frequency domain beam forming method, and performing beam scanning in the pitching direction so as to obtain frequency domain broadband beam output in a plurality of pitching angles.
And splicing the frequency spectrums of a plurality of subsegments in the frequency domain broadband beam output of the vertical array. And performing modulus squaring treatment on the broadband beam output of the plurality of sub-segment frequency domains in each pitching beam to obtain a corresponding power spectrum. And for each pitching wave beam, splicing the power spectrums of the adjacent subsections end to end, connecting the power spectrums of all the subsections together to obtain a longer power spectrum which is called as a received signal power spectrum on the wave beam.
Then, copy power spectrums of the sound source at different positions are obtained through off-line calculation of sound field software in the prior art, and matched filtering processing is carried out on the copy power spectrums and the received signal power spectrums on each pitching wave beam to obtain a positioning result. Dividing grids in the horizontal distance and depth within the detection range (the depth is 10-500 m, and the horizontal distance is 5-40 km), and calculating a power spectrum at the geometric center of the vertical receiving array when a target is positioned on each grid point through sound field software, wherein the power spectrum is called as a copy power spectrum;
and respectively carrying out matched filtering processing on the power spectrum of the received signal on each wave beam and the copy power spectrum of each grid point, and taking the maximum value of the matched filtering output as the output result on the grid point, thereby obtaining the pitch angle-depth fuzzy graph. And calculating the corresponding relation between the pitch angle and the horizontal distance by using sound field software in an off-line manner, thereby determining the horizontal distance corresponding to each pitch angle. And converting the pitch angle-depth fuzzy map into a horizontal distance-depth positioning fuzzy map, and searching a peak value to obtain a positioning result.
The basic principle and the implementation scheme of the invention are verified by computer numerical simulation, and the result shows that: by utilizing the weak target passive positioning method based on the interference structure matching processing, the target depth and the horizontal distance can be estimated under the condition of lower signal to noise ratio. The method provided by the invention can achieve the aim of effectively estimating the horizontal distance and the depth of the target under the condition of lower signal-to-noise ratio.
Drawings
FIG. 1 is a flow chart of the main steps of the present invention;
FIG. 2 is a schematic diagram of a single vertical array receiving a horizontal motion sound source signal under a reliable sound path;
FIG. 3 is a frequency spectrum of a power spectrum of a received signal within a beam at a pitch angle of 87 degrees;
FIG. 4 is a frequency spectrum of a single segment of the power spectrum within a beam at a pitch angle of 87 degrees;
FIG. 5 is a reliable acoustic path interference structure (signal-to-noise ratio-24 dB in band);
FIG. 6 is a pitch angle-depth positioning ambiguity plot obtained at an in-band signal-to-noise ratio of-24 dB;
FIG. 7 is a schematic diagram of the conversion from sea surface discrete grid points to pitch angle-distance coordinate lines;
FIG. 8 is a horizontal distance-depth localization blur map obtained at-24 dB of in-band signal-to-noise ratio, with crosses indicating the true sound source position;
FIG. 9 is the depth slice of FIG. 8 at a distance of 21km (true distance);
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the technical scheme adopted by the invention for solving the existing problems can be divided into the following steps:
1) and designing an MIMO sonar array type and an orthogonal waveform which can be used for high-resolution three-dimensional forward-looking imaging. The matrix and the orthogonal waveform. The MIMO sonar array type consists of 2 transmitting transducers and M receiving ULAs. Wherein the number M of the ULAs is more than or equal to 3, and the number N of the hydrophones in each ULA is more than or equal to 8. The 2-element ULA consisting of 2 transmitting transducers and the M receiving ULA are parallel to each other, and the perpendicular bisectors coincide with each other. For 2 transmitting transducers, 2 orthogonal waveforms for the MIMO sonar are designed. The 2 orthogonal waveforms have the same frequency band and the same pulse width, the autocorrelation functions of the 2 orthogonal waveforms have the same main lobe, the sidelobe of the autocorrelation function is less than or equal to 0.01 time of the peak value of the main lobe of the autocorrelation function, and the peak value of the cross-correlation function of the 2 orthogonal waveforms is less than or equal to 0.01 time of the peak value of the main lobe of the autocorrelation function.
2) And transmitting and collecting signals according to the designed array type and waveform. The 2 transmitting transducers simultaneously transmit designed orthogonal signals, and M receiving ULAs synchronously acquire echoes of a target scene.
3) And performing horizontal processing. And performing matched filtering processing on the echoes on the M receiving ULAs to obtain M groups of matched filtering outputs, wherein each group comprises 2N matched filtering outputs. And performing multi-beam processing on the 2N matched filtering outputs in each group by adopting conventional beam forming according to a weighting mode of the 2N-element ULA to obtain M groups of horizontal multi-beam outputs, wherein each group comprises Q horizontal beams.
4) And (5) performing vertical processing. The horizontal beam outputs having the same horizontal beam angle are divided into groups, each group having M inputs in total and the number of groups being Q, as inputs to the vertical processing. And processing each group of input by using a high-resolution spatial spectrum estimation algorithm to obtain a vertical imaging result.
5) And combining the horizontal processing result and the vertical processing result to obtain a final high-resolution three-dimensional foresight imaging result.
Each step of the present invention is described in detail below:
step 1) mainly relates to conventional beam forming of a vertical array frequency domain. The specific contents are as follows:
the multi-element vertical receiving array collects target radiation noise, the collected signal is divided into M subsections according to time, Fourier transform is carried out on each subsection, and M subsections frequency spectrums are obtained and expressed as follows:
Kmfl＝[x1,m fl,x2,m fl,x3,m fl,...,xN,m fl]T (1)
wherein, Km(fl) M (M is 1, 2.. M) th sub-segment frequency spectrum middle frequency point f of each array elementlValue of (a), xnm(fl) Represents the m-th sub-segment frequency spectrum middle frequency point f of the N (N is 1, 2.. N) array elementlThe value of (A) is the number of array elements (N)]TIndicating transposition.
And performing frequency domain conventional broadband beam forming on a plurality of sections of frequency spectrums on the vertical array by using a linear array frequency domain beam forming method. The beam scanning is performed in elevation directions to obtain frequency domain broadband beam outputs at a plurality of elevation angles. The beam output can be expressed as:
pm(fl,θ)＝[w(fl,θ)]HKm(fl) (2)
wherein p ism(flAnd theta) represents the beam scanning pitch angle theta and the frequency f in the m-th section of frequency spectrumlBeam output of, w (f)l,θ)＝[1,eikdcosθ,ei2kdcosθ,...,ei(N-1)kdcosθ]TIs a weight vector of conventional beamforming]HRepresenting conjugate transpose, d is the array element spacing, k is 2 pi fl/c0Is wave number, c0Is the reference sound speed used by the beamformer.
The output spectrum of the mth subband beam with steering angle θ can be expressed as:
pm(θ)＝[pm(f1,θ),pm(f2,θ),...,pm(fL,θ)]T，
where L represents the number of frequency points.
And step 2) mainly relates to splicing processing of the broadband beam output of the vertical array frequency domain. The specific contents are as follows:
after obtaining multiple sections of frequency spectrums in the step 1), multiplying output points of the multiple sections of frequency domain broadband wave beams at each pitching angle by the conjugate vectors of the output points to obtain multiple sections of power spectrums.
Sm(θ)＝pm(θ)⊙pm *(θ)＝[pm *(f1,θ),pm *(f2,θ),...,pm *(fL,θ)]T，(4)
Wherein S ism(θ) indicates an output power spectrum where the m-th sub-segment beam scanning pitch angle is θ, which indicates a dot product. And splicing a plurality of power spectrums of adjacent time periods, thereby obtaining a longer power spectrum. Expressed as S (θ):
S(θ)＝[p1(θ)T,p2(θ)T,...,pM(θ)T]T， (5)
which is referred to as the received signal power spectrum on that beam.
Step 3) mainly relates to modeling of the copy power spectrum of each grid point and matched filtering processing of the power spectrum of the received signal on each beam and the copy power spectrum, and the specific contents are as follows:
and uniformly dividing the grids in the horizontal distance and depth direction in the detection range. Consider the coherent superposition of direct waves and sea surface reflected waves. The ideal received power spectrum is:
H(f)＝|h(f)|2(1-ej(2πΔτ)f)， (6)
wherein f is frequency, H (f) is ideal received power spectrum, h (f) is received signal spectrum, and Delta tau is time delay difference between direct wave and sea surface reflected wave. And (3) calculating the time delay difference delta tau at the geometric center of the vertical receiving array when the target is positioned on each grid point through sound field software, selecting the same receiving signals in the frequency band range, and substituting the same receiving signals into the formula (6) to obtain the copy power spectrum of each grid point.
The cost function is:
wherein H (f, theta, z)a) To assume a target depth zaAngle of pitch thetaaThe copy power spectrum calculated by the time-sound field software,
which means that a is matched filtered using B and the maximum value is output. The output result at this grid point is t (z)a,θa) Thereby obtaining a vertical angle-depth blur map.
And 4) mainly calculating the corresponding relation between the pitch angle and the horizontal distance by using sound field software in an off-line manner, converting the pitch angle-depth fuzzy graph into a horizontal distance-depth positioning fuzzy graph, and searching a peak value to obtain a positioning result. The specific contents are as follows:
taking any depth in the detection range, dividing the horizontal distance in the detection range, and calculating the pitch angle average value theta of the direct wave at the geometric center of the vertical receiving array and the sea surface reflected wave when the target is positioned at each horizontal distance grid through sound field software in an off-line manner, thereby obtaining the corresponding relation between the pitch angle and the horizontal distance, namely:
wherein theta isdRepresenting the pitch angle, theta, of the direct wavesrRepresenting the pitch angle of the sea surface reflection.
According to the corresponding relation between the pitching angle and the horizontal distance, the pitching angle is converted into the horizontal depth, so that a horizontal distance-depth positioning fuzzy graph is obtained, and the output result of each grid point is t (z)a,ra). Finally, obtaining a positioning result by searching a peak value, namely:
(rreal,sreal)＝arg{max[t(za,θa)]}。 (9)
wherein arg { max [, [ alpha ] ]]And represents the output result of the corresponding grid point at the maximum value. r isrealRepresenting the true horizontal distance, srealRepresenting the true depth.
The positioning result of the method provided by the invention is given through computer numerical simulation, and the flow chart of the main steps of the method is shown in figure 1.
The specific implementation example is as follows:
taking a typical deep sea environment as an example, the implementation example of the invention is given. The implementation example uses a computer to perform numerical simulation to check the effect of the method of the present invention.
1) RAP Environment
Assuming a sea depth of 5000 meters, the acoustic velocity profile is the MUNK profile, and the critical depth is 3700 meters.
2) Transducer parameters
The receiving array is a uniform vertical linear array, the depth of the array elements is 4200-4246.5 meters, the spacing of the array elements is 1.5 meters, and the number of the array elements is 32.
3) Simulated transmission signal and received signal
Assuming that the depth of a target sound source is 100 meters, and the distance moves horizontally from 21 kilometers to 22 kilometers, a schematic diagram of a horizontal motion sound source signal received by a vertical array under a reliable sound path is shown in figure (1). The frequency band range of the underwater target radiation signal is assumed to be 200Hz-500 Hz. The signal is obtained from white gaussian noise through a band-pass filter during simulation. The in-band received signal-to-noise ratio on a single hydrophone was set to-24 dB. And obtaining a transfer function according to sound field software to obtain a receiving signal.
And acquiring a section of received signals by using the hydrophone array, and processing the section of signals to obtain a positioning result. Received signal length, number of sub-segments, overlap ratio of sub-segments, length of sub-segments
The received signal is divided into 31 sub-segments in the time domain and Fourier transform is performed to obtain 31 sub-segment frequency spectrums. And performing frequency domain conventional broadband beam forming on a plurality of sections of frequency spectrums on the vertical array by using a linear array frequency domain beam forming method. The beam scanning is carried out in the elevation direction, the scanning angle is 70-96 degrees, the scanning interval is 1 degree, and the frequency domain broadband beam output at 27 elevation angles is obtained. And processing the power spectrum to obtain a frequency domain broadband beam output power spectrum. The elevation angle is 87 degrees, and the frequency spectrum of the output power spectrum of the 11 th section of frequency domain broadband beam is shown in a graph (4). The structure of the reliable acoustic path interference when noise is drowned is shown in fig. 5.
4) Received signal power spectrum formed by simulation splicing
And splicing the 31 sections of output power spectrums at each pitch angle in sequence to obtain 27 long received signal power spectrums. The spectrum of the 18 th (pitch angle 87 degrees) output power spectrum is shown in fig. 3, and the abscissa at the box represents the delay difference between the sea surface reflected wave and the direct wave.
5) Simulated copy power spectrum
The horizontal distance is set to be 10-35 kilometers, the depth is set to be 10-400 meters, and grids are uniformly divided on the horizontal distance and the depth. And (3) calculating the delay difference between the direct wave and the sea surface reflected wave when the sound source is positioned at each grid point by using Bellhop software in an off-line manner, setting RAP environmental parameters in the software, calculating the delay difference of each grid point, obtaining the delay difference, obtaining the copy power spectrum of each grid point by using the formula (6), and selecting the frequency band range of 200-500 Hz.
6) On-line matched filtering processing for actual received signal power spectrum
And respectively carrying out matched filtering processing on the power spectrum of the received signal at each pitch angle and the copy power spectrum of each grid point, and taking the maximum value of the matched filtering output as the output result of the grid point, thereby obtaining an angle-depth fuzzy graph. As shown in fig. 6.
7) Calculating the corresponding relation between the pitch angle and the horizontal distance off line, and converting on line
The sound field software is used for calculating the corresponding relation between the pitch angle and the horizontal distance in an off-line manner, and the corresponding relation is shown in a figure (7). Thereby determining the horizontal distance corresponding to each pitch angle. And (3) converting the pitch angle-depth fuzzy map into a horizontal distance-depth positioning fuzzy map, wherein the conversion result is shown in a figure (8).
8) Peak search obtains positioning result
The peak search was performed on the graph (8) to obtain a sound source distance of 20.8km and a depth of 100m.
According to the implementation example, the weak target positioning method based on the interference structure matching processing under the reliable acoustic path can extract the time delay information submerged by noise, improve the positioning performance of the weak target under the low signal-to-noise ratio, and estimate the depth and the horizontal distance of the target under the lower signal-to-noise ratio.
The invention splices the multi-path output power spectrums so as to extract the multi-path interference characteristics submerged by noise, aiming at the practical problem that the multi-path interference characteristics are submerged by the noise when the passive positioning receiving signal-to-noise ratio of the sound source is low and the sound source depth can not be accurately estimated by using the multi-path interference characteristics. And according to the phenomenon that propagation delay difference between the sea surface sound source direct wave and the sea surface reflected wave is sensitive along with the change of depth, the characteristic that a matched filter is sensitive to frequency mismatch is utilized, and the power spectrum of the received signal and the copy power spectrum are subjected to matched filtering processing, so that sound source position information is obtained under the condition of low signal-to-noise ratio.
Claims (1)
1. A weak target positioning method based on interference structure matching processing under a deep sea reliable acoustic path is characterized by comprising the following steps:
step 1: arranging a single multi-element vertical array below a critical depth to obtain the frequency domain broadband beam output of the vertical array and obtain the radiation noise of a target acquired by the multi-element vertical array, wherein the length of an acquired time domain signal is 60-600 seconds;
dividing the collected time domain signal into M subsections, and carrying out Fourier transform on each subsection to obtain M subsections frequency spectrums;
Km fl＝[x1,m fl,x2,m fl,x3,m fl,...,xN,m fl]T
wherein, Km(fl) M (M is 1, 2.. M) th sub-segment frequency spectrum middle frequency point f of each array elementlValue of (a), xnm(fl) Represents the m-th sub-segment frequency spectrum middle frequency point f of the N (N is 1, 2.. N) array elementlThe value of (A) is the number of array elements (N)]TRepresenting a transpose;
the time length range of each subsection is 4-30 seconds, adjacent subsections are overlapped in time, the overlapping ratio range is defined as the time length of the overlapped part divided by the time length of a single subsection, and the range is 0-75%;
performing broadband multi-beam processing on the frequency spectrums of all the subsections by using a linear array frequency domain beam forming method, and performing beam scanning in a pitching direction so as to obtain frequency domain broadband beam output at a plurality of pitching angles;
pm(fl,θ)＝[w(fl,θ)]HKm(fl)
wherein p ism(flAnd theta) represents the beam scanning pitch angle theta and the frequency f in the m-th section of frequency spectrumlBeam output of, w (f)l,θ)＝[1,eikdcosθ,ei2kdcosθ,...,ei(N-1)kdcosθ]TIs a weight vector of conventional beamforming]HRepresenting conjugate transpose, d is the array element spacing, k is 2 pi fl/c0Is wave number, c0Is the reference sound speed used by the beamformer;
then the output spectrum of the mth sub-segment beam with the steering angle theta is:
pm(θ)＝[pm(f1,θ),pm(f2,θ),...,pm(fL,θ)]T，
wherein, L represents the number of frequency points;
step 2, splicing the output of the broadband wave beam of the vertical array frequency domain:
multiplying the output points of the multi-section frequency domain broadband wave beams at each pitching angle by the conjugate vectors of the output points to obtain a multi-section power spectrum:
Sm(θ)＝pm(θ)⊙pm *(θ)＝[pm *(f1,θ),pm *(f2,θ),...,pm *(fL,θ)]T，(4)
wherein S ism(θ) indicates an output power spectrum where the scanning pitch angle of the mth sub-segment beam is θ, which indicates a dot product;
splicing the power spectrums of the adjacent time periods to obtain a longer power spectrum, denoted by S (theta), as a power spectrum of a received signal on the beam:
S(θ)＝[p1(θ)T,p2(θ)T,...,pM(θ)T]T；
step 3, modeling of copy power spectrums of grid points, and matched filtering processing of power spectrums of received signals on beams and the copy power spectrums:
and uniformly dividing grids in the horizontal distance and depth direction in a detection range, calculating the time delay difference delta tau at the geometric center of the vertical receiving array when a target is positioned on each grid point, and obtaining copy power spectrums of the grid points by selecting the same receiving signals in frequency band ranges:
where f is frequency, H (f, theta, z)a) To assume a target depth zaAngle of pitch thetaaThe copy power spectrum calculated by the time-sound field software,
the step B is used for performing matched filtering processing on the A and outputting the maximum value;
the output result at this grid point is t (z)a,θa) So as to obtain a vertical angle-depth fuzzy map;
step 4, utilizing sound field software to calculate the corresponding relation between the pitch angle and the horizontal distance in an off-line manner, converting the pitch angle-depth fuzzy graph into a horizontal distance-depth positioning fuzzy graph, searching a peak value to obtain a positioning result:
taking any depth in the detection range, dividing the horizontal distance in the detection range, and calculating the pitch angle average value theta of the direct wave at the geometric center of the vertical receiving array and the sea surface reflected wave when the target is positioned at each horizontal distance grid through sound field software in an off-line manner, thereby obtaining the corresponding relation between the pitch angle and the horizontal distance:
wherein: thetadRepresenting the pitch angle, theta, of the direct wavesrRepresenting the pitch angle of the sea surface reflection wave;
converting the pitch angle into horizontal depth to obtain a horizontal distance-depth positioning fuzzy map, each of whichThe grid point output result is t (z)a,ra)；
And finally, obtaining a positioning result by searching a peak value:
(rreal,sreal)＝arg{max[t(za,θa)]}
wherein arg { max [, [ alpha ] ]]And represents the output result of the corresponding grid point at the maximum value. r isrealRepresenting the true horizontal distance, srealRepresenting the true depth.
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