Anti-interference broadband beam forming algorithm suitable for underwater acoustic communication signals

Abstract

The invention provides an anti-interference broadband beam forming algorithm suitable for an underwater acoustic communication signal, and the algorithm comprises the steps: determining parameter data of a transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and processing the parameter data to obtain a spatial orientation set corresponding to the transmitted linear frequency modulation signal; performing discrete Fourier transform on data received by each array element in the spatial orientation set to obtain frequency domain data, and converting the frequency domain data to obtain a vector form; obtaining narrowband beam output according to the narrowband signal in the vector form, calculating according to the narrowband beam output and the parameter data to obtain time domain signal output, and obtaining a spatial spectrum value based on the time domain signal output; and repeating the step 3 to traverse the spatial spectrum values in all orientations in the spatial orientation set to obtain a spatial orientation spectrogram of the beam forming algorithm. The invention not only improves the anti-interference capability, but also provides an efficient beam forming method which can be realized in real time in practical engineering, and has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater acoustic communication signal processing, and particularly relates to an anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals. Background Art

[0002] Array signal processing technology has a wide range of applications in multiple fields such as radar, sonar, wireless communication, and medical imaging. As the earliest application scenario of array signal processing technology, the concept of phased array radar was proposed during World War I and was actually applied during World War II. Similarly, the application of array signal processing in sonar systems is also very extensive and has many similarities with radar systems. The difference is that due to the complex propagation characteristics of sound waves in water, especially in an environment with severe multipath interference, the challenges faced by array signal processing in sonar systems are more complex than those in radar. The multipath effect in the underwater environment has a significant impact on the processing of array signals, seriously affecting the signal quality and processing accuracy.

[0003] The Conventional Beamformer (CBF) method realizes the spatial filtering effect by simply performing delay summation operations on the signals of each channel. This method has advantages such as low computational complexity, simple implementation, and strong robustness, so it is still widely used in engineering practice. However, the spatial resolution of the conventional beamforming method is relatively low, the interference suppression effect is limited, and it cannot effectively cope with interference signals in complex environments.

[0004] In contrast, the Minimum Variance Distortionless Response Beamformer (MVDR) can minimize the output power of the array while ensuring the distortionless output of the signal in the direction of interest, maximizing the signal-to-noise ratio, and has strong interference suppression ability and higher direction resolution. However, the MVDR algorithm is sensitive to noise, has a large computational amount, and is difficult to meet the real-time requirement, especially difficult to implement in embedded systems or environments with limited resources. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies of the existing technology, which have high computational complexity and are difficult to implement in real time, and provides an anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals. Based on the broadband conventional beamforming method, the computational amount is between the conventional broadband beamforming (CBF) and the Minimum Variance Distortionless Response (MVDR) algorithm. It is slightly larger than the conventional beamforming, but significantly smaller than the MVDR algorithm. Thus, without sacrificing the processing performance, the computational complexity is reduced, making it suitable for real-time applications.

[0006] To achieve the above object, the technical solution provided by the present invention is as follows:

[0007] An anti-jamming broadband beamforming algorithm applicable to underwater acoustic communication signals, comprising:

[0008] Step 1: Determine the parameter data of the transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and process the parameter data to obtain the spatial azimuth set corresponding to the transmitted linear frequency modulation signal;

[0009] Step 2: Perform discrete Fourier transform on the data received by each array element in the spatial azimuth set to obtain frequency-domain data, and convert the frequency-domain data into a vector form;

[0010] Step 3: Obtain a narrowband beam output according to the narrowband signal in the vector form, calculate a time-domain signal output according to the narrowband beam output and the parameter data, and obtain a spatial spectrum value based on the time-domain signal output;

[0011] Step 4: Repeat Step 3 to traverse the spatial spectrum values at all azimuths in the spatial azimuth set to obtain the spatial azimuth spectrum diagram of the beamforming algorithm.

[0012] As a further improvement of the present invention, Step 1 includes:

[0013] Determine the frequency band B, pulse width T, and time-domain transmitted signal copy s(t) in the parameter information of the transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and pad the data points in the parameter information with 0s to L points, where L is an integer power of 2;

[0014] Determine the scanning spatial azimuth set Θ, and for a uniform linear array, take θ∈Θ = [-90°, 90°];

[0015] Perform L-point discrete Fourier transform on the time-domain transmitted signal copy to obtain a frequency-domain signal copy.

[0016] As a further improvement of the present invention, the calculation expression of the transmitted synchronization signal in the underwater acoustic communication signal is:

[0017]

[0018] In formula (1), s(t) represents the time-domain transmitted signal copy, A represents the amplitude, j represents the imaginary number, j 2 =-1, f 0 represents the carrier frequency, k represents the frequency modulation slope, B represents the frequency modulation bandwidth, and T represents the signal duration;

[0019] Perform L-point DFT on the time-domain transmitted signal copy s(t) to obtain the frequency-domain signal copy S(k).

[0020] As a further improvement of the present invention, step two includes:

[0021] Segment the data received by each element in the spatial azimuth concentration respectively to obtain segmented data corresponding to each segment;

[0022] Perform discrete Fourier transform on the segmented data to obtain corresponding frequency-domain data;

[0023] Convert the frequency-domain data into a vector form, where each sub-band corresponds to a set of narrow-band data in the vector form.

[0024] As a further improvement of the present invention, in step two:

[0025] Segment the data x m (i), m = 1, …, M received by each element in the spatial azimuth concentration respectively, and the length of each segment of data is L; among them, the nth segment of received data of the mth element is expressed as:

[0026]

[0027] In formula (2), represents the time-domain data of this segment of data, and L - 1 represents the latest time sample in this segment of data;

[0028] Perform DFT on the segmented data corresponding to each element to obtain corresponding frequency-domain data. The nth segment of received data of the mth element is expressed as the sequence number of each sub-band within the corresponding frequency band.

[0029] As a further improvement of the present invention, step three includes:

[0030] Based on the vector form, each set of the narrow-band data corresponds to a set of narrow-band signals. Perform narrow-band beamforming on the narrow-band signals respectively to obtain beam outputs of each sub-band;

[0031] Multiply the beam outputs of each sub-band with the frequency-domain transmitted signal replica in the parameter data in the frequency domain and perform inverse discrete Fourier transform to obtain a time-domain signal output;

[0032] Take the absolute value of the time-domain signal output first and then take the maximum value to obtain a spatial spectrum value.

[0033] As a further improvement of the present invention, in step three:

[0034] Write the frequency-domain data of each element in a vector form. Each sub-band corresponds to a set of narrow-band data, and the expression is:

[0035]

[0036] In formula (3), X (n) (k) represents the frequency-domain data matrix, represents the frequency-domain data vector corresponding to this section of the m array elements;

[0037] Each sub-band corresponds to a weighting value w m (f k )(m = 1,..., M, k = 0,..., L - 1), and complex weighting summation is performed on each sub-band;

[0038] Each sub-band corresponds to a group of narrowband signals, and narrowband beamforming is performed respectively to obtain the beam output of each sub-band. The expression is:

[0039]

[0040] In formula (4), Y (n) (k) represents the frequency-domain beam output result, w H (f k ) represents the weighting value matrix, X (n) (k) represents the frequency-domain data matrix, represents the conjugate of the weighting value vector, represents the frequency-domain data vector of each array element;

[0041] Multiply the beam output of each sub-band with the signal replica in the frequency domain. The expression is:

[0042] Z (n) (k) = Y (n) (k)S * (k), k = 0,..., L - 1 Formula (5)

[0043] In formula (5), Z (n) (k) represents the inverse discrete Fourier transform, Y (n) (k) represents the frequency-domain beam output result, S * (k) represents the frequency-domain replica of the transmitted signal;

[0044] Perform IDFT on the inverse discrete Fourier transform Z (n) (k) to obtain the time-domain signal output. The expression is:

[0045] z (n) (l), (l = 0,..., L - 1) = IDFT[Z (n) (k), (k = 0,..., L - 1)] Formula (6)

[0046] In formula (6), z (n) (l) represents the time-domain signal output, Z (n) (k) represents the inverse discrete Fourier transform;

[0047] Take z (n) (l) Take the absolute value and the maximum value to obtain the spatial spectrum values at the azimuth θ corresponding to each sub-band.

[0048] As a further improvement of the present invention, in the fourth step, repeat the third step to traverse all azimuths θ ∈ Θ in the spatial azimuth set, and obtain the spatial azimuth spectrum diagram of the proposed beamforming algorithm.

[0049] The advantages of the present invention are as follows:

[0050] 1. Through the multiplication operation of the frequency-domain received data matrix, the present invention significantly reduces the computational complexity. Compared with the conventional wideband beamforming algorithm, the present invention only slightly increases the computational burden, and its computational complexity is much lower than that of the minimum variance distortionless response (MVDR) algorithm, thus greatly improving the feasibility of engineering implementation.

[0051] 2. By multiplying the beam-domain signal of the wideband beamforming with the signal frequency-domain replica, the present invention enables the beam output to achieve the best correlation between the actual arrival azimuth of the signal and the signal replica. By calculating the correlation peak, an accurate azimuth spectrum can be obtained.

[0052] 3. Compared with the traditional wideband beamforming algorithm, the present invention has significant advantages in anti-interference ability. It can effectively suppress the multipath effect and noise interference. Especially in the complex underwater environment, it can enhance the signal-to-noise ratio of the system and ensure the reliable detection of the target signal and the communication quality.

[0053] 4. By optimizing the frequency-domain processing and beamforming strategy, the present invention provides an efficient and low-complexity solution for underwater acoustic communication systems, which is suitable for resource-limited embedded platforms and can achieve reliable target detection and communication in the underwater multipath propagation environment.

[0054] 5. The present invention not only improves the anti-interference ability, but also provides an efficient and real-time implementable beamforming method in practical engineering, and has broad application prospects.

[0055] The additional aspects and advantages of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present invention will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, wherein:

[0057] Figure 1 : Flowchart of an anti-interference wideband beamforming algorithm applicable to underwater acoustic communication signals provided by the present invention;

[0058] Figure 2: Flowchart of the principle of the beamforming algorithm provided by the present invention;

[0059] Figure 3 : UUV side array received data model provided by the present invention;

[0060] Figure 4 : Autocorrelation diagram of the LFM signal provided by the present invention;

[0061] Figure 5 : Ambiguity function diagram of the LFM signal provided by the present invention;

[0062] Figure 6 : Experimental result diagram of the detection ability of the AWBF, CBF, and MVDR algorithms for processing target reflected echoes provided by the present invention;

[0063] Figure 7 : Experimental result diagram of the experiment on the mean square error of the target echo azimuth estimation of the AWBF, CBF, and MVDR algorithms provided by the present invention;

[0064] Figure 8 : Scanning spatial spectrum of the AWBF, CBF, and MVDR algorithms provided by the present invention under single-frequency interference addition;

[0065] Figure 9 : Scanning spatial spectrum of the AWBF, CBF, and MVDR algorithms provided by the present invention under broadband noise addition. Detailed implementation manners

[0066] The following details the embodiments of the present invention. The embodiments are exemplary and are intended to explain the present invention, and should not be construed as a limitation to the present invention.

[0067] Please refer to Figure 1 , the embodiment of the present invention provides an anti-interference broadband beamforming algorithm applicable to underwater acoustic communication signals, including the following steps 1 to 4:

[0068] Step 1: Determine the parameter data of the transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and process the parameter data to obtain the spatial azimuth set corresponding to the transmitted linear frequency modulation signal.

[0069] Step 1 of the embodiment of the present invention includes: determining the parameter information of the transmitted linear frequency modulation signal according to the underwater acoustic communication signal, where the parameter information includes the frequency band B, the pulse width T, and the time-domain transmitted signal copy s(t) saved, and padding the number of data points in the parameter information to L points, where L is an integer power of 2;

[0070] Determine the scanning spatial azimuth set Θ. For a uniform linear array, take θ ∈ Θ = [-90, 90];

[0071] Perform an L-point discrete Fourier transform on the time-domain transmitted signal copy to obtain the frequency-domain signal copy.

[0072] Specifically, the calculation expression of the transmitted synchronization signal (i.e., the complex exponential form of the LFM signal) in the above underwater acoustic communication signal in the embodiments of the present invention is:

[0073]

[0074] In formula (1), s(t) represents the time-domain transmitted signal copy, A represents the amplitude, j represents the imaginary unit, and j 2 = -1, f 0 represents the carrier frequency, k represents the frequency modulation slope, B represents the frequency modulation bandwidth, and T represents the signal duration.

[0075] Perform an L-point discrete Fourier transform DFT on the time-domain transmitted signal copy s(t) to obtain the frequency-domain signal copy S(k). Specifically, perform a DFT transformation on the time-domain transmitted signal copy s(t) to the frequency domain, obtain the frequency-domain signal copy S(k), and save it. The calculation expression is:

[0076] S(k), (k = 0, …, L - 1) = DFT[s(n), (n = 0, …, L - 1)]

[0077] That is:

[0078]

[0079] Take the conjugate of S(k) to obtain S * (k) = conj[S(k)].

[0080] More specifically, for the above parameter data in step one of the embodiments of the present invention, such as Figure 3 shown in the UUV side array receiving data model, the array configuration implemented according to the receiving data model and the simulation signal parameters adopted. The linear frequency modulation signal in the embodiments of the present invention is a commonly used broadband detection signal. As Figure 4 and Figure 5 shown, the LMF signal has strong autocorrelation performance, and the ambiguity function of the LMF signal is approximately thumbtack-shaped and takes into account both time delay and frequency resolution. Therefore, the embodiments of the present invention select the linear frequency modulation signal as the system detection signal.

[0081] Step 2: Perform a discrete Fourier transform on the data received by each element in the spatial azimuth concentration to obtain frequency-domain data, and convert the frequency-domain data into a vector form.

[0082] Step 2 of the embodiments of the present invention includes:

[0083] (1) Segment the data received by each array element in the spatial azimuth concentration respectively to obtain the segmented data corresponding to each segment; specifically, the data in the embodiments of the present invention includes the array reception data, and the array reception data consists of a desired signal and noise. Assume that the reception data of the m-th array element (m = 1, …, M) in the array reception data is x m (i), the received desired signal is s m (i), and the received noise is n m (i), then x m (i) = s m (i) + n m (i). Next, send the data of each array element into the cache, and then segment the cache data and perform DFT processing. First, segment the cache data. The length of the data block is L. Number the data blocks to be processed. n represents the serial number of the data block to be processed, n = 1, 2, …, N, and n = N represents the latest data block.

[0084] More specifically, in the embodiments of the present invention, the data x m (i) received by each array element in the spatial azimuth concentration is segmented respectively, and the length of each segment of data is L; among them, the n-th segment of reception data of the m-th array element is expressed as:

[0085]

[0086] In formula (2), represents the time-domain data of this segment of data, and L - 1 represents the latest time sample in this segment of data.

[0087] (2) Perform discrete Fourier transform DFT on the segmented data corresponding to each array element to obtain the corresponding frequency-domain data; specifically, perform DFT on the segmented data corresponding to each array element to obtain the corresponding frequency-domain data. The n-th segment of reception data of the m-th array element is expressed as The serial number of each band in the corresponding frequency band.

[0088] More specifically, the array reception data consists of a desired signal and noise. Assume that the reception data of the m-th array element (m = 1, …, M) of the array is x m (i), the received desired signal is s m (i), and the received noise is n m (i), then x m (i) = s m (i) + n m (i).

[0089] In the embodiments of the present invention, the data of each array element is also sent to a cache, and then the cached data is segmented and DFT processed. First, the cached data is segmented, the length of the data block is L, and the data blocks to be processed are numbered. n represents the sequence number of the data block to be processed, n = 1, 2, …, N, and n = N represents the latest data block. In the embodiments of the present invention, the segmented data of each array element is subjected to an L-point DFT to obtain the corresponding frequency-domain data:

[0090]

[0091] In the formula, represents the frequency-domain data matrix, represents the frequency-domain data vector corresponding to this segment of the m-th array element, and L-1 represents the latest time sample in this segment of data;

[0092] That is: In the formula, k (k = 0, …, L-1) corresponds to the sequence number of each sub-band within the frequency band, and the corresponding frequency is:

[0093] (3) Convert the frequency-domain data into a vector form, where each sub-band corresponds to a set of narrowband data in the vector form. The expression is:

[0094]

[0095] In the formula, X (n) (k) represents the frequency-domain data matrix, represents the column vector of the frequency-domain data corresponding to this segment of the m-th array element.

[0096] Each sub-band f k corresponds to a set of weighting values w m (f k )(m = 1, …, M, k = 0, …, L-1). The narrowband data of each sub-band is weighted and summed, and the weighting values are represented in vector form. The expression is:

[0097] w(f k ) = [w 1 (f k ), w 2 (f k ), …, w M (f k )] T , k = 0, …, L-1

[0098] In the formula, w(f k ) represents the weighting value vector, w m (f k ), m = 1, …, M; k = 0, L-1 represents the weighting value corresponding to the frequency component f k of the m-th array element.

[0099] Step 3: Obtain the narrowband beam output based on the narrowband signal in vector form, calculate the time-domain signal output based on the narrowband beam output and the parameter data, and obtain the spatial spectrum value based on the time-domain signal output.

[0100] Specifically, Step 3 of the embodiment of the present invention includes:

[0101] (1) Based on the vector form, each group of narrowband data corresponds to a group of narrowband signals;

[0102] More specifically, write the frequency-domain data of each array element in vector form. Each sub-band corresponds to a group of narrowband data, and the expression is:

[0103]

[0104] In formula (3), X (n) (k) represents the frequency-domain data matrix, represents the frequency-domain data vector corresponding to this section of the m array element;

[0105] Each sub-band corresponds to a weighting value w m (f k )(m = 1, …, M, k = 0, …, L - 1). Perform complex weighting summation on each sub-band; the weighting value is expressed in vector form as: w(f k ) = [w 1 (f k ), w 2 (f k ), …, w M (f k )] T , k = 0, …, L - 1.

[0106] Each sub-band corresponds to a group of narrowband signals. Perform narrowband beamforming respectively to obtain the beam output of each sub-band. The expression is:

[0107]

[0108] In formula (4), Y (n) (k) represents the frequency-domain beam output result, w H (f k ) represents the weighting value matrix, X (n) (k) represents the frequency-domain data matrix, represents the conjugate of the weighting value vector, represents the frequency-domain data vector of each array element;

[0109] (2) Perform narrowband beamforming on the narrowband signals respectively to obtain the beam output of each sub-band. The expression is:

[0110]

[0111] (3) Multiply the beam output Y of each sub - band (n) (k) with the frequency - domain transmitted signal replica S * (k) in the frequency domain and perform an inverse discrete Fourier transform to obtain the time - domain signal output;

[0112] For the time - domain signal output z (n) (l), first take the absolute value and then take the maximum value to obtain the spatial spectrum value.

[0113] Specifically, in step three of the present invention:

[0114] Multiply the beam output of each sub - band with the signal replica in the frequency domain. The expression is:

[0115] Z (n) (k) = Y (n) (k)S * (k), k = 0, …, L - 1 Formula (5)

[0116] In formula (5), Z (n) (k) represents the inverse discrete Fourier transform, Y (n) (k) represents the frequency - domain beam output result, S * (k) represents the frequency - domain replica of the transmitted signal;

[0117] (4) Perform IDFT on the inverse discrete Fourier transform Z (n) (k) to obtain the time - domain signal output. The expression is:

[0118] z (n) (l), (l = 0, …, L - 1) = IDFT[Z (n) (k), (k = 0, …, L - 1)] Formula (6)

[0119] In formula (6), z (n) (l) represents the time - domain signal output, Z (n) (k) represents the inverse discrete Fourier transform;

[0120] (5) Take the absolute value of z (n) (l) and take the maximum value to obtain the spatial spectrum value corresponding to the azimuth θ of each sub - band. More specifically: In the above - mentioned embodiment of the present invention, performing IDFT transformation of Z (n) (k) to the time domain:

[0121] z (n) (l), (l = 0, …, L - 1) = IDFT[Z (n) (k), (k = 0, …, L - 1)]

[0122] That is:

[0123] Step 4: Repeat Step 3 to traverse the spatial spectrum values at all azimuths in the spatial azimuth set, and obtain the spatial azimuth spectrum diagram of the beamforming algorithm. Specifically, in the embodiment of the present invention, Step 4 is: repeat Step 3 to traverse all azimuths θ∈Θ in the spatial azimuth set, and obtain the spatial azimuth spectrum diagram of the proposed beamforming algorithm.

[0124] The embodiment of the present invention is significantly superior to the traditional broadband beamforming algorithm in terms of anti-interference ability. Especially in a complex environment with strong noise interference or multipath effects, it can effectively suppress the interference source and improve the signal-to-noise ratio of the system. It not only enhances the focusing ability on the target signal, but also shows strong robustness in suppressing interference, especially multipath interference.

[0125] The embodiment of the present invention utilizes the characteristics of the chirp signal to optimize the beamforming process through frequency-domain processing. It not only enhances the focusing ability on the target signal, but also shows strong robustness in suppressing interference, especially multipath interference. Compared with the traditional broadband beamforming method, the embodiment of the present invention can effectively improve the accuracy and reliability of the target direction-of-arrival (DOA) estimation in real-time signal processing, and is especially suitable for the real-time direction estimation task in underwater acoustic communication systems.

[0126] The following is a simulation study on the anti-interference broadband beamforming algorithm for underwater acoustic communication signals disclosed above in the embodiment of the present invention. The simulation conditions are as follows:

[0127] In this simulation, a uniform linear array with 2m and 16 array elements is selected as the receiving signal device, and the system is set to use a frequency f s of 50kHz, a center frequency f o of 7.5kHz, a frequency modulation bandwidth B of 3kHz, and a pulse width T of about 0.3s. The signal-to-noise ratio SNR is set to -40dB:1dB:0dB, the target azimuth is set to a random angle within -50° to 50°, the spatial scanning range Θ∈[-60,60], and a Monte Carlo experiment is carried out. The experiment is repeated 1000 times under each signal-to-noise ratio condition, and the detection probabilities of the three beamforming algorithms AWBF, CBF, and MVDR and the mean square error of angle estimation as shown in Figure 6 are obtained. It can be seen that the detection probabilities of the three algorithms all increase with the increase of the signal-to-noise ratio, and the detection probabilities of the AWBF, CBF, and MVDR algorithms reach 100% at -28dB, -19dB, and -15dB respectively. Figure 7

[0128] Figure 6 Figure 6 Figure 7 Figure 6 As shown in the figure, it can be seen from the figure that under the simulation conditions, AWBF has better detection performance and stronger anti-noise interference ability compared with the other two algorithms. Figure 7The simulation results of the mean square error of angle estimation for three beamforming algorithms are shown. From Figure 7 it can be seen that under the simulation conditions, the mean square error of angle estimation of AWBF is also better than that of the other two algorithms.

[0129] To further verify the anti-interference performance of the proposed algorithm, under the conditions of the above array and signal parameters, the target echo angle is set to 0°, the signal-to-noise ratio is 0 dB, and a single-frequency interference within the band with frequency f 1 of 8 kHz and broadband noise interference within the band are added respectively. The arrival angles of the interference signals are both 20°, and the signal-to-noise ratios are both -5 dB. As Figure 8 and Figure 9 shown, the spatial spectrum diagrams are output using three algorithms. Figure 8 Figure Figure 8 shows the output spatial spectrum under the addition of single-frequency interference within the band. From Figure 8 it can be seen that peaks appear at 0° for all three algorithms, estimating the target azimuth to be 0°. The MVDR algorithm is more sensitive to interference, and the arrival direction of the interference signal is reflected as 20° in the spatial spectrum. The other two algorithms have no response at 20°. Figure 9 Figure Figure 9 shows the output spatial spectrum under the addition of broadband noise interference within the band. From

[0130] it can be seen that the AWBF algorithm has an obvious anti-interference effect compared with the other two algorithms. Peaks appear at 0° for all three algorithms, estimating the target azimuth to be 0°. The arrival direction of the interference signal is reflected as 20° in the spatial spectra of CBF and MVDR, while the AWBF spatial spectrum has no response at 20°, showing an obvious anti-interference effect.

[0131] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. An anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals, characterized in that: include: Step 1: determining parameter data of a transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and processing the parameter data to obtain a spatial orientation set corresponding to the transmitted linear frequency modulation signal; Step 2: performing discrete Fourier transform on the data received by each array element in the spatial orientation set to obtain frequency domain data, and converting the frequency domain data to obtain a vector form; Step 3: obtaining a narrowband beam output according to the narrowband signal in the vector form, obtaining a time domain signal output according to the narrowband beam output and the parameter data, and obtaining a spatial spectrum value based on the time domain signal output; Step 4: Repeat step 3 to traverse the spatial spectrum values ​​in all directions in the spatial direction set to obtain the spatial direction spectrum diagram of the beamforming algorithm.

2. The anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals according to claim 1, characterized in that: The step one comprises: Determine the frequency band B, pulse width T, and time domain transmission signal replica s(t) in the parameter information of the transmitted linear frequency modulation signal according to the underwater acoustic communication signal, and fill the number of data points in the parameter information from 0 to L points, where L is an integer power of 2; Determine the scanning space orientation set Θ, and for a uniform linear array, take θ∈Θ=[-90°,90°]; The time domain transmission signal replica is subjected to L-point discrete Fourier transform to obtain a frequency domain signal replica.

3. The anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals according to claim 1, characterized in that: The calculation expression of the transmission synchronization signal in the underwater acoustic communication signal is: In formula (1), s(t) represents the time domain transmitted signal replica, A represents the amplitude, j represents the imaginary number, and j 2 =-1, f0 represents the carrier frequency, k represents the frequency modulation slope, B represents the frequency modulation bandwidth, and T represents the signal duration; Perform L-point DFT on the time domain transmitted signal replica s(t) to obtain the frequency domain signal replica S(k).

4. The anti-interference broadband beamforming algorithm for underwater acoustic communication signals according to claim 1, characterized in that: The second step comprises: Segmenting the data received by each array element in the spatial orientation set to obtain segmented data corresponding to each segment; Performing discrete Fourier transform on the segmented data to obtain corresponding frequency domain data; The frequency domain data is converted into a vector form, in which each subband corresponds to a group of narrowband data.

5. The anti-interference broadband beamforming algorithm suitable for underwater acoustic communication signals according to claim 4, characterized in that: In the step 2: The data x received by each array element in the spatial orientation concentration m (i), m = 1, ..., M are divided into segments, and the length of each segment is L; among them, the nth segment of the received data of the mth array element It is expressed as: In formula (2), Represents the time domain data of this segment of data, and L-1 represents the latest time sample in this segment of data; The segmented data corresponding to each array element is subjected to DFT to obtain the corresponding frequency domain data. The nth segment received data of the mth array element is expressed as The serial number of each band in the corresponding frequency band.

6. The anti-interference broadband beamforming algorithm for underwater acoustic communication signals according to claim 1, characterized in that: The step three comprises: Based on the vector form, each group of the narrowband data corresponds to a group of narrowband signals, and narrowband beamforming is performed on the narrowband signals to obtain outputs of each sub-band beam; Multiplying the beam output of each subband with a frequency domain transmit signal replica in the parameter data in the frequency domain and performing an inverse discrete Fourier transform to obtain a time domain signal output; The spatial spectrum value is obtained by first taking the absolute value and then taking the maximum value of the time domain signal output.

7. The anti-interference broadband beamforming algorithm for underwater acoustic communication signals according to claim 6, characterized in that: In the step three: The frequency domain data of each array element is written in vector form. Each sub-band corresponds to a set of narrowband data. The expression is: In formula (3), X (n) (k) represents the frequency domain data matrix, Represents the frequency domain data vector corresponding to this segment of the m array element; Each subband corresponds to a weighted value w m (f k )(m=1,…,M,k=0,…,L-1), perform complex weighted summation on each subband; Each sub-band corresponds to a group of narrowband signals, and narrowband beamforming is performed separately to obtain the beam output of each sub-band. The expression is: In formula (4), Y (n) (k) represents the frequency domain beam output result, w H (f k ) represents the weighted value matrix, X (n) (k) represents the frequency domain data matrix, represents the conjugate of the weight vector, Represents the frequency domain data vector of each array element; The beam output of each subband is multiplied by the signal replica in the frequency domain, and the expression is: Z (n) (k) = Y (n) (k)S * (k), k=0,…,L-1 Formula (5) In formula (5), Z (n) (k) represents the inverse discrete Fourier transform, Y (n) (k) represents the frequency domain beam output result, S * (k) represents the frequency domain replica of the transmitted signal; Inverse discrete Fourier transform Z (n) (k) Perform IDFT to obtain the time domain signal output, which is expressed as: z (n) (l),(l=0,…,L-1)=IDFT[Z (n) (k),(k=0,…,L-1)] Formula (6) In formula (6), z (n) (l) represents the time domain signal output, Z (n) (k) represents inverse discrete Fourier transform; z (n) (l) Take the absolute value and take the maximum value to obtain the spatial spectrum value at the direction θ corresponding to each sub-band.

8. The anti-interference broadband beamforming algorithm for underwater acoustic communication signals according to claim 1, characterized in that: In the step 4, step 3 is repeated to traverse all the orientations θ∈Θ in the spatial orientation set to obtain the spatial orientation spectrum of the proposed beamforming algorithm.

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