Ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning

Abstract

The invention discloses an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning, and belongs to the field of ultrasonic cross-metal communication channel estimation. Constructing a pilot signal relationship between the receiving end and the transmitting end on the two sides of the metal wall; dividing a to-be-estimated metal channel impulse response into zero-value blocks or non-zero blocks of block sparse Bayesian according to whether the to-be-estimated metal channel impulse response is zero or not; the method comprises the following steps: iteratively updating hyper-parameters a, b, Bi and lambda of a non-zero block by adopting an expectation maximization algorithm, realizing structured sparse learning, calculating a maximum posteriori estimation result by utilizing a final hyper-parameter of the non-zero block, and taking the maximum posteriori estimation result as an estimation value of ultrasonic cross-metal channel pulse response. According to the method, a block sparse Bayesian learning algorithm is improved by utilizing the characteristics of metal channel pulse response, and compared with an original algorithm, the accuracy of channel estimation in a low signal-to-noise ratio environment is improved.

Description

Technical field:

[0001] The present invention belongs to the field of ultrasonic cross-metal communication channel estimation, and in particular relates to an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning. Background technology:

[0002] When performing status detection on a sealed metal container or transmitting data across metal walls (such as ship hulls, bulkheads, and spacecraft fuselages), reliable and efficient data transmission requires knowing the characteristics of the channel through which the signal passes, i.e., performing channel estimation on the metal channel.

[0003] Cross-metal communication is not suitable for wired transmission using perforated channels, as this method can damage the integrity of the metal wall, causing leakage or weakening the wall's structural strength. Furthermore, electromagnetic waves, the most commonly used wireless communication medium, are also unsuitable as a transmission medium for cross-metal communication systems due to the skin effect. However, ultrasonic waves, which have minimal attenuation when propagating through metal, are widely used in cross-metal communication systems.

[0004] Metal channels exhibit frequency-selective fading. Orthogonal frequency division multiplexing (OFDM) divides a wide frequency band into multiple orthogonal subcarriers, transforming frequency-selective fading channels into flat fading channels. This effectively overcomes the frequency-selective fading problem in metal channels, making OFDM suitable for ultrasonic cross-metal communication. However, achieving high-speed data transmission using OFDM requires understanding the channel state through channel estimation. Currently, many channel estimation methods are used in OFDM systems, with the least squares channel estimation method being the most common and fundamental. This method only requires the transmitted and received pilot signals to estimate the channel. However, this method requires a large number of pilots, resulting in high pilot overhead and an inability to eliminate the influence of noise. The estimation error is large in low signal-to-noise ratio conditions, making it difficult to ensure the reliability of the communication system.

[0005] The prior art CN104836629A describes a method and system for constructing a metal channel model, which helps predict the transmission performance of signals in metal channels. The method includes: determining the time domain mathematical model of the transmitter transducer and the receiver transducer; determining the transfer function of the transmitter coupling agent and the transfer function of the receiver coupling agent; determining the time domain attenuation model of ultrasonic waves in the metal medium; and determining the time domain mathematical model of the metal channel with echo pulses. The system includes: a transducer model determination unit for determining the time domain mathematical model of the transmitter transducer and the receiver transducer; a coupling agent function determination unit for determining the transfer function of the transmitter coupling agent and the transfer function of the receiver coupling agent; an attenuation model determination unit for determining the time domain attenuation model of ultrasonic waves in the metal medium; and a channel model determination unit for determining the time domain mathematical model of the metal channel with echo pulses. It only provides research on metal channels and has not been expanded based on actual applications. In addition, the research content is too idealistic and lacks practicality.

[0006] The channel impulse response of an ultrasonic inter-metal channel is a series of periodic, amplitude-decaying echo pulses. Therefore, it can be considered a sparse signal. Using sparse signal reconstruction algorithms to estimate it can reduce the number of pilot signals. However, existing sparse signal reconstruction algorithms perform poorly in low signal-to-noise ratio environments, which are typically encountered in inter-metal communication systems. Summary of the invention:

[0007] In response to the shortcomings of the existing technology, the present invention proposes an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning, which fully utilizes the characteristics of the time domain impulse response of the metal channel to improve the block sparse Bayesian learning algorithm, while reducing the number of pilot signals used and improving the accuracy of channel estimation in low signal-to-noise ratio environments.

[0008] In order to achieve the above object, the technical solution of the present invention is as follows:

[0009] An ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning is proposed. The method uses a cross-metal communication system for data transmission in a sealed metal container. The ultrasonic cross-metal communication system includes ultrasonic transducers arranged on both sides of the metal wall through a coupling layer as transmitters and receivers. The specific steps are as follows:

[0010] S1. Constructing the pilot signal relationship between the receiving end and the transmitting end on both sides of the metal wall;

[0011] S2. Divide the metal channel impulse response to be estimated into zero-value blocks or non-zero blocks of block sparse Bayes according to whether it is zero value;

[0012] S3, by using the maximum expectation algorithm to cyclically update the hyperparameters a, b, B of the non-zero blocks i,λ, when the loop iteration is completed, the final hyperparameters a,b,B are obtained i ,λ; where the hyperparameters a and b jointly control the variance of the block, and the hyperparameter B i The structure of the control block, the hyperparameter λ represents the noise variance.

[0013] S4. Calculate a maximum a posteriori estimation result using the final hyperparameters of the non-zero block, and use the maximum a posteriori estimation result as an estimated value of the ultrasonic cross-metal channel impulse response.

[0014] Furthermore, the specific steps for constructing the relationship between the pilot signals of the receiving end and the transmitting end on both sides of the metal wall are as follows: let the pilot signal of the transmitting end be X, and the pilot signal of the receiving end be Y. The relationship between the pilot signals of the receiving end and the transmitting end is:

[0015] Y=diag(X)Fh+W

[0016] Y is the received N×1-dimensional pilot signal, X is the transmitted N×1-dimensional pilot signal, F is the N×M-dimensional Fourier transform matrix corresponding to the pilot signal, all of which are known signals, diag() is a diagonal operation, let Φ=diag(X)F be the observation matrix; h is the channel impulse response of the M×1-dimensional metal channel, is the signal to be determined, and W is the N×1-dimensional noise in the propagation process; in order to reduce the pilot overhead, the number of pilots N is less than the length M of the channel impulse response of the metal channel.

[0017] Furthermore, the specific steps of dividing the metal channel impulse response into several blocks according to whether it is zero value are as follows:

[0018] S1.1. Calculate the echo peak time interval in the metal channel pulse response based on the ultrasonic wave velocity v and the metal wall thickness d.

[0019] S1.2. determining each echo peak in the metal channel impulse response based on the echo peak time interval ΔT;

[0020] S1.3. Set a preset threshold range with each echo peak as the center, divide the signal area within the threshold range into non-zero value blocks, and divide the signal area between adjacent non-zero value blocks into zero value blocks to obtain the final division result h = [h1,…,h g ], where j∈{1,2,…,g}, g is a natural number; when the subscript j of the block is an odd number, h j For zero-value blocks, when the block subscript j is an even number, h j For non-zero value blocks, zero value blocks and non-zero value blocks are arranged alternately. S1.4, for non-zero value blocks h j , h j Reorder to h i , where i is a natural number, define hi The length is L i . Non-zero value block h i Obey the parameterized multivariate Gaussian distribution, that is Where p represents the probability density function, represents a Gaussian distribution.

[0021] Furthermore, the maximum expectation algorithm is used to update the hyperparameters a, b, B of the non-zero blocks in the block sparse Bayesian i ,λ, the specific steps are as follows:

[0022] S2.1. Update hyperparameter B i , for B i Perform weighted prior enhancement, where:

[0023] Assume B i is a positive definite matrix that controls the correlation structure of the i-th block, the positive definite matrix B i The update rule is:

[0024] S2.1.1 Calculation in, is the posterior covariance matrix Σ h The i-th subdiagonal block of is the posterior mean μ h The i-th sub-block;

[0025] S2.1.2, for B i The first-order autoregressive process is used to model B i The formal constraint is a Toeplitz matrix denoted as

[0026] S2.1.3. Construct a diagonal matrix G i , using the diagonal matrix G i right Perform diagonal scaling to obtain the final hyperparameters

[0027] in, is the center of the block, and σ controls the rate at which the waveform decays;

[0028] S2.2, update hyperparameters a, b; use hyperparameters ab i Controls the amplitude of non-zero blocks, b i The i in the block represents the index number of the block. The amplitude of the echo reflected in the metal channel shows an exponential decay form, which is expressed by the hyperparameter ab i Controlling the amplitude of non-zero blocks can better capture the correlation between the amplitudes of each block and avoid estimating some echo signals with small amplitudes as zero:

[0029] Let abi is a non-negative parameter used to control the amplitude of the non-zero block. The update rule is:

[0030] make Established, the values ​​of hyperparameters a and b are obtained, where tr is the matrix trace operation;

[0031] S2.3. Update the hyperparameter λ. The update rule is as follows:

[0032] When the signal-to-noise ratio (SNR) is less than 20dB,

[0033] When the signal-to-noise ratio SNR>>20dB,

[0034] Where, Φ i represents the i-th sub-block of the observation matrix Φ.

[0035] When the final hyperparameters of the non-zero blocks in the block sparse Bayes are determined, the maximum a posteriori estimate is calculated based on the hyperparameters. It is used as the estimated value of the ultrasonic cross-metal channel impulse response;

[0036] A system for ultrasonic cross-metal channel estimation based on improved block sparse Bayesian learning, a system for transmitting data using a cross-metal communication system for a sealed metal container, comprising a data transmitter disposed on one side of the metal wall and a data receiver disposed on the other side of the metal wall;

[0037] The data transmitter converts the input data from serial to parallel form, then randomly inserts a pilot signal into the modulated signal after the serial-to-parallel conversion. This signal is then modulated using quadrature phase shift keying (QPSK). An inverse Fourier transform (IFFT) is performed on the modulated signal with the pilot signal inserted. A guard prefix is ​​added to the beginning of the IFFT-transformed signal to improve anti-interference capabilities. The signal with the guard prefix is ​​converted into an analog signal through digital-to-analog conversion (D / A). After amplification using a driver, the signal is converted from an electrical signal into an ultrasonic signal through an ultrasonic transducer. This signal is then converted into an ultrasonic signal for electroacoustic conversion and then transmitted into the metal channel of the metal wall.

[0038] The data receiving end converts the ultrasonic signal received by the ultrasonic transducer from the metal channel into an electrical signal, amplifies the electrical signal using a low-noise amplifier, and then undergoes analog-to-digital conversion (A / D), serial-to-parallel conversion, cyclic prefix removal, Fourier transform (FFT), channel estimation, parallel-to-serial conversion, and demodulation to correctly demodulate the transmitted signal. During this process, the receiving end estimates the metal channel based on the transmitted and received pilot information.

[0039] A computer device, comprising: a processor, a memory, and a network interface;

[0040] The processor is connected to the memory and the network interface, wherein the network interface is used to provide network communication functions, the memory is used to store program code, and the processor is used to call the program code to execute the ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning.

[0041] A computer-readable storage medium stores a computer program, wherein the computer program is suitable for being loaded by a processor and executing an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning.

[0042] Beneficial effect: The present invention utilizes the sparse metal channel characteristics of the ultrasonic cross-metal communication system to reduce the number of pilots used while improving the accuracy of channel estimation in low signal-to-noise ratio environments. The block sparse Bayesian learning algorithm is improved by using the hyperparameter ab i Control the amplitude of the non-zero block. Since the echo signal is generated by the reflection of ultrasound in the metal channel, the amplitude of the echo is in the form of exponential decay, and the hyperparameter ab is used. i Controlling the amplitude of non-zero blocks can better capture the correlation between the amplitudes of each block and avoid estimating some echo signals with small amplitudes as zero. i The form of B is further constrained. i The weighted prior enhancement is implemented; considering that the estimated echo signal is approximately a Gaussian pulse signal, the diagonal matrix G is used i To B i Diagonal scaling is performed to make the energy distribution of the block exhibit a central peak and decay toward the edges, suppressing edge effects at the covariance level. This improves the non-smooth decay of the echo pulse signal in the edge regions as estimated by the original algorithm. This method effectively improves metal channel estimation in low signal-to-noise ratio conditions, enabling metal channel estimation in ultrasonic cross-metal communication systems and ensuring transmission reliability. Description of the drawings:

[0043] Figure 1 Schematic diagram of the flow of an ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning in an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of a model of an ultrasonic cross-metal communication system used in an embodiment of the present invention.

[0045] Figure 3 Schematic diagram of the relationship between the normalized mean square error and the number of pilots of different methods at 5 dB in an embodiment of the present invention. Specific implementation method:

[0046] The embodiments of the present invention are further described in detail below with reference to specific drawings and examples.

[0047] The cross-metal communication system uses OFDM technology for signal transmission. The specific signal processing process is as follows: Figure 1 , the metal channel model is as follows Figure 2 . The input data is first converted into parallel form and then modulated by orthogonal phase shift keying. A pilot signal is inserted into the modulated signal, and a cyclic prefix is ​​added to the beginning of the signal to improve anti-interference ability. Finally, the signal with the cyclic prefix is ​​converted into an analog signal through digital-to-analog conversion (D / A), amplified by a driver, and then enters the metal channel. At the receiving end, the signal is amplified by a low-noise amplifier, and then undergoes analog-to-digital conversion, cyclic prefix removal, Fourier transform, channel estimation, demodulation, and parallel-to-serial conversion to obtain the received data.

[0048] The OFDM system parameters are: number of subcarriers N = 4096, frequency spacing Δf = 1kHz, cyclic prefix Number of pilots N CP =400.

[0049] The metal wall thickness is d s =63.5mm, the speed of ultrasonic wave propagation in the metal wall is c s =5900m / s.

[0050] An improved block sparse Bayesian learning algorithm is used to estimate the metal channel of the ultrasonic cross-metal communication system. The steps are as follows:

[0051] Step 1: Record the transmitted pilot signal X and the received pilot signal Y, as well as the Fourier matrix F corresponding to the transmitted pilot signal, and set Φ = diag(X)F. Construct the relationship between the pilot signals at the receiving and transmitting ends on both sides of the metal wall as: Y = Φh + W. Where h is the channel impulse response of the M × 1-dimensional metal channel, is the signal to be estimated, and W is the noise during the propagation process.

[0052] Step 2: Divide the metal channel impulse response h into several blocks according to whether it is zero value, specifically:

[0053] (1) Calculate the echo peak time interval in the metal channel pulse response based on the wave velocity v of the ultrasonic wave propagating in the metal wall and the metal wall thickness d

[0054] (2) Determine each echo peak in the metal channel impulse response based on the ΔT.

[0055] (3) Each echo peak is taken as the center, and a preset threshold range is set. The signal area within the threshold range is divided into non-zero value blocks, and the signal area between adjacent non-zero value blocks is divided into zero value blocks to obtain the final division result h = [h1,…,h g ], where j∈{1,2,…,g}, g is a natural number. When the subscript j of the block is an odd number, h j For zero-value blocks, when the block subscript j is an even number, h j For non-zero value blocks, zero value blocks and non-zero value blocks are arranged alternately.

[0056] (4) For non-zero value blocks h j , h j Reorder to h i , where i is a natural number, define h i The length is L i . Non-zero value block h i Obey the parameterized multivariate Gaussian distribution, that is Where p represents the probability density function, Represents Gaussian distribution. Since the elements of the zero block are all 0, we only need to estimate the elements of the non-zero block.

[0057] Step 3: Use the maximum expectation algorithm to update the hyperparameters a, b, B of the non-zero blocks in the block sparse Bayes i ,λ, the specific steps are as follows:

[0058] S1: Update the hyperparameter B in block sparse Bayes i ;

[0059] (1) Calculation in, is the posterior covariance matrix Σ h The i-th subdiagonal block of is the posterior mean μ h The i-th sub-block;

[0060] (2) To B i The first-order autoregressive (AR) process is used to model B i The formal constraint is

[0061] Toeplitz matrix, specifically: for each B i , record the mean of the main diagonal elements and sub-main diagonal elements of each block respectively and make but

[0062] (3) Construct the diagonal matrix G i , using the diagonal matrix G i right Perform diagonal scaling to obtain the final hyperparameters

[0063] in, is the center of the block, and σ controls the rate at which the waveform decays. In this example,

[0064] S2. Update the hyperparameters a, b in the block sparse Bayesian.

[0065] Let the following formula hold Use Newton's iteration method to solve the equation and get the values ​​of a and b, where tr is the matrix trace operation;

[0066] S3. Update the hyperparameter λ in the block sparse Bayesian.

[0067] When the signal-to-noise ratio (SNR) is less than 20dB,

[0068] When the signal-to-noise ratio SNR>>20dB, Where, Φ i Represents the i-th sub-block of the observation matrix Φ. When the iteration converges or reaches the maximum number of iterations, the iteration ends and the hyperparameters Θ={a,b,B i ,λ}.

[0069] Calculate the maximum a posteriori estimate based on the value of the hyperparameters As the estimated value of the ultrasonic cross-metal channel impulse response,

[0070] like Figure 3 It shows the relationship between the normalized mean square error of different methods and the number of pilots at 5 dB. As the number of pilots increases, the normalized mean square error decreases. The normalized mean square error of the method of the present invention is always lower than that of other compared methods.

Claims

1. An ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning, which uses a cross-metal communication system for data transmission in a sealed metal container. The ultrasonic cross-metal communication system includes ultrasonic transducers arranged on both sides of the metal wall through a coupling layer as transmitters and receivers, and is characterized in that: The specific steps of the method are as follows: S1. Constructing the pilot signal relationship between the receiving end and the transmitting end on both sides of the metal wall; S2. Divide the metal channel impulse response to be estimated into zero-value blocks or non-zero blocks of block sparse Bayes according to whether it is zero value; S3, by using the maximum expectation algorithm to cyclically update the hyperparameters a, b, B of the non-zero blocks i ,λ, when the loop iteration is completed, the final hyperparameters a,b,B are obtained i ,λ; where the hyperparameters a and b jointly control the variance of the block, and the hyperparameter B i The structure of the control block, the hyperparameter λ represents the noise variance; S4. Calculate a maximum a posteriori estimation result using the final hyperparameters of the non-zero block, and use the maximum a posteriori estimation result as an estimated value of the ultrasonic cross-metal channel impulse response.

2. The ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to claim 1, characterized in that: Construct the relationship between the pilot signals of the receiving and transmitting ends on both sides of the metal wall. The specific steps are as follows: let the pilot signal of the transmitting end be X, and the pilot signal of the receiving end be Y. The relationship between the pilot signals of the receiving and transmitting ends is: Y=diag(X)Fh+W Y is the received N×1 dimensional pilot signal, X is the transmitted N×1 dimensional pilot signal, F is the N×M dimensional Fourier transform matrix corresponding to the pilot signal, all of which are known signals, diag() is a diagonal operation, let Φ=diag(X)F be the observation matrix; h is the channel impulse response of the M×1 dimensional metallic channel, is the signal to be determined, and W is the N×1 dimensional noise during the propagation process. In order to reduce the pilot overhead, the number of pilots N is less than the length M of the channel impulse response of the metallic channel.

3. The ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to claim 2, characterized in that: The specific steps of dividing the metal channel impulse response into several blocks according to whether it is zero value are as follows: S1.

1. Calculate the echo peak time interval in the metal channel pulse response based on the ultrasonic wave velocity v and the metal wall thickness d. S1.

2. determining each echo peak in the metal channel impulse response based on the echo peak time interval ΔT; S1.

3. Set a preset threshold range with each echo peak as the center, divide the signal area within the threshold range into non-zero value blocks, and divide the signal area between adjacent non-zero value blocks into zero value blocks to obtain the final division result h = [h1,…,h g ], where j∈{1,2,…,g}, g is a natural number. When the subscript j of the block is an odd number, h j For zero-value blocks, when the block subscript j is an even number, h j For non-zero value blocks, zero value blocks and non-zero value blocks are arranged alternately; S1.

4. For non-zero value blocks h j , h j Reorder to h i , where i is a natural number, define h i The length is L i ; Non-zero value block h i Obey the parameterized multivariate Gaussian distribution, that is Where p represents the probability density function, represents a Gaussian distribution.

4. The ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to claim 3, characterized in that: Use the maximum expectation algorithm to update the hyperparameters a, b, B of the non-zero blocks in the block sparse Bayes i ,λ, the specific steps are as follows: S2.

1. Update hyperparameter B i , for B i Perform weighted prior enhancement, in: Assume B i is a positive definite matrix that controls the correlation structure of the i-th block, the positive definite matrix B i The update rule is: S2.1.1 Calculation in, is the posterior covariance matrix Σ h The i-th subdiagonal block of is the posterior mean μ h The i-th sub-block; S2.1.2, for B i The first-order autoregressive process is used to model B i The formal constraint is a Toeplitz matrix, denoted as S2.1.

3. Construct a diagonal matrix G i , using the diagonal matrix G i right Perform diagonal scaling to obtain the final hyperparameters in, is the center of the block, and σ controls the rate at which the waveform decays; S2.2, update hyperparameters a, b; use hyperparameters ab i Controls the amplitude of non-zero blocks, b i The i in the block represents the index number of the block. The amplitude of the echo reflected in the metal channel shows an exponential decay form, which is expressed by the hyperparameter ab i Controlling the amplitude of non-zero blocks can better capture the correlation between the amplitudes of each block and avoid estimating some echo signals with small amplitudes as zero: Let ab i is a non-negative parameter used to control the amplitude of the non-zero block. The update rule is: make Established, the values ​​of hyperparameters a and b are obtained, where tr is the matrix trace operation; S2.

3. Update the hyperparameter λ. The update rule is as follows: When the signal-to-noise ratio (SNR) is less than 20dB, When the signal-to-noise ratio SNR>>20dB, Where, Φ i represents the i-th sub-block of the observation matrix Φ.

5. The ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to claim 4, characterized in that: When the final hyperparameters of the non-zero blocks in the block sparse Bayes are determined, the maximum a posteriori estimate is calculated based on the hyperparameters. as an estimate of the ultrasonic cross-metal channel impulse response; where Σ 0＝ diag(ab 2 B2,…,ab i B i ).

6. A system using the ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to claim 1, characterized in that: A system for transmitting data using a cross-metal communication system in a sealed metal container, comprising a data transmitter disposed on one side of the metal wall and a data receiver disposed on the other side of the metal wall; The data transmitter converts the input data from serial to parallel form, then randomly inserts a pilot signal into the modulated signal after serial-to-parallel conversion, and then modulates it through orthogonal phase shift keying. The modulated signal with the inserted pilot signal is subjected to inverse Fourier transform (IFFT), and a guard prefix is ​​added to the beginning of the signal after the IFFT transform to improve anti-interference capability. The signal with the protection prefix is ​​converted into an analog signal through digital-to-analog conversion (D / A), amplified by a driver, and then converted into an ultrasonic signal through an ultrasonic transducer. After electroacoustic conversion, it is transmitted into the metal channel of the metal wall. The data receiving end converts the ultrasonic signal received by the ultrasonic transducer from the metal channel into an electrical signal, amplifies the electrical signal using a low-noise amplifier, and then undergoes analog-to-digital conversion (A / D), serial-to-parallel conversion, cyclic prefix removal, Fourier transform (FFT), channel estimation, parallel-to-serial conversion, and demodulation to correctly demodulate the transmitted signal. During this process, the receiving end estimates the metal channel based on the transmitted and received pilot information.

7. A computer device, characterized in that: include: processor, memory, and network interface; The processor is connected to the memory and the network interface, wherein the network interface is used to provide network communication functions, the memory is used to store program code, and the processor is used to call the program code to execute the ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the ultrasonic cross-metal channel estimation method based on improved block sparse Bayesian learning according to any one of claims 1 to 5.

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