Underwater acoustic communication method based on double-pilot-block structure under influence of time-varying Doppler

Abstract

The invention discloses an underwater acoustic communication method based on a double-pilot-block structure under the influence of time-varying Doppler, and relates to the field of underwater acoustic communication. According to the underwater acoustic communication method based on the double-pilot-frequency-block structure, a Doppler accumulation model which is an intermediate variable is constructed, a loss function is designed, a time period when time-varying Doppler changes and Doppler scaling factors before and after the time-varying Doppler changes are estimated, and a receiving end is subjected to segmented resampling and segmented equalization, so that the time-varying Doppler time-varying time-varying Doppler communication is realized. And the information recovery of the underwater acoustic signal under the influence of the time-varying Doppler is realized. The problem that underwater resources are limited is fully considered, redundant information is greatly reduced, and the method can be suitable for various common modulation technologies of existing underwater acoustic communication. The problem that information demodulation is difficult due to Doppler change in the long-time underwater acoustic communication process is solved, and the communication accuracy and the effective data transmission rate in the underwater environment are improved.

Description

Technical Field

[0001] The invention relates to the field of underwater acoustic communication, and in particular to an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. Background Art

[0002] Doppler effect is the most important challenge that underwater acoustic communication environment needs to face, and it is a key factor affecting the success of underwater communication. The size of Doppler is directly related to the change in relative speed between the transmitter and the receiver, resulting in the stretching or compression of the transmitted signal. Generally speaking, a single-scale multipath lag channel model with Doppler effect is considered to characterize such effects. In the actual communication process, due to the uncertainty of the underwater environment and the variability of the climate, the sudden appearance of islands or coral reefs or the sudden change of ocean conditions in a specific area is easy to occur, resulting in the original driving trajectory or driving speed of the transmitter and receiver to suddenly change during the communication process, so that the transmission signal is faced with time-varying Doppler in the long-term transmission process. In this mobile scenario, the impact of the changing Doppler is difficult to estimate, making the demodulation of underwater acoustic communication more difficult. Existing methods for dealing with time-varying Doppler usually revolve around an estimated Doppler scaling factor, and refine its changing trend by adaptively refining different data segments. However, this algorithm is usually an exhaustive algorithm with a large amount of calculation, and it is difficult to successfully apply it to resource-limited underwater acoustic communication environments. Based on the above situation and considering the difficulty of demodulating underwater acoustic signals under the influence of time-varying Doppler, an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler is designed. It is an important idea to deal with the underwater time-varying Doppler phenomenon and improve the communication efficiency.

[0003] Therefore, technicians in this field are committed to developing an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. In summary, the present invention is oriented to an underwater acoustic communication system and designs an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. Different from the existing methods for dealing with time-varying Doppler, the algorithm constructs an intermediate variable for estimating the time-varying Doppler scaling factor, a Doppler accumulation model, and derives the functional form of the model. Taking into account the current situation of limited measurement values, the algorithm adopts a fitting method to predict the Doppler accumulation model according to the distribution characteristics of the piecewise function. The algorithm constructs a loss function by calculating the root mean square error between the true value and the fitted value, successfully identifies the index block where the Doppler scaling factor varies in time, and then estimates the value before and after the time-varying Doppler changes, thereby realizing the effective estimation of the time-varying Doppler scaling factor. The present invention is mainly used in the orthogonal chirp division multiplexing (OCDM) underwater acoustic communication system in the description, but is also applicable to the orthogonal frequency division multiplex (OFDM) underwater acoustic communication system. The present invention can realize effective estimation of parameters related to the time-varying Doppler scaling factor, thereby improving communication accuracy and effective data transmission rate. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to cope with the underwater time-varying Doppler phenomenon and improve the communication efficiency.

[0005] To achieve the above object, the present invention provides an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler, comprising the following steps:

[0006] Step 1: Generate a transmission signal with a dual pilot block structure;

[0007] Step 2, sending the signal through a single-scale multipath hysteresis channel model with a time-varying Doppler scaling factor;

[0008] Step 3, oversampling the received signal under the influence of time-varying Doppler;

[0009] Step 4: construct an intermediate variable model, Doppler accumulation model;

[0010] Step 5, estimating the Doppler scaling factor of the time-varying Doppler change period and before and after the change in the received signal;

[0011] Step 6, performing a segmented resampling operation on the received signal;

[0012] Step 7: Perform segmented equalization on the resampled signals before and after the Doppler change.

[0013] Furthermore, the step 1 comprises the following steps:

[0014] Step 1.1, generate a dual pilot block structure and send a data block;

[0015] Step 1.2: Obtain a transmission signal having a dual pilot block structure.

[0016] Furthermore, in step 4, an intermediate variable for estimating a time-varying Doppler scaling factor is constructed according to the characteristics of the dual pilot block structure.

[0017] Furthermore, in step 4, the time-varying Doppler is estimated indirectly by finding an intermediate variable, and the intermediate variable is modeled as a Doppler accumulation model.

[0018] Furthermore, in step 4, a functional representation of the Doppler accumulation model is derived.

[0019] Furthermore, in step 5, a loss function is designed by using a regression method.

[0020] Furthermore, in step 5, the index block where the Doppler scaling factor changes with time is identified, and the values ​​before and after the time-varying Doppler change are estimated.

[0021] Furthermore, the step 5 comprises the following steps:

[0022] Step 5.1, performing a cross-correlation operation on the oversampled signal to estimate the arrival times of multiple correlation peaks;

[0023] Step 5.2, estimating the Doppler accumulation model by the correlation peak arrival time;

[0024] Step 5.3, according to the piecewise function distribution characteristics, the Doppler accumulation model is fitted using the piecewise fitting method;

[0025] Step 5.4, construct a loss function using the root mean square error of the estimated Doppler accumulation model and the fitted Doppler accumulation model;

[0026] Step 5.5, minimize the loss function and estimate the time period when the time-varying Doppler changes;

[0027] Step 5.6: Estimate the Doppler scaling factor before and after the Doppler change.

[0028] Furthermore, at the receiving end, the data before and after the Doppler change are resampled in sections, and then the carrier frequency offset is estimated and compensated, as well as the channel estimation and signal equalization are performed to restore the transmission information.

[0029] Furthermore, the step 7 comprises the following steps:

[0030] Step 7.1, performing carrier frequency offset interference compensation on the resampled signal;

[0031] Step 7.2, performing a two-step preprocessing operation on the signal for compensating for carrier frequency offset interference;

[0032] Step 7.3: Perform equalization operation and estimate the transmission information.

[0033] Existing underwater acoustic communication systems have limited ability to cope with the impact of time-varying Doppler, and it is difficult to accurately find the time period of Doppler changes and make accurate estimates. The present invention proposes an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. The algorithm can estimate the time period when the time-varying Doppler occurs by constructing intermediate variables, and then estimate the impact of the Doppler on the signal before and after the change. It can improve the accuracy of signal communication. The underwater acoustic communication method proposed in the present invention constructs an intermediate variable for estimating the time-varying Doppler scaling factor and a Doppler accumulation model based on the characteristics of the dual pilot block structure, and derives the functional form of the model. Considering the current situation of limited data resources, the loss function is designed by regression, and the index block where the Doppler scaling factor changes with time is successfully identified, and then the value before and after the time-varying Doppler change is estimated.

[0034] At present, underwater acoustic communication mainly considers the impact of a single Doppler on signal transmission. When the Doppler changes, it is difficult to demodulate the changed information, and the effective transmission rate is limited. The underwater acoustic communication method designed by the present invention designs the operation ideas of segmented compensation and segmented demodulation according to the estimated time-varying Doppler related parameters, and uses different Doppler estimation values ​​to compensate for the transmission data before and after the Doppler change, and performs subsequent compensation and equalization operations to complete signal recovery. In the underwater acoustic communication algorithm used at the receiving end of the present invention, the data before and after the Doppler change are segmented and resampled, and then the carrier frequency deviation is estimated and compensated, as well as the channel estimation and signal equalization, to effectively realize the information recovery in each transmission fast.

[0035] Most of the existing methods for dealing with Doppler are search algorithms with high computational complexity. In addition, additional training sequences need to be added, which consumes more limited resources under the condition of limited underwater resources. The underwater acoustic communication method based on the dual pilot block structure signal used in the present invention can directly estimate the time-varying Doppler related parameters using the multiple peaks generated by the receiving end without searching. In addition, the dual pilot block structure can be used for subsequent channel parameter estimation without the need to send additional redundant information. The underwater acoustic communication method designed for the dual pilot block structure of the present invention uses a pilot sequence for estimating time-varying Doppler, which can also assist in the estimation of carrier frequency offset and channel in the subsequent process without the need to add additional training sequences, and can achieve the situation where multiple parameter estimates share known information.

[0036] As for the influence of time-varying Doppler in the current underwater mobile communication field, the existing coping measures are difficult to track the changing Doppler well and estimate it with low complexity, resulting in a low success rate of signal demodulation under the influence of time-varying Doppler. Therefore, the present invention aims at the challenging problem of the influence of time-varying Doppler and designs an underwater acoustic communication method based on a dual pilot block structure. The method can estimate the changing period of the time-varying Doppler and the values ​​before and after the change, and the subsequent segmented resampling and segmented equalization operations can realize the information recovery of the underwater acoustic signal under the influence of time-varying Doppler. The proposed method improves the communication accuracy and the effective data transmission rate.

[0037] In the operating scenario of underwater acoustic communication in a mobile scenario, the present invention aims at the influence of time-varying Doppler on the received signal, considers building a Doppler accumulation model to estimate the time-varying Doppler, and then performs segmented resampling and segmented equalization processing to complete information recovery. A method for underwater acoustic communication based on a dual pilot block structure under the influence of time-varying Doppler is proposed.

[0038] The present invention provides an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. Considering the current situation of mobile underwater communication scenarios, signal demodulation needs to deal with the influence of time-varying Doppler, so as to design an underwater acoustic communication system. The Doppler accumulation model based on the dual pilot block structure designed by the present invention can be used to estimate the relevant parameters of time-varying Doppler. In the process of estimating the relevant parameters of the present invention, according to the distribution characteristics of the piecewise function, a loss function is constructed, and the index block where the Doppler scaling factor is time-varying is successfully identified, and then the value before and after the time-varying Doppler changes is estimated. The present invention can realize the effective estimation of the parameters related to the time-varying Doppler scaling factor, improve the communication accuracy and the effective data transmission rate. The dual pilot block structure used in the present invention can be used to estimate the remaining parameter information in the subsequent information recovery process to ensure the transmission bandwidth efficiency. The method for estimating parameters of the present invention is a solution optimization algorithm, which has a lower computational complexity than the exhaustive search method.

[0039] The present invention mainly considers the OCDM underwater acoustic system communication method under time-varying Doppler interference in the communication scenario. This scenario is only a typical representative scenario. The method can also be applied to the OFDM underwater acoustic communication system. In addition, the method can also be used normally in scenarios such as land where the Doppler variation is weak, thereby realizing effective recovery of information.

[0040] (1) Step 1: Generate a transmission signal with a dual pilot block structure.

[0041] (11) Step 11: Generate a dual pilot block structure and send a data block.

[0042] The data vector is generated by a complex alphabet table, represented as d(i) = [d(iN), d(iN+1), …, d(iN+N-1)] TThe OCDM data block is represented as The size of the matrix is ​​N × N is the inverse discrete Fresnel transform matrix, expressed as

[0043]

[0044] Where N is the number of OCDM subcarriers. The pilot vector is represented by p = [a, 0, ..., 0] T , insert pilot blocks before and after the OCDM data block and Each pilot block is allocated energy When L is an even number, The transmitted signal of the i-th transmission block can be defined as The length is M = 2L + N. After serial-to-parallel conversion, a pilot block is added at the beginning and the end. The data block exists between each dual pilot block. The dual pilot block structure is defined as Its length is 2L, and the sending sequence is expressed as The nth element of the transmitted sequence is denoted as S[n].

[0045] (12) Step 12: Generate a transmission signal having a dual pilot block structure.

[0046] Set the sampling period to T s ≈1 / B, where B is the transmission signal bandwidth. A pulse shaper is used to generate a continuous signal, expressed as

[0047]

[0048] Where ρ(t) represents the pulse shaper. After up-conversion, the OCDM passband transmit signal is expressed as where f c is the carrier frequency.

[0049] (2) Step 2: Send the signal through a single-scale multipath lag channel model with a time-varying Doppler scaling factor.

[0050] In the mobile underwater acoustic communication scenario, the single-scale multipath hysteresis channel model can be expressed as

[0051]

[0052] There are a total of P paths, τ P and A p are the propagation delay of the pth path and the time-varying amplitude of the pth path respectively. The time-varying Doppler scaling factor α(t) is c During the transmission of a data block, α(t) changes from α1 to α2, using an equivalent value αc ∈[α1,α2] to describe the Kth c The equivalent value of the Doppler scaling factor in the data block. The discrete form of α(t) is expressed as

[0053]

[0054] (3) Step 3: Oversample the OCDM received signal under the influence of time-varying Doppler.

[0055] At the receiving end, the OCDM received signal is expressed as

[0056]

[0057] Where * represents the convolution operation and the variance is σ 2 Additive Gaussian white noise The mean is zero. The received signal is oversampled, and the sampling period is T r , the oversampling rate is λ=T s / T r , the oversampled signal is expressed as

[0058]

[0059] (4) Step 4: Construct an intermediate variable model: Doppler accumulation model.

[0060] By finding the intermediate variables to indirectly estimate the time-varying Doppler, the intermediate variables are modeled as the Doppler accumulation model, which is specifically expressed as

[0061]

[0062] in is the average of the cumulative sum of the time-varying Doppler scaling factors, expressed as

[0063]

[0064] The Doppler accumulation model can be derived as a piecewise function, the first section is a constant function, and the second section is a rational function, which can be expressed as

[0065]

[0066] (5) Step 5: Estimate the Doppler scaling factor before and after the time-varying Doppler change period in the received signal.

[0067] (51) Step 51: Use the local template to perform a cross-correlation operation on the oversampled signal to estimate the arrival times of multiple correlation peaks.

[0068] For local templates, use T r The sampling period is oversampled, which is expressed as

[0069]

[0070] Using the local template, the oversampled signal is cross-correlated. The oversampled signal passes through the normalized matched filter, and the arrival time of the i-th correlation peak can be derived, which is expressed as

[0071]

[0072] The search subset of Doppler scaling factors is expressed as is the search range after oversampling.

[0073] (52) Step 52: Estimating Doppler accumulation model by correlation peak arrival time

[0074] The Doppler accumulation model can be estimated from the correlation peak arrival time and is expressed as

[0075]

[0076] in and are the estimated values ​​of the first peak and the subsequent peaks, respectively. When the noise interference is ignored, the estimated Doppler accumulation model is similar to the ideal model.

[0077] (53) Step 53: According to the piecewise function distribution characteristics, a piecewise fitting method is applied.

[0078] The estimated Doppler accumulation model is fitted using linear regression for the first constant function and rational regression for the second rational function. The estimated values ​​of the time-varying Doppler scaling factor are subsetted. The set is divided into two subsets by variable i. and For linear regression and rational regression respectively. Set and The sizes of are K,i and K-i+1 respectively.

[0079] The piecewise function in the Doppler accumulation model is a constant value in the first segment, which converts the estimated value corresponding to the largest block index into Considered as the optimal estimate of this section, the linear regression curve of the first section can be expressed as In the second subsection, the Doppler accumulation model follows a rational function distribution, and by using the set The block index value in and the corresponding Doppler accumulation model estimate can be obtained by the rational regression curve

[0080] (54) Step 54: Construct a loss function based on the root mean square error between the estimated value and the fitted value

[0081] The loss function is constructed using the root mean square error between the estimated Doppler accumulation model and the fitted Doppler accumulation model, which is specifically expressed as

[0082]

[0083] in i / K and (K-i+1) / K are weights to balance the number of elements in different sets.

[0084] (55) Step 55: Minimize the loss function and estimate the time period when the time-varying Doppler changes.

[0085] By finding the minimum value of the loss function, the block index K can be obtained c The estimated value of the time-varying Doppler scaling factor is the block index K c It can be estimated as

[0086]

[0087] The received signal before the Doppler change is expressed as r1[k]=r[k], in is the maximum influence of Doppler on the received signal in this section. The received signal after Doppler change is expressed as r2[k]=r[k],

[0088] (56) Step 56: Estimate the Doppler scaling factor before and after the Doppler change.

[0089] Block index using estimated time-varying Doppler scaling factor The Doppler scaling factor of the received signal before and after the change is estimated and expressed as

[0090]

[0091] Where K min Represents the time from synchronization point to Kth min The minimum block index selected between correlation peaks.

[0092] (6) Step 6: Perform segmented resampling operation on the received signal.

[0093] Using the Doppler scaling factor estimate before and after the change and The OCDM received signal is resampled segment by segment, which is expressed as

[0094]

[0095] To simplify the description, the subscripts (·)1 and (·)2 are omitted in the subsequent description. The resampled signal is subjected to carrier removal and downsampling operation, and then passes through a low-pass filter, which is expressed as

[0096]

[0097] After serial-to-parallel conversion, y[n] is rewritten into matrix form as

[0098]

[0099] in Diagonal Matrix Channel parameter matrix H M (i) The first column is [h i (0),…, h i(L h ),0,…,0] T . in H M (i) is a Toeplitz matrix of size M×M, where L h is the maximum channel delay.

[0100] (7) Step 7: Perform segmented equalization on the resampled signal before and after the Doppler change.

[0101] (71) Step 71: Perform carrier frequency offset interference compensation on the resampled signal.

[0102] Use the estimated carrier frequency offset for the resampled signal Carrier frequency offset interference compensation is performed, expressed as,

[0103]

[0104] When the carrier frequency is offset When the estimate is more accurate, it can be deduced

[0105]

[0106] (72) Step 72: Perform a two-step preprocessing operation on the signal to compensate for the carrier frequency offset interference.

[0107] A two-step preprocessing operation is performed on the signal to compensate for carrier frequency offset interference. The subtraction vector of length N where H1(i) is an N×N upper triangular matrix with the first row being [0,…,0,h i (L h ),…,hi (1)]. Using a discrete Fourier transform matrix of size N × N Perform Fourier transform to obtain the preprocessed signal, expressed as,

[0108]

[0109] The estimated channel frequency response is is a diagonal matrix, H N (i) is the circulant matrix consisting of the channel impulse responses. The parameter matrix is an N×N diagonal matrix with the elements on the main diagonal being After the time overlap operation, is colored noise.

[0110] (73) Step 73: Perform equalization operation and estimate the transmission information.

[0111] The estimated OCDM data symbol is represented as

[0112]

[0113] The balanced matrix G(i) is an N×N diagonal matrix, and the (l,l)th diagonal element is [G(i)] l,l The zero-forcing equalizer is expressed as

[0114]

[0115] in is the estimated channel frequency response. The minimum mean square error equalizer is expressed as

[0116]

[0117] in is the power of transmitted information, and the covariance of colored noise is

[0118] The present invention discloses an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler, and relates to the research field of underwater acoustic signal communication under the influence of time-varying Doppler. In view of the current situation that the methods for dealing with time-varying Doppler in underwater acoustic communication are relatively limited, the present invention designs an underwater acoustic communication method based on a dual pilot block, and estimates the time period when the time-varying Doppler changes, as well as the Doppler scaling factor before and after the change, by constructing the Doppler accumulation model as an intermediate variable and designing the loss function. The present invention fully considers the problem of limited underwater resources, and the estimation sequence used also assists in the estimation of parameters such as carrier frequency deviation and channel, greatly reducing redundant information, and can be applied to various existing common modulation technologies for underwater acoustic communications. The present invention solves the problem of information demodulation difficulties caused by Doppler changes during long-term underwater acoustic communications, and improves the communication accuracy and effective data transmission rate in underwater environments.

[0119] Compared with the prior art, the present invention has the following obvious substantial features and significant advantages:

[0120] 1. The underwater acoustic communication method based on dual pilot blocks designed by the present invention realizes the effective estimation of multiple related parameters of time-varying Doppler and improves the accuracy of underwater acoustic communication in mobile scenarios.

[0121] 2. The segmented demodulation method designed in the present invention can effectively process the data before and after the Doppler change respectively, reasonably use the estimated value for compensation, and improve the transmission success rate of the effective data in the transmission signal.

[0122] 3. The underwater acoustic communication method based on dual pilot blocks designed by the present invention can assist in the estimation of parameters such as time-varying Doppler, carrier frequency deviation, channel coefficient, etc. required for signal demodulation, with high information utilization efficiency and high bandwidth utilization. The estimation algorithm of time-varying Doppler is not an exhaustive search method, and the calculation complexity is low.

[0123] 4. The present invention realizes the information recovery of underwater acoustic signals under the influence of time-varying Doppler, thereby improving communication accuracy and effective data transmission rate.

[0124] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] Figure 1 It is an execution flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0126] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0127] In the drawings, components with the same structure are indicated by the same numerical reference numerals, and components with similar structures or functions are indicated by similar numerical reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of the components is appropriately exaggerated in some places in the drawings.

[0128] like Figure 1 As shown, a specific implementation process of an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler provided by the present invention is provided. The present invention is aimed at the underwater acoustic communication problem under the influence of time-varying Doppler in the underwater acoustic communication operation scene, so in actual use, the equipment parameter settings should be adjusted according to the actual sea environment.

[0129] The present invention provides an underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler. Considering the current situation of mobile underwater communication scenarios, signal demodulation needs to deal with the influence of time-varying Doppler, so as to design an underwater acoustic communication system. The Doppler accumulation model based on the dual pilot block structure designed by the present invention can be used to estimate the relevant parameters of time-varying Doppler. In the process of estimating the relevant parameters of the present invention, according to the distribution characteristics of the piecewise function, a loss function is constructed, and the index block where the Doppler scaling factor is time-varying is successfully identified, and then the value before and after the time-varying Doppler changes is estimated. The present invention can realize the effective estimation of the parameters related to the time-varying Doppler scaling factor, improve the communication accuracy and the effective data transmission rate. The dual pilot block structure used in the present invention can be used to estimate the remaining parameter information in the subsequent information recovery process to ensure the transmission bandwidth efficiency. The method for estimating parameters of the present invention is a solution optimization algorithm, which has a lower computational complexity than the exhaustive search method.

[0130] The present invention mainly considers the OCDM underwater acoustic system communication method under time-varying Doppler interference in the communication scenario. This scenario is only a typical representative scenario. The method can also be applied to the OFDM underwater acoustic communication system. In addition, the method can also be used normally in scenarios such as land where the Doppler variation is weak, thereby realizing effective recovery of information.

[0131] like Figure 1 As shown, the specific execution steps of the present invention are as follows:

[0132] (1) Step 1: Generate a transmission signal with a dual pilot block structure.

[0133] The data vector is generated by a complex alphabet table, represented as d(i) = [d(iN), d(iN+1), …, d(iN+N-1)] T The OCDM data block is represented as The size of the matrix is ​​N × N is the inverse discrete Fresnel transformation matrix. When L is an even number, N is the number of OCDM subcarriers. The pilot vector is represented by p = [a, 0, ..., 0] T , insert pilot blocks before and after the OCDM data block and Each pilot block is allocated energy When L is an even number, The transmitted signal of the i-th transmission block can be defined as The length is M = 2L + N. After serial-to-parallel conversion, a pilot block is added at the beginning and the end. The data block exists between each dual pilot block. The dual pilot block structure is defined as Its length is 2L, and the sending sequence is expressed as The nth element of the transmitted sequence is represented as s[n]. Set the sampling period to T s ≈1 / B, where B is the transmission signal bandwidth. A pulse shaper is used to generate a continuous signal, expressed as Where ρ(t) represents the pulse shaper. After up-conversion, the OCDM passband transmit signal is expressed as where f c is the carrier frequency.

[0134] (2) Step 2: Send the signal through a single-scale multipath lag channel model with a time-varying Doppler scaling factor.

[0135] In the mobile underwater acoustic communication scenario, the single-scale multipath hysteresis channel model can be expressed as There are a total of P paths, τ p and A p are the propagation delay of the pth path and the time-varying amplitude of the pth path respectively. The time-varying Doppler scaling factor α(t) is c During the transmission of a data block, α(t) changes from α1 to α2, using an equivalent value α c ∈[α1,α2] to describe the Kth c The equivalent value of the Doppler scaling factor in the data block. The discrete form of α(t) is expressed as

[0136] (3) Step 3: Oversample the OCDM received signal under the influence of time-varying Doppler.

[0137] At the receiving end, the OCDM received signal is expressed as Where * represents the convolution operation and the variance is σ 2 Additive Gaussian white noise The mean is zero. The received signal is oversampled, and the sampling period is T r , the oversampling rate is λ=T s / T r , the oversampled signal is expressed as

[0138] (4) Step 4: Construct an intermediate variable model: Doppler accumulation model.

[0139] By finding the intermediate variables to indirectly estimate the time-varying Doppler, the intermediate variables are modeled as the Doppler accumulation model, which is specifically expressed as in is the average of the cumulative sum of the time-varying Doppler scaling factors, expressed as The Doppler accumulation model can be derived as a piecewise function, the first section is a constant function, and the second section is a rational function, which can be expressed as

[0140] (5) Step 5: Estimate the Doppler scaling factor before and after the time-varying Doppler change period in the received signal.

[0141] For local templates, use T r The sampling period is oversampled, which is expressed as Using the local template, the oversampled signal is cross-correlated. The oversampled signal passes through the normalized matched filter, and the arrival time of the i-th correlation peak can be derived, which is expressed as The search subset of Doppler scaling factors is expressed as is the search range after oversampling. The Doppler accumulation model can be estimated from the correlation peak arrival time and is expressed as in and are the estimated values ​​of the first peak and the subsequent peaks, respectively. When the noise interference is ignored, the estimated Doppler accumulation model is similar to the ideal model. The estimated Doppler accumulation model is fitted by linear regression for the first constant function and rational regression for the second rational function. The estimated values ​​of the time-varying Doppler scaling factor are constructed as a subset The set is divided into two subsets by variable i. and For linear regression and rational regression respectively. Set and The sizes of are K,i and K-i+1 respectively. The piecewise function in the Doppler accumulation model is a constant value in the first segment, which converts the estimated value corresponding to the largest block index into Considered as the optimal estimate of this section, the linear regression curve of the first section can be expressed as In the second section, the Doppler accumulation model follows a rational function distribution, and by using the set The block index values ​​in and the corresponding Doppler accumulation model estimates can be used to obtain the rational regression curve The loss function is constructed using the root mean square error between the estimated Doppler accumulation model and the fitted Doppler accumulation model, which is specifically expressed as in i / K and (K-i+1) / K are weights to balance the number of elements in different sets. By finding the minimum value of the loss function, we can get the block index K c The estimated value of the time-varying Doppler scaling factor is the block index K c It can be estimated as The received signal before the Doppler change is expressed as r1[k]=r[k], in is the maximum influence of Doppler on the received signal in this section. The received signal after Doppler change is expressed as r2[k]=r[k], Block index using estimated time-varying Doppler scaling factor The Doppler scaling factor of the received signal before and after the change is estimated, which is expressed as Where K min Represents the time from synchronization point to Kth min The minimum block index selected between correlation peaks.

[0142] (6) Step 6: Perform segmented resampling operation on the received signal.

[0143] Using the Doppler scaling factor estimate before and after the change and The OCDM received signal is resampled segment by segment, which is expressed as To simplify the description, the subscripts (·)1 and (·)2 are omitted in the subsequent description. The resampled signal is subjected to carrier removal and downsampling, and then passes through a low-pass filter, which is expressed as After serial-to-parallel conversion, y[n] is rewritten into matrix form as in Diagonal Matrix Channel parameter matrix H M (i) The first column is [h i (0),…,h i (L h ),0,…,0] T . in H M(i) is a Toeplitz matrix of size M×M, where L h is the maximum channel delay.

[0144] (7) Step 7: Perform segmented equalization on the resampled signal before and after the Doppler change.

[0145] Use the estimated carrier frequency offset for the resampled signal Carrier frequency offset interference compensation is performed, which is expressed as When the carrier frequency is offset When the estimate is more accurate, it can be deduced A two-step preprocessing operation is performed on the signal to compensate for carrier frequency offset interference. The subtraction vector of length N where H1(i) is an N×N upper triangular matrix with the first row being [0,…,0,h i (L h ),…,h i (1)]. Using a discrete Fourier transform matrix of size N × N Perform Fourier transform to obtain the preprocessed signal, expressed as, The estimated channel frequency response is is a diagonal matrix, H N (i) is the circulant matrix consisting of the channel impulse responses. The parameter matrix is an N×N diagonal matrix with the elements on the main diagonal being After the time overlap operation, is colored noise. The estimated OCDM data symbol is expressed as The balanced matrix G(i) is an N×N diagonal matrix, and the (l,l)th diagonal element is [G(i)] l,l The zero-forcing equalizer is expressed as in is the estimated channel frequency response. The minimum mean square error equalizer is expressed as in is the power of transmitted information, and the covariance of colored noise is

[0146] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler, characterized in that: The following steps are involved: Step 1: Generate a transmission signal with a dual pilot block structure; Step 2, sending the signal through a single-scale multipath hysteresis channel model with a time-varying Doppler scaling factor; Step 3, oversampling the received signal under the influence of time-varying Doppler; Step 4: construct an intermediate variable model, Doppler accumulation model; Step 5, estimating the Doppler scaling factor of the time-varying Doppler change period and before and after the change in the received signal; Step 6, performing a segmented resampling operation on the received signal; Step 7: Perform segmented equalization on the resampled signals before and after the Doppler change.

2. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 1 comprises the following steps: Step 1.1, generate a dual pilot block structure and send a data block; Step 1.2: Obtain a transmission signal having a dual pilot block structure.

3. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 4 is to construct an intermediate variable for estimating the time-varying Doppler scaling factor according to the characteristics of the dual pilot block structure.

4. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 4 indirectly estimates the time-varying Doppler by finding an intermediate variable, and models the intermediate variable as a Doppler accumulation model.

5. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 4 is to derive a functional representation of the Doppler accumulation model.

6. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler according to claim 1, characterized in that: In step 5, a loss function is designed by using a regression method.

7. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 5 is to identify the index block where the Doppler scaling factor changes with time, and estimate the values ​​before and after the time-varying Doppler changes.

8. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: The step 5 comprises the following steps: Step 5.1, performing a cross-correlation operation on the oversampled signal to estimate the arrival times of multiple correlation peaks; Step 5.2, estimating the Doppler accumulation model by the correlation peak arrival time; Step 5.3, according to the piecewise function distribution characteristics, the Doppler accumulation model is fitted using the piecewise fitting method; Step 5.4, construct a loss function using the root mean square error of the estimated Doppler accumulation model and the fitted Doppler accumulation model; Step 5.5, minimize the loss function and estimate the time period when the time-varying Doppler changes; Step 5.6: Estimate the Doppler scaling factor before and after the Doppler change.

9. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler as claimed in claim 1, characterized in that: At the receiving end, the data before and after the Doppler change are resampled in sections, and then the carrier frequency offset is estimated and compensated, as well as the channel estimation and signal equalization are performed to restore the transmitted information.

10. The underwater acoustic communication method based on a dual pilot block structure under the influence of time-varying Doppler according to claim 1, characterized in that: The step 7 comprises the following steps: Step 7.1, performing carrier frequency offset interference compensation on the resampled signal; Step 7.2, performing a two-step preprocessing operation on the signal for compensating for carrier frequency offset interference; Step 7.3: Perform equalization operation and estimate the transmission information.

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