CN110708561A - Underwater information acquisition and transmission method based on compressed sensing and channel coding - Google Patents
Underwater information acquisition and transmission method based on compressed sensing and channel coding Download PDFInfo
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Abstract
The invention relates to an underwater information acquisition and transmission method based on compressed sensing and channel coding, and belongs to the technical field of sparse transformation, compressed sensing and channel transmission. Acquiring underwater information by using a Gaussian random matrix, namely observing, wherein the random observation matrix has random distribution, quantizing by using a scalar quantizer, and outputting a quantized code word; the quantized code words are subjected to channel coding and modulation and then sent to a receiving end through an underwater channel; and after demodulation and decoding are carried out at a receiving end, the compressed sensing reconstruction information is used for outputting a reconstruction structure, and then sparse inverse transformation is carried out on a reconstruction result to obtain the recovered underwater information acquired by the Gaussian random matrix. The method completes compression during sampling, reduces the pressure of system hardware and reduces the cost; by using channel redundancy coding, the error rate in the underwater wireless information transmission process is greatly reduced; and by adopting a reconstruction mode mainly based on AMP, the reconstruction of the coefficient of sparse representation output under the condition of unknown sparsity can be realized.
Description
Technical Field
The invention relates to an underwater information acquisition and transmission method based on compressed sensing and channel coding, and belongs to the technical field of sparse transformation, compressed sensing and channel transmission.
Background
With the development of multimedia technology and the increasing of the resolution of images and video signals, high-definition pictures and high-definition videos gradually become the mainstream of information transmission. The underwater environment is complex, and shooting and information transmission under water can be interfered to different degrees. The Compressed Sensing (CS) theory developed in recent years provides a way to de-interfere and reconstruct signals, which can be randomly observed through a measurement matrix at a lower sampling rate when the signals are sparse or compressible. And accurately reconstructing the signal through an optimization algorithm according to the obtained few observation values, wherein the reconstruction quality of the signal only depends on the number of the observation values and is irrelevant to which observation values are specifically used.
Natural images are typically not sparse, but can be sparsely represented under appropriately selected transform bases. In the compressive sensing algorithm, wavelet basis and multi-scale geometric analysis methods are often adopted to obtain sparse representation of an image. Sparse representation concentrates the energy of the signal on a small number of atoms that contain the main structural features of the image. Finding the optimal sparse representation base of an image is one basis for high quality reconstructed images. The smaller the residual value between the atoms in the dictionary and the image signal, the more matched the structural features, the easier it is to form a more concise sparse representation.
Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are effective tools for sparse representation of images. The underwater information two-dimensional wavelet transform can decompose an image into a plurality of sub-system numbers, and different sub-system numbers describe different information components in an original image.
For sparse signals, the purpose of low complexity is achieved according to a universal observation value without depending on the distribution characteristic of the signals, each observation value approximately and equally contains partial 'information' of the signals, any observation value is lost and interfered, other observations are not influenced to participate in the reconstruction process, and the method can adapt to a severe channel environment.
Disclosure of Invention
The invention aims to provide an underwater information acquisition and transmission method based on compressed sensing and channel coding, aiming at the technical defect that the information received in the process of transmitting pictures or information in an underwater environment is inaccurate or easy to lose.
The technical scheme adopted by the invention is as follows:
acquiring underwater information by using a Gaussian random matrix, namely observing, wherein the random observation matrix has random distribution, quantizing by using a scalar quantizer, and outputting a quantized code word; the quantized code words are subjected to channel coding and modulation and then sent to a receiving end through an underwater channel; and after demodulation and decoding are carried out at a receiving end, the compressed sensing reconstruction information is used for outputting a reconstruction structure, and then sparse inverse transformation is carried out on a reconstruction result to obtain the recovered underwater information acquired by the Gaussian random matrix.
The underwater information acquisition and transmission method based on compressed sensing and channel coding comprises the following steps:
step 1, carrying out compression sampling on underwater information and outputting an observation result matrix, specifically:
step 1.1, marking an original input signal as I; sparsely express I as
Wherein the content of the first and second substances,is a sparse wavelet sparse basis, and x is a sparse coefficient;
wherein, the dimension of I is m multiplied by n, namely m rows and n columns; m and n are both greater than or equal to 8;
the sparse basis adopted by the sparse expression is one of a discrete cosine transform basis, a Fourier transform basis and a discrete wavelet transform basis:
step 1.2, observing the sparsely expressed signals in the step 1.1 through a signal observation model, and outputting an observation result matrix Y;
wherein, the signal observation model is as follows: y is AX;
wherein Y is an observation result matrix, and Y belongs to Rm×nX is inputSignals I, m and n are dimension values of I in the step 1;
the matrix A is a Gaussian random matrix, and A belongs to RN×mWherein N is a dimension value less than m;
step 2, carrying out scalar quantity quantization on the observation result matrix Y output in the step 1.2, and quantizing the observation result matrix Y into a bit stream B;
each value in the matrix Y is quantized by adopting K bit information;
wherein the value range of K is an integer which is more than or equal to 4 and less than 24;
step 3, carrying out channel coding on the bit stream B output in the step 2, and outputting a coded symbol C;
the channel coding is one or a cascade code of several channel coding in spinal cord code, polar, LDPC, Turbo, convolution code and RS code;
step 4, modulating the coded symbol C and outputting a modulated symbol Q;
the modulation mode includes but is not limited to QAM, OFDM, TCM and various digital modulation modes; various digital modulation modes include but are not limited to MASK, MSFK and MPSK modulation, M is the power N of 2; n is greater than or equal to 1;
wherein, the wireless channel is one of a Gaussian white noise channel, a Rayleigh channel and a Rician channel;
step 6, receiving and demodulating the modulated symbol Q transmitted in the step 5, and outputting a demodulated symbol Q1;
step 7, performing channel decoding on the demodulated symbol Q1 output in the step 6, and outputting a channel decoded bit stream B1;
step 8, performing inverse quantization operation on the channel decoding bit stream B1 output in the step 7, and outputting an inverse quantization matrix;
step 9, performing compressed sensing reconstruction on the inverse quantization matrix output in the step 8, and outputting a reconstructed result Y1;
where compressed perceptual reconstruction includes, but is not limited to, AMP, OMP, and BP;
and step 10, performing sparse inverse representation on the reconstructed result output in the step 9 to obtain a recovered matrix I1.
Advantageous effects
Compared with the existing information transmission method, the underwater information acquisition and transmission method based on compressed sensing and channel coding has the following beneficial effects:
1. aiming at the acquisition of underwater complex information, the method directly completes compression during sampling, and does not perform full sampling like the traditional image compression, and then discards a transformation coefficient through sparse representation to obtain a compressed image; the method provided by the invention is used for performing sub-sampling on information mainly comprising underwater pictures and then eliminating noise and interference based on sparse basis transformation, thereby recovering the original information; in the imaging information acquisition process, the compression and acquisition of underwater information are integrated into a process, the measured value of image information is directly obtained through a small number of sensors, and the original information is reconstructed according to the obtained measured value, so that the number of the sensors can be reduced, the hardware pressure of an imaging system is relieved, and the cost is reduced;
2. by using channel redundancy coding, the error rate in the underwater wireless information transmission process is greatly reduced;
3. and by adopting a reconstruction mode mainly based on AMP, the reconstruction of the coefficient of sparse representation output under the condition of unknown sparsity can be realized.
Drawings
FIG. 1 is a block diagram of the underwater information acquisition and transmission method based on compressed sensing and channel coding according to the present invention;
FIG. 2 is a flow chart of an embodiment 1 of the underwater information acquisition and transmission method based on compressed sensing and channel coding according to the present invention;
fig. 3 is a simulation diagram of an embodiment 2 of the underwater information acquisition and transmission method based on compressed sensing and channel coding.
Detailed Description
The underwater information acquisition and transmission method based on compressed sensing and channel coding according to the present invention is further illustrated and described in detail below with reference to the accompanying drawings and embodiments.
Example 1
Fig. 1 is a schematic flow chart of the underwater information acquisition and transmission method based on compressed sensing and channel coding according to the present invention.
As can be seen from fig. 1, the underwater information is sparsely expressed first, and the sparsely expressed information is observed to compress data; then quantizing the observation result to form a code word; and coding the code words, adding redundancy protection, transmitting the code words to a receiving end through a channel, demodulating and decoding the code words, and performing sparse reverse representation on the coefficients subjected to scalar quantization and compressed sensing reconstruction to obtain the restored original signals.
Fig. 2 is a flow of an embodiment 1 of the method for acquiring and transmitting underwater information based on compressed sensing and channel coding according to the present invention, as follows:
step a, carrying out compression sampling on underwater information I;
for underwater 1 × M one-dimensional information, in the acquisition, effective N measurement values are directly acquired instead of M sampling values (N < M) satisfying the Nyquist sampling theorem, and then the information is expressed in a linear projection manner after being compressed and stored as a matrix Y, thereby realizing beneficial effect 1;
for underwater m × N two-dimensional information, such as shot images and videos, N observations of the information can be performed using a random gaussian matrix, and the sampling and compression processes are completed in a dimension reduction manner, which is specifically as follows:
step a.1, recording an original input signal as I; sparsely expressing the matrix I by using wavelet sparse basis as
Wherein the content of the first and second substances,
is a wavelet sparse basis, and x is a sparse coefficient;
in specific implementation, I is a picture or a signal that can be sparsely represented; the dimension of I is 256 × 256, i.e., m is 256 and n is 256;
in the specific implementation process, the first-stage reactor,
can be a discrete remainderA chord transformation basis, a fast fourier transformation basis;
step a.2, observing the sparsely expressed signals in the step a.1 through a signal observation model, and outputting an observation result matrix Y;
wherein, the signal observation model is as follows: y is AX;
wherein, X is the original input signal I; y is an observation result matrix; m and n are dimension values of I in the step a.1;
the matrix A is a Gaussian random matrix, and A belongs to RN×mWherein N is a dimension value less than m; each row of A is equivalent to one measurement, and N rows are equivalent to N measurement values;
b, carrying out scalar quantity quantization on the observation result matrix Y output in the step a.2, and quantizing the observation result matrix Y into a bit stream B;
wherein, each value in the matrix Y is quantized by 8 bits of information;
c, performing Polar channel coding on the quantized bit stream B output by the step B, and outputting a Polar coded symbol C;
wherein, Polar channel coding comprises the following substeps:
step c.1, carrying out channel polarization according to the Pasteur parameters based on a formula (1):
wherein Z is a Pasteur parameter, and W () is a transition probability of a channel; y is an output symbol, Y is a set of output symbols;
step c.2, for the polarization code with the coding length of N and the information length of K, calculating a generation matrix based on the formula (2);
wherein, BNFor N-dimensional bit flip operation GNTo generate a matrix, F is the matrix
Detailed description of the inventionTaking K/N as 0.5;
is a Kroneker radical;
step c.3, Polar coding is carried out on the quantized bit stream to be transmitted based on the formula (3) through the generated matrix of c.2, and a Polar coded symbol C is output:
wherein the content of the first and second substances,
in order to transmit the information bits, the bit rate of the information,
the code words are coded polar;
d, modulating the Polar coded symbol C and outputting a modulated symbol Q;
wherein, the modulation mode adopts BPSK;
e, transmitting the modulated symbol Q through a wireless channel;
in the embodiment, a Rayleigh channel is used as a gaussian white noise;
step f, demodulating the symbol transmitted in step e and outputting a demodulated symbol Q1;
step g, carrying out channel decoding on the demodulated symbol Q1 output in the step f, and outputting a channel decoding bit stream B1;
h, performing inverse quantization operation on the channel decoding bit stream B1 output in the step g, and outputting an inverse quantization matrix y;
step i, performing AMP compressed sensing reconstruction on the inverse quantization matrix Y output in the step h, and outputting a reconstructed result Y1;
the signal observation model is Y ═ AX in step 1.2; x is an original input signal, namely I; y is an observation result matrix; the matrix A is a Gaussian random matrix;
in specific implementation, the information repetition under the condition of unknown sparsity is realized by adopting AMPConstruct, in maximum number of iterations tmaxFor iteration stop control conditions, the following sub-steps are included:
step i.1, initializing a residual error r and a vector x to be restored;
setting the residual error r as y, setting y as the inverse quantization matrix in the step i, and setting the vector x to be restored as 0, namely setting r0Y, and x0＝0m×1；
Step i.2, obtaining noisy observation vector estimation q of x in the t iteration by using formula (4)t；
qt-1＝ATrt-1+xt-1； (4)
Wherein r ist-1Residual error, x, estimated for the t-1 th iterationt-1The value of the vector to be restored in the t-1 iteration is obtained;
step i.3, calculating the filter operator eta for filtering noise by formula (5)t(·)；
Wherein λ istAs a threshold parameter, the noise variance δtThe residual r can be found by equation (6)t-12 norm of (d);
step i.4, iterating the formulas (7) and (8) through tmaxThe second iteration obtains xtThe result is the result after reconstruction, and is marked as Y1;
xt＝ηt(qt-1；λtδt) (7)
rt＝y-Axt+rt-1bt(8)
wherein the convergence coefficient btSolving for x by using formula (9)t-10 norm of;
step j, performing sparse reverse representation on the reconstruction result Y1 output in the step j to obtain recovered I1;
fig. 3 is a bit error rate comparison graph of information transmission under water by using polar codes and transmission by using compressed sensing in combination with polar codes.
Therefore, when the transmission mode of combining the compressed sensing with the polar coding is used, the bit error rate can be reduced, and the beneficial effect 2 is realized.
Example 2
The embodiment illustrates the simulation result of the underwater information acquisition and transmission method based on compressed sensing and channel coding.
The raw data for the simulation is image information under water. Firstly, sparsely expressing an image through a discrete cosine transform basis (DCT) and a discrete wavelet transform basis (DWT), observing through a Gaussian random matrix, quantizing in a uniform quantization mode, respectively carrying out polar coding and convolutional code coding on quantized data, modulating the quantized data, then respectively reconstructing the demodulated and decoded data through a channel by using an approximate message transfer (AMP), and recovering the image. The images adopted by the method are 256x256 sea urchin images and sea cucumber images, the compression rate is 0.5, the channel model is a Rayleigh channel, and the signal-to-noise ratio is 10 dB.
Table 1 of the resulting simulation comparisons is as follows:
TABLE 1 images encoded using polar (PSNR values in Table, unit dB)
TABLE 2 images using convolutional coding (PSNR values in Table, unit dB)
As can be seen from table 1, the compressed sensing can better recover the original image information by collecting less information in combination with the channel coding technique. While the present invention has been described with reference to the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments and the drawings. Equivalents and modifications may be made without departing from the spirit of the disclosure, which is to be considered as within the scope of the invention.
Claims (8)
1. The underwater information acquisition and transmission method based on compressed sensing and channel coding is characterized by comprising the following steps: the method comprises the following steps:
step 1, compressing and sampling underwater information and outputting an observation result matrix; the method specifically comprises the following steps:
step 2, carrying out scalar quantity quantization on the observation result matrix Y, and quantizing the observation result matrix Y into a bit stream B;
step 3, carrying out channel coding on the bit stream B output in the step 2, and outputting a coded symbol C;
step 4, modulating the coded symbol C and outputting a modulated symbol Q;
step 5, transmitting the modulated symbol Q through a wireless channel;
wherein, the wireless channel is one of a Gaussian white noise channel, a Rayleigh channel and a Rician channel;
step 6, receiving and demodulating the modulated symbol Q transmitted in the step 5, and outputting a demodulated symbol Q1;
step 7, performing channel decoding on the demodulated symbol Q1 output in the step 6, and outputting a channel decoded bit stream B1;
step 8, performing inverse quantization operation on the channel decoding bit stream B1 output in the step 7, and outputting an inverse quantization matrix;
step 9, performing compressed sensing reconstruction on the inverse quantization matrix output in the step 8, and outputting a reconstructed result Y1;
and step 10, performing sparse inverse representation on the reconstructed result output in the step 9 to obtain a recovered matrix I1.
2. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 1, wherein: the step 1 comprises the following steps:
step 1.1, marking an original input signal as I; sparsely express I as
Wherein the content of the first and second substances,
is a sparse wavelet sparse basis, and x is a sparse coefficient;
wherein, the dimension of I is m multiplied by n, namely m rows and n columns; m and n are both greater than or equal to 8;
the sparse basis adopted by the sparse expression is one of a discrete cosine transform basis, a Fourier transform basis and a discrete wavelet transform basis:
step 1.2, observing the sparsely expressed signals in the step 1.1 through a signal observation model, and outputting an observation result matrix Y;
wherein, the signal observation model is as follows: y is AX;
wherein Y is an observation result matrix, and Y belongs to Rm×nX is input signals I, m and n are dimension values of I in the step 1;
the matrix A is a Gaussian random matrix, and A belongs to RN×m。
3. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 2, wherein: n is a dimension value less than m.
4. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 1, wherein: in step 2, each value in the matrix Y is quantized using K bits of information.
5. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 4, wherein: in step 2, the value range of K is an integer which is more than or equal to 4 and less than 24.
6. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 1, wherein: in step 3, the channel coding is one or a concatenated code of several channel coding selected from spinal code, polar, LDPC, Turbo, convolutional code, and RS code.
7. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 1, wherein: in step 4, the modulation modes include but are not limited to QAM, OFDM, TCM and various digital modulation modes; various digital modulation modes include but are not limited to MASK, MSFK and MPSK modulation, M is the power N of 2; n is greater than or equal to 1.
8. The underwater information acquisition and transmission method based on compressed sensing and channel coding as claimed in claim 1, wherein: in step 9, the compressed sensing reconstruction includes, but is not limited to, AMP, OMP, and BP.
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