阅读说明：本技术 基于压缩感知与信道编码的水下信息采集与传输方法 (Underwater information acquisition and transmission method based on compressed sensing and channel coding ) 是由 冯立辉 卢继华 王欢 于 2019-09-12 设计创作，主要内容包括：本发明涉及基于压缩感知与信道编码的水下信息采集与传输方法,属于稀疏变换、压缩感知以及信道传输技术领域。使用高斯随机矩阵对水下信息进行采集即进行观测,随机观测矩阵具有随机分布,再采用标量量化器进行量化,并输出量化后的码字；量化后的码字进行信道编码和调制后经水下信道发送给接收端；在接收端进行解调和解码后,使用压缩感知重构信息,输出重构结构,再对重构结果进行稀疏逆变换得到恢复的高斯随机矩阵采集的水下信息。所述方法在采样时就完成了压缩,减缓系统硬件压力,降低成本；使用信道冗余编码,大大减少了水下无线信息传递过程中的误码率；采用AMP为主的重构方式,能实现稀疏度未知情况下对稀疏表示输出的系数进行重构。(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.)

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 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 R^m×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 R^N×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.

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 R^m×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 R^N×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;

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;

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.