Self-adaptive coding image transmission system and method based on lunar surface field intensity prediction

Abstract

The invention provides a self-adaptive coding image transmission system and method based on lunar surface field intensity prediction.The self-adaptive coding image transmission system comprises a lunar surface channel simulation module and an image UEP coding and decoding receiving and transmitting module, the lunar surface channel simulation module is responsible for conducting field intensity prediction in combination with lunar surface real geographic information and electromagnetic parameters to obtain the receiving end signal intensity, and the image UEP coding and decoding receiving and transmitting module is responsible for conducting image UEP coding and decoding. And the image UEP coding and decoding transceiver module is responsible for realizing data classification by adopting discrete wavelet transform, decomposing an input original image into different sub-bands, and then constructing an unequal error protection scheme by combining a RaptorQ coding technology to ensure that important information obtains higher priority protection in transmission, so that the transmission reliability is improved. The method has the beneficial effects that: 1, the transmission efficiency is maximized while the data transmission reliability is ensured; 2, adaptive adjustment can be carried out according to communication conditions of different areas, and excellent adaptability and flexibility are shown;

Description

Technical Field

[0001] The present invention relates to the field of lunar surface communication technology, and in particular to an adaptive coded image transmission system and method based on lunar surface field strength prediction. Background Art

[0002] Image transmission is a typical application of lunar surface communication, providing the most intuitive and important information support for lunar exploration missions, and is of great significance for achieving lunar positioning, navigation and scientific research. However, the image recovery quality of lunar surface probes is reduced by signal attenuation and high packet loss rate caused by the lack of atmosphere, temperature difference and irregular terrain. The Lunar Reconnaissance Orbiter (LRO) generates about 550Gbit of data from its observation camera every day, and the amount of image data during lunar surface exploration will be even greater. However, Chang'e 3 can only perform point-to-point transmission at a bit rate of 800kbps, which is not enough to transmit images and videos, especially in the case of real-time communication. In addition, the rugged terrain and persistent dust clouds on the moon hinder the propagation of signals at communication frequencies higher than ultra-high frequency (UHF), which will cause severe wireless channel fading and further lead to bit-level errors or packet loss of image data.

[0003] Traditional error correction codes retransmit the entire encoded sequence when decoding fails, which can cause unacceptably long delays. This problem is particularly evident in multimedia streaming, where transmission delays can significantly degrade the quality of the viewing experience. In addition to recovering bit-level errors through physical layer forward error correction codes (FEC), application layer forward error correction codes (AL-FEC) are also designed to protect transmitted packets from higher-layer transmission errors. Although physical layer FEC is good at correcting bit errors within the physical frame, it is insufficient in addressing packet loss at the application layer. In contrast, fountain codes (e.g., Luby Transform (LT), Raptor, and RaptorQ codes), as typical representatives of application layer forward error correction (AL-FEC) codes, are more effective in handling packet loss in mobile video streaming services.

[0004] At this stage, the research on lunar communication links is disconnected from the upper-level image transmission technology and they cannot cooperate with each other. There is no complete communication system that can integrate the communication links and image coding transmission issues.

[0005] Defects of the prior art:

[0006] 1) Existing radio propagation models do not fully consider the different geographical conditions and environmental factors on the lunar surface, resulting in inaccurate predictions of lunar link loss; 2) The amount of data generated by lunar exploration equipment is huge, and the existing communication rate is insufficient to meet the requirements of efficient transmission, especially in real-time communication scenarios. It is necessary to design a method that can efficiently transmit large amounts of data within a limited bandwidth; 3) The mobile location and changing communication conditions during lunar exploration require an adaptive transmission system to ensure reliable and efficient image transmission. Current research on lunar communication links and image transmission technology is mostly carried out independently, and there is a lack of a complete system that can integrate the two. Summary of the invention

[0007] In order to solve the problems in the prior art, the present invention provides an adaptively coded image transmission system based on lunar surface field strength prediction, including a lunar channel simulation module and an image UEP encoding and decoding transceiver module. The lunar channel simulation module is responsible for combining the real geographic information of the lunar surface and electromagnetic parameters to predict the field strength and obtain the signal strength at the receiving end. The image UEP encoding and decoding transceiver module is responsible for using discrete wavelet transform to realize data classification, decomposing the input original image into different sub-bands, and then combining RaptorQ encoding technology to construct an unequal error protection scheme to ensure priority protection of important information and improve transmission reliability.

[0008] As a further improvement of the present invention, the adaptive coding image transmission system also includes an adaptive RaptorQ coding module, which predicts the field strength change of lunar surface communication through the AKVS algorithm and dynamically adjusts the K value according to the strength of the field strength.

[0009] As a further improvement of the present invention, the lunar channel simulation module is responsible for combining the measured lunar geographic information data, judging the type of obstacles on the communication link, combining the loss calculation method of the ITU-RP.526 model, referring to the loss calculation model of COST-231 and the multipath loss model, to establish a radio propagation model for a single base station on the lunar surface, and finally obtaining the theoretical received signal strength of each location on the lunar geographic information.

[0010] As a further improvement of the present invention, the receiving end signal strength P r The formula is as follows:

[0011] P r (x, y, z) = P t +G t +G r -L(x, y, z) (2)

[0012] Among them, P t Indicates the signal transmission power, G t represents the transmit antenna gain, Gr represents the receiving antenna gain, and L(x, y, z) represents the total electromagnetic wave loss on the lunar surface.

[0013] As a further improvement of the present invention, in the lunar channel simulation module, the receiving end signal strength P is established by receiving sensitivity. r The relationship between the received signal-to-noise ratio (SNR) is as follows:

[0014] SNR(x,y,z)=P r (x,y,z)-NF-10lgk B T e B w (3)

[0015] Where NF is the equipment noise factor (dB), k B is the Boltzmann constant (J / K), T e is the absolute temperature (K), B w is the signal bandwidth (Hz);

[0016] The relationship between the theoretical bit error rate BER and the signal-to-noise ratio SNR is obtained through a given modulation method. When BPSK modulation is used, the relationship between the two is as follows:

[0017] BER=Qfunc(SNR) (4)

[0018] Where Qfunc(·) is a probability function.

[0019] As a further improvement of the present invention, the image UEP encoding and decoding transceiver module includes:

[0020] Transmitter unit: used to perform discrete wavelet transform on the original image, decompose the image into different sub-bands, and then perform RaptorQ encoding on each sub-band one by one in the order from low frequency to high frequency, and then send it to the lunar surface packet loss channel;

[0021] Receiving unit: Receives information sent from the lunar packet loss channel, builds a decoding matrix based on the received symbols, and performs inverse wavelet transform based on the recovered wavelet coefficients to obtain the reconstructed image and compare it with the original image. At the same time, it feeds back the geographical location and decoding status to the transmitting end in real time.

[0022] As a further improvement of the present invention, the transmitting end unit performs the following steps:

[0023] Step S1: The original image is transformed by wavelet 'db2' to generate sixteen sub-bands, i.e. sixteen source blocks B. s =[b 0 , b 1 , ..., b 15], the i-th source data block is b i , including k i The length is T i (bit) data packet;

[0024] Step S2: according to the contribution to image restoration, the sixteen sub-bands are divided into five levels from low frequency to high frequency, and the level importance relationship is: Level 1>Level 2>Level 3>Level 4>Level 5;

[0025] Step S3: Let R i Indicates the channel coding rate of each level, that is, the ratio of source symbols to coded symbols. In order to protect the stream information of high importance level, the proportion of redundant symbols is increased, that is, the coding rate of information with high protection level is low, that is, R 1 ≤R 2 ≤R 3 ≤R 4 ≤R 5 .

[0026] As a further improvement of the present invention, in the image UEP encoding and decoding transceiver module, during the transmission process of each image layer, the transmitter continues to encode and transmit, and the receiver continues to try to decode. Once a layer of data is successfully decoded at the receiver, the sender will immediately receive feedback that the current layer has been successfully decoded and the next layer can be transmitted, and the number of data packets N that have been transmitted is recorded. * and bit number bit * .

[0027] As a further improvement of the present invention, the specific steps of the AKVS algorithm are as follows:

[0028] Step 1: Input the required image Image, lunar geographic location (x, y, z), and encoding tuple list Indices;

[0029] Step 2: Initialize the noise factor NF = 5, the theoretical number of bits to be sent is bit * =+∞;

[0030] Step 3: Get the block size B after discrete wavelet transform of the image s =[b 0 , b 1 , ..., b 15 ];

[0031] Step 4: Based on the lunar surface geographic location (x, y, z), derive the receiving end field strength P through field strength prediction r (x, y, z);

[0032] Step 5: Calculate the signal-to-noise ratio SNR and theoretical bit error rate BER at the location;

[0033] Step 6: Get the index K of K from Indices index ;

[0034] Step 7: Traverse B s Each image block b i ;

[0035] Step 8: k * Set to zero, k * =0;

[0036] Step 9: Traverse K index Each element k in j ;

[0037] Step 10: Calculate the minimum packet length

[0038] Step 11: Calculate the packet loss rate ε ij ;

[0039] Step 12: Calculate the theoretical minimum number of transmitted packets

[0040] Step 13: Determine whether bit* is greater than or equal to T ij N ij If yes, theoretically send bit * Update to T ij N ij , k * Update to k j , otherwise, continue to traverse the k value;

[0041] Step 14: k j After the traversal is completed, k * Add to K ＊ ;

[0042] Step 15: b i After the traversal is completed, return K ＊ .

[0043] The present invention also discloses an adaptive coded image transmission method based on lunar surface field strength prediction, comprising the following steps:

[0044] Step 1: Input the lunar surface topography, lunar surface geographic location (x, y, z), and the image to be transmitted to the adaptive coding image transmission system;

[0045] Step 2: According to the lunar surface topography and lunar surface geographic location (x, y, z) input in step 1, deploy the main base station LCT, and theoretically analyze the received signal strength and the receiving end signal strength P at each location on the lunar surface. r (x, y, z), and construct a lunar surface packet loss channel;

[0046] Step 3: The transmitter and receiver establish an image unequal error protection transmission system through discrete wavelet transform and RaptorQ coding;

[0047] Step 4: The receiving end feeds back the geographic location to the transmitting end in real time, and the transmitting end adjusts the coding strategy in real time through the AKVS algorithm in the adaptive RaptorQ coding;

[0048] Step 5: At the transmitter, DWT is used to implement block transmission of image data. s =[b 0 , b 1 , ..., b 15 ];

[0049] Step 6: At the receiving end, restore the wavelet coefficients through RaptorQ decoding, and reconstruct the image by restoring the image data through inverse discrete wavelet transform;

[0050] Step 7: Count the transmission redundancy Overhead after the restored image, and calculate the peak signal-to-noise ratio (PSNR) of the restored image to obtain the transmission effect of the adaptive coding image transmission system.

[0051] The beneficial effects of the present invention are as follows: 1. High efficiency: By dynamically adjusting the K value of RaptorQ coding, the present invention maximizes the transmission efficiency while ensuring the reliability of data transmission. The numerical simulation results show that compared with the RS coding scheme commonly used in traditional deep space communication, the present invention has significant advantages in data recovery capability and transmission efficiency, and greatly improves the viewing experience and transmission quality of images; 2. Reliability: RaptorQ coding has performed well in harsh environments due to its powerful data recovery capability. On this basis, the present invention combines unequal error protection (UEP) and AKVS algorithm to further enhance the stability of data transmission through a differentiated protection mechanism. Even under communication conditions with a high packet loss rate, the system can still maintain reliable data recovery capabilities; 3. Adaptability: The system can be adaptively adjusted according to the communication conditions in different regions, showing excellent adaptability and flexibility: (1) Code transmission adaptation: By utilizing the rateless characteristics of fountain codes, the system can dynamically adjust the code rate according to the current channel conditions during the code transmission process to optimize the transmission performance; (2) Position feedback adaptation: According to the field strength feedback and the device position, the modulation coding strategy is dynamically adjusted to achieve position-based adaptive coding optimization, thereby effectively coping with the complex and changeable communication environment on the lunar surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a model diagram of the adaptive coding image transmission system based on the lunar surface field intensity prediction of the present invention;

[0053] Figure 2It is a schematic diagram of five-level wavelet transform decomposition of the adaptive coding image transmission system of the present invention;

[0054] Figure 3 This is a comparison chart of the field strength coverage performance of the Apollo 15 landing site and the lunar South Pole under different frequency bands of the present invention;

[0055] Figure 4 This is a comparison chart of SNR vs Overhead when the PSNR of the present invention reaches 25dB;

[0056] Figure 5 This is a comparison chart of SNR vs PSNR when the data consumption is slightly greater than the original data volume;

[0057] Figure 6 This is a comparison diagram of Overhead VS PSNR when SNR=10dB of the present invention;

[0058] Figure 7 It is a diagram of the reconstruction process of the YuTu-2 (first row) and Shackleton (second row) images of the present invention; DETAILED DESCRIPTION

[0059] The present invention mainly aims at the problem of uninterrupted high-speed transmission of image services under the constraints of the extreme geographical environment of the lunar surface. It analyzes the lunar geographical information, explores the communication requirements between various devices on the lunar surface, combines fountain code (RaptorQ) and discrete wavelet transform (DWT) technology to realize image content unequal error protection (UEP), and adjusts the coding scheme through an adaptive K value selection algorithm (AKVS). Finally, an adaptive coded image transmission system based on lunar surface field strength prediction is designed, which lays a technical foundation for communication planning of lunar exploration.

[0060] Overview of the adaptive coded image transmission system based on lunar surface field strength prediction:

[0061] First, we combine the real digital elevation model (DEM) of the lunar surface to reasonably select the deployment location of the lunar communication tower (LCT), and simulate the radio wave propagation results through a suitable model to obtain the receiving end field strength, that is, the signal strength. This step provides solid data support for subsequent communication transmission.

[0062] Secondly, in image data transmission, discrete wavelet transform (DWT) is used to achieve data classification and decompose the image into different sub-bands. Combined with RaptorQ coding technology, an unequal error protection scheme is constructed to ensure that important information is given higher priority protection during transmission, thereby improving transmission reliability.

[0063] Finally, the two are combined to design an adaptive coded image transmission system based on lunar surface field strength prediction. This scheme also proposes an adaptive K value selection algorithm (AKVS) for RaptorQ coding, which dynamically adjusts the coding rate according to the geographic location to ensure the reliability and stability of image transmission while improving transmission efficiency.

[0064] The adaptive coding image transmission system based on lunar surface field strength prediction proposed by the present invention has the following characteristics:

[0065] 1. Virtual field strength: Combine the real geographical information of the lunar surface and electromagnetic parameters to predict the field strength. The present invention combines the ITU-RP.526 diffraction propagation model, the COST-231 free space propagation loss model and the multipath loss model to predict the lunar propagation loss, and further obtains the simulated field strength coverage, that is, the signal strength at the receiving end.

[0066] 2. Hierarchical protection: The image unequal error protection scheme that combines discrete wavelet transform (DWT) and RaptorQ coding can realize hierarchical transmission of image data and prioritize the protection of important information. This scheme improves data transmission efficiency and resource allocation while also enhancing the reliability of important information transmission.

[0067] 3. Adaptive communication: By predicting the changes in the field strength of lunar communications, the parameters of RaptorQ coding are dynamically adjusted to achieve efficient and reliable transmission of image data. The AKVS algorithm predicts the changes in the field strength of lunar communications and dynamically adjusts the K value (i.e., coding rate or redundancy) according to the strength of the field.

[0068] The present invention provides an adaptive coding image transmission system based on lunar surface field strength prediction, comprising a lunar surface channel simulation module and an image UEP coding and decoding transceiver module, wherein the lunar surface channel simulation module is responsible for combining the real geographic information of the lunar surface and electromagnetic parameters to predict the field strength and obtain the signal strength of the receiving end; the image UEP coding and decoding transceiver module is responsible for using discrete wavelet transform to realize data classification, decomposing the input original image into different sub-bands, and then combining RaptorQ coding technology to construct an unequal error protection scheme to ensure that important information obtains higher priority protection during transmission, thereby improving transmission reliability.

[0069] As a further preference of the present invention, the adaptive coding image transmission system also includes an adaptive RaptorQ coding module. The adaptive RaptorQ coding module predicts the field strength changes of lunar surface communications through the AKVS algorithm, and dynamically adjusts the K value (i.e., coding rate or redundancy) according to the strength of the field strength, thereby ensuring the reliability and stability of image transmission while improving transmission efficiency.

[0070] A specific scheme of an adaptive coded image transmission system based on lunar surface field strength prediction is as follows:

[0071] First, a lunar image adaptive transmission system consisting of three modules was established. The system adaptively adjusted the encoding parameters according to the actual geographical location of the moon, ensuring efficient and reliable transmission of lunar image data. Figure 1 Describes the architecture of the system and the functions of its various parts.

[0072] Below, we will describe in detail the functions and operating mechanisms of each of the three major modules in the system as shown in the figure above.

[0073] 1. Lunar channel simulation module

[0074] This module proposes the field strength coverage effect of a single base station deployed in a multi-facility communication network on the lunar surface. Combined with the measured lunar geographic information data, by judging the type of obstacles on the communication link, combined with the loss calculation method of the ITU-RP.526 model, referring to the loss calculation model of COST-231 and the multipath loss model, a radio propagation model for a single base station on the lunar surface is established, and finally the theoretical received signal strength at each location on the lunar geographic information is obtained.

[0075] The calculation expression of the total electromagnetic wave loss on the lunar surface is as follows:

[0076] L(x, y, z) = L f +L d +L mp (1)

[0077] Where L f represents free space loss (dB), L d represents diffraction loss (dB), L mp Represents multipath loss (dB).

[0078] Consider the signal transmission power P t , transmitting antenna gain G t and the receiving antenna gain G r , the receiving end signal strength P under LCT deployment can be obtained r as follows:

[0079] P r (x, y, z) = P t +G t +G r -L(x, y, z) (2)

[0080] The receiving sensitivity measures the relationship between the received signal strength and the signal-to-noise ratio of the receiving end. When the receiving end signal strength is greater than the receiving sensitivity, it can be considered that the signal can be received normally. Therefore, the receiving end signal strength P is established through the receiving sensitivity. r The relationship between the received signal-to-noise ratio (SNR) is as follows:

[0081] SNR(x, y, z) = P r (x, y, z)-NF-10lgk B T e B w (3)

[0082] Where NF is the equipment noise factor (dB), k B is the Boltzmann constant (J / K), T e is the absolute temperature (K), B w is the signal bandwidth (Hz).

[0083] Given a modulation mode, we can also get the relationship between the theoretical bit error rate BER and the signal-to-noise ratio SNR. Assuming BPSK modulation, the relationship between the two is as follows:

[0084] BER=Qfunc(SNR) (4)

[0085] Where Qfunc(·) is a probability function.

[0086] 2. Image UEP codec transceiver module

[0087] The image UEP codec transceiver module includes:

[0088] Transmitter unit: In the transmitter image encoding stage, the original image is first subjected to discrete wavelet transform (DWT). Next, each sub-band is RaptorQ encoded one by one in the order from low frequency to high frequency, and then enters the lunar surface packet loss channel.

[0089] The transmitter unit performs the following steps:

[0090] Step S1: The original image is processed by wavelet 'db2' and then subjected to a 5-level DWT to generate 16 sub-bands, i.e., 16 source blocks B. s =[b 0 , b 1 , ..., b 15 ], the i-th source data block is b i , including k i The length is T i (bit) data packet.

[0091] Step S2: Since the frequencies of the sub-bands are different, the sixteen sub-bands can be divided into five levels from low frequency to high frequency according to their contribution to image restoration. The level importance relationship is: Level 1>Level 2>Level 3>Level 4>Level 5.

[0092] Step S3: Let R iIndicates the channel coding rate of each level, that is, the ratio of source symbols to coded symbols. In order to protect the stream information of high importance level, it is necessary to increase the proportion of redundant symbols, that is, the coding rate of information with high protection level is low, that is, R 1 ≤R 2 ≤R 3 ≤R 4 ≤R 5 The schematic diagram of the five-level wavelet transform decomposition is as follows: Figure 2 As shown in the figure. During the transmission of each image layer, the transmitter continues to encode and transmit, while the receiver continues to try to decode. Once a layer of data is successfully decoded by the receiver, the sender will immediately receive feedback that the current layer has been successfully decoded and the next layer can be transmitted. The number of packets that have been transmitted, N, is recorded. * and bit number bit * .

[0093] The i-th source data block b i Contains k i The length is T i Assuming that each data packet is discarded once the number of error bits exceeds 1, the specific packet length T i Packet loss rate ε at a specific location i as follows:

[0094]

[0095] So far, the received signal strength P at a specific location under the deployment of the lunar main base station LCT is obtained. r (x, y, z), signal-to-noise ratio SNR(x, y, z) and BER, and finally the packet loss rate ε of the lunar binary erasure channel at a specific location is obtained.

[0096] Transmitter unit: The image decoding stage is the inverse operation of image encoding. After the transmission is completed, the decoding matrix is ​​constructed based on the received symbols. After that, the inverse wavelet transform can be performed based on the recovered wavelet coefficients to obtain the reconstructed image and compare it with the original image. At the same time, the receiving end will feedback the geographical location and decoding status to the transmitting end in real time.

[0097] 3. Adaptive RaptorQ coding scheme

[0098] The adaptive K value selection algorithm (AKVS) is shown in Algorithm 1. K is the number of source symbols in each image block. Different K values ​​can represent different coding rates. Indices is a list of (K, J, S, H, W) tuple values ​​in the RaptorQ standard. i∈{0, 1, ..., 15} represents B s The i-th block in T, j represents the j-th group of data in Indices. ij Represents image block b i In Kj The minimum length of source symbols. s For all blocks in the j Calculate the theoretical packet loss rate and the theoretical number of packets required to be transmitted, and find the K value that minimizes the theoretical number of transmitted bits. s The length is 16, and the maximum length of Indices is 477, so the algorithm complexity is low.

[0099]

[0100]

[0101] The present invention also discloses an adaptive coded image transmission method based on lunar surface field strength prediction, and its working process is as follows:

[0102] Step 1: System input: Input the lunar surface topography, lunar surface geographic location (x, y, z), and the image to be transmitted to the adaptive coding image transmission system;

[0103] Step 2: Field strength prediction: Based on the lunar surface topography and lunar surface geographic location (x, y, z) input in step 1, deploy the main base station LCT, theoretically analyze the received signal strength, and the receiving end signal strength P at each location on the lunar surface r (x, y, z), and construct a lunar surface packet loss channel;

[0104] Step 3: System construction: The transmitter and receiver establish an image unequal error protection transmission system through discrete wavelet transform and RaptorQ coding;

[0105] Step 4: Adaptive coding: The receiver feeds back the geographic location to the transmitter in real time, and the transmitter adjusts the coding strategy in real time through the AKVS algorithm in the adaptive RaptorQ coding;

[0106] Step 5: Data transmission: At the transmitter, DWT is used to realize block transmission of image data. s =[b 0 , b 1 , ..., b 15 ];

[0107] Step 6: Data reception: At the receiving end, the wavelet coefficients are restored by RaptorQ decoding, and the image data is restored by inverse discrete wavelet transform to reconstruct the image;

[0108] Step 7: Transmission effect: Count the transmission redundancy Overhead after the restored image, and calculate the peak signal-to-noise ratio (PSNR) of the restored image to obtain the transmission effect of the adaptive coding image transmission system.

[0109] In order to verify the function of the simulation system, we built an adaptive coded image transmission system based on the lunar surface field strength prediction. We used the actual elevation values ​​of the lunar South Pole and the Apollo 15 landing point to predict the field strength and perform adaptive coding transmission. We compared the transmission effects of the AKVS scheme proposed in this invention with the traditional UEP scheme, the EEP scheme with equal error protection, and the RS (255, 223) scheme commonly used in deep space communications.

[0110] In the simulation system of this paper, the simulation parameters are shown in Table 1:

[0111]

[0112] Table 1 Simulation parameters

[0113] The values ​​of the number of source symbols K for each sub-band of the comparison scheme are shown in the following table. Wherein, '\' indicates that the value of K in the AKVS scheme is adaptively changed and is not fixed.

[0114]

[0115] Table 2 Subband K values ​​for different coding schemes

[0116] (1) Comparison of field strength and coverage performance

[0117] Through field strength prediction, we can obtain a comparison of the field strength coverage performance of the Apollo 15 landing site and the lunar South Pole in the S-band (2.4GHz) and UHF-band (440MHz), as shown in the figure below. Within the 4km radius coverage of the Apollo 15 landing site, the coverage rate of signal-to-noise ratio SNR>15dB can reach 85% and 77% in the UHF band and S-band, respectively. Within the 4km radius coverage of the lunar South Pole, the coverage rate of signal-to-noise ratio SNR>15dB can reach 82% in the UHF band and 51% in the S band. When extended to a radius of 10km, the coverage rates in the UHF band and S band are 45% and 17%, respectively. Figure 3 shown.

[0118] (2) Transmission effect analysis

[0119] Figure 4 The overhead under different signal-to-noise ratios is shown when the PSNR reaches 25dB. For the RS coding scheme, the image cannot be restored when the signal-to-noise ratio is less than 6dB. Figure 4It can be seen that only when the image is large, i.e., the test image 2, the RS coding scheme is slightly better than the AKVS scheme at SNR = 6 dB. In other cases, the AKVS scheme proposed in this patent is optimal and can converge to the stable value faster. This means that our scheme can have smaller transmission redundancy and higher transmission efficiency at different signal-to-noise ratios when the PSNR reaches the same value, that is, when the restored image quality of each scheme is comparable.

[0120] Figure 5 It shows the PSNR that various schemes can achieve at different signal-to-noise ratios SNR when the data consumption is slightly greater than the original data volume. The redundancy of the two RaptorQ-based coding schemes for the images is less than 7%, and the redundancy of the RS-based coding scheme is about 13%. When 6 < SNR < 7, the RS coding scheme is slightly better than the RaptorQ coding scheme. In other cases, the AKVS scheme is the optimal scheme. Generally speaking, the RaptorQ coding scheme can make the range of PSNR values during the image restoration process smaller (20 - 50 dB), while the range of PSNR values of the RS coding scheme is larger (5 - 50 dB), which will bring the "cliff" effect and seriously affect the viewing experience of the image. When PSNR > 25 dB, the image quality is acceptable in terms of the viewing experience. Therefore, the AKVS scheme proposed in the present invention can make the image quality improve to the viewable level faster and enhance the viewing experience of image transmission.

[0121] Figure 6 It shows the transmission overhead required to reach different PSNRs at SNR = 10 dB. The RaptorQ coding scheme is better than the RS coding scheme, and the AKVS scheme proposed in the patent is the best among all coding schemes, which can significantly reduce the transmission overhead Overhead and improve the transmission efficiency.

[0122] (3) Summary of result analysis

[0123] Table 3 shows the coverage rate and image quality of the AKVS scheme under different lunar scenarios. Specifically, at the flat Apollo 15 landing site, the AKVS scheme ensures the smoothness of image transmission (i.e., peak signal-to-noise ratio PSNR > 25 dB). Within a 4-kilometer radius at 440 MHz and 2400 MHz, the coverage rate can reach about 86%, and most areas can fully restore the original image regardless of its size. In the rugged terrain of the lunar south pole with a radius of 4 kilometers, using the AKVS scheme ensures smooth image transmission (i.e., PSNR > 25 dB). The coverage rate at 440 MHz can reach 90%, and more than 87% of the areas can fully restore the original image. At 2400 MHz, the coverage rate can only reach 57%, and the transmission effect is worse than the previous situation. Since the LCT in the rugged terrain is located on the mountaintop, the coverage range of low-frequency signals (UHF band) within a small radius (4 kilometers) is larger than that in the flat terrain. Figure 7 Actual reconstructions of YuTu-2 and Shackleton are shown, and actual images corresponding to specific PSNRs are shown. Figure 7 In Table 3, we show the actual image transmission effect of the AKVS scheme under different communication frequencies and different lunar scenes. Figure 3 The actual image transmission effect at a certain geographical location on the moon can be obtained.

[0124] Table 3 Coverage and image quality of AKVS solution under different lunar scenes

[0125]

[0126] Aiming at the characteristics of lunar image transmission tasks, the present invention combines the actual measured lunar topographic data to design a lunar scientific research station image communication and information coding transmission management architecture scheme, and builds a visual adaptive coding image transmission system based on lunar surface field strength prediction. The simulation system combines discrete wavelet transform (DWT) and RaptorQ coding technology, designs an unequal error protection (UEP) scheme, and integrates the AKVS algorithm and field strength prediction module. It adjusts the transmission coding parameters in real time to adapt to different communication conditions, and improves the adaptability and practicality of the simulation system through comprehensive evaluation of coverage, transmission redundancy and image quality. The specific innovations are as follows:

[0127] 1. Combined with discrete wavelet transform DWT, a unequal error protection scheme (UEP) based on RaptorQ coding is designed, which enables the system to maintain stable data transmission under harsh communication conditions.

[0128] 2. Design the AKVS algorithm as the core algorithm of the system, which can adjust the K value in real time according to the strength of the field, thereby ensuring that the system can maintain the best performance under different communication conditions.

[0129] 3. Combined with the field strength prediction module, an adaptive image transmission system based on RaptorQ coding is built. This system not only uses transmission redundancy and image peak signal-to-noise ratio as measurement criteria, but can ultimately provide the coverage and image quality of the AKVS solution under different lunar scenes, thereby improving the availability of the simulation system.

[0130] The highlights of the new technology solution are as follows:

[0131] High efficiency: By dynamically adjusting the K value of RaptorQ coding, the present invention maximizes the transmission efficiency while ensuring data transmission reliability. Numerical simulation results show that compared with the RS coding scheme commonly used in traditional deep space communications, this scheme has significant advantages in data recovery capability and transmission efficiency, greatly improving the viewing experience and transmission quality of images.

[0132] Reliability: RaptorQ coding has performed well in harsh environments due to its strong data recovery capabilities. On this basis, the present invention combines unequal error protection (UEP) and AKVS algorithm to further enhance the stability of data transmission through a differentiated protection mechanism. Even under communication conditions with high packet loss rates, the system can still maintain reliable data recovery capabilities.

[0133] Adaptability: The system can make adaptive adjustments according to the communication conditions in different areas, demonstrating excellent adaptability and flexibility: (1) Coded transmission adaptation: By utilizing the rate-free characteristics of fountain codes, the system can dynamically adjust the code rate according to the current channel conditions during the code transmission process to optimize the transmission performance. (2) Position feedback adaptation: Based on the field strength feedback and device location, the modulation and coding strategy is dynamically adjusted to achieve position-based adaptive coding optimization, thereby effectively coping with the complex and changeable communication environment on the lunar surface.

[0134] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An adaptive coded image transmission system based on lunar surface field strength prediction, characterized in that: It includes a lunar channel simulation module and an image UEP encoding and decoding transceiver module. The lunar channel simulation module is responsible for predicting the field strength by combining the real geographic information of the lunar surface and electromagnetic parameters to obtain the signal strength at the receiving end. The image UEP encoding and decoding transceiver module is responsible for using discrete wavelet transform to realize data classification, decomposing the input original image into different sub-bands, and then combining RaptorQ coding technology to construct an unequal error protection scheme to ensure priority protection of important information and improve transmission reliability.

2. The adaptive coding image transmission system according to claim 1, characterized in that: The adaptive coding image transmission system also includes an adaptive RaptorQ coding module, which predicts the field intensity change of lunar surface communication through the AKVS algorithm and dynamically adjusts the K value according to the strength of the field intensity.

3. The adaptive coding image transmission system according to claim 1, characterized in that: The lunar channel simulation module is responsible for combining the measured lunar geographic information data, determining the types of obstacles on the communication link, combining the loss calculation method of the ITU-RP.526 model, referring to the loss calculation model of COST-231 and the multipath loss model, to establish a radio propagation model for a single lunar base station, and finally obtaining the theoretical received signal strength at each location on the lunar geographic information.

4. The adaptive coding image transmission system according to claim 3, characterized in that: The receiving end signal strength P r The formula is as follows: P r (x,y,z)=P t +G t +G r -L(x,y,z) (2) Among them, P t Indicates the signal transmission power, G t represents the transmit antenna gain, G r represents the receiving antenna gain, and L(x, y, z) represents the total electromagnetic wave loss on the lunar surface.

5. The adaptive coding image transmission system according to claim 4, characterized in that: In the lunar channel simulation module, the receiving end signal strength P is established by receiving sensitivity. r The relationship between the received signal-to-noise ratio (SNR) is as follows: SNR(x,y,z)=P r (x,y,z)-NF-10lgk B T e B w (3) Where NF is the equipment noise factor (dB), k B is the Boltzmann constant (J / K), T e is the absolute temperature (K), B w , is the signal bandwidth (Hz); The relationship between the theoretical bit error rate BER and the signal-to-noise ratio SNR is obtained through a given modulation method. When BPSK modulation is used, the relationship between the two is as follows: BER=Qfunc(SNR) (4) Where Qfunc(·) is a probability function.

6. The adaptive coding image transmission system according to claim 1, characterized in that: The image UEP encoding and decoding transceiver module includes: Transmitter unit: used to perform discrete wavelet transform on the original image, decompose the image into different sub-bands, and then perform RaptorQ encoding on each sub-band one by one in the order from low frequency to high frequency, and then send it to the lunar surface packet loss channel; Receiving unit: Receives information sent from the lunar packet loss channel, builds a decoding matrix based on the received symbols, and performs inverse wavelet transform based on the recovered wavelet coefficients to obtain the reconstructed image and compare it with the original image. At the same time, it feeds back the geographical location and decoding status to the transmitting end in real time.

7. The adaptive coding image transmission system according to claim 6, characterized in that: The transmitting end unit performs the following steps: Step S1: The original image is transformed by five-level discrete wavelet transform using wavelet 'db2' to generate sixteen sub-bands, i.e. sixteen source blocks B s =[b0, b1, ..., b 15 ], the i-th source data block is b i , including k i The length is T i (bit) data packet; Step S2: according to the contribution to image restoration, the sixteen sub-bands are divided into five levels from low frequency to high frequency, and the level importance relationship is: Level 1>Level 2>Level 3>Level 4>Level 5; Step S3: Let R i It represents the channel coding rate of each level, that is, the ratio of source symbols to coding symbols. In order to protect the stream information of high importance level, the ratio of redundant symbols is increased, that is, the coding rate of information with high protection level is low, that is, R1≤R2≤R3≤R4≤R5.

8. The adaptive coding image transmission system according to claim 6, characterized in that: In the image UEP codec transceiver module, during the transmission of each image layer, the transmitter continues to encode and transmit, while the receiver continues to try to decode. Once a layer of data is successfully decoded by the receiver, the sender will immediately receive feedback that the current layer has been successfully decoded and the next layer can be transmitted. The number of data packets that have been transmitted, N, is recorded. * and bit number bit * .

9. The adaptive coding image transmission system according to claim 2, characterized in that: The specific steps of the AKVS algorithm are as follows: Step 1: Input the required image Image, lunar geographic location (x, y, z), and encoding tuple list Indices; Step 2: Initialize the noise factor NF = 5, the theoretical number of bits to be sent is bit * =+∞; Step 3: Get the block size B after discrete wavelet transform of the image s =[b0, b1, ..., b 15 ]; Step 4: Based on the lunar surface geographic location (x, y, z), derive the receiving end field strength P through field strength prediction r (x, y, z); Step 5: Calculate the signal-to-noise ratio SNR and theoretical bit error rate BER at the location; Step 6: Get the index K of K from Indices index ; Step 7: Traverse B s Each image block b i ; Step 8: k * Set to zero, k * =0; Step 9: Traverse K index Each element k in j ; Step 10: Calculate the minimum packet length Step 11: Calculate the packet loss rate ε ij ; Step 12: Calculate the theoretical minimum number of transmitted packets Step 13: Determine bit * Is it greater than or equal to T? ij N ij If yes, theoretically send bit * Update to T ij N ij , k * Update to k j , otherwise, continue to traverse the k value; Step 14: k j After the traversal is completed, k * Add to K ＊ ; Step 15: b i After the traversal is completed, return K * .

10. An adaptive coded image transmission method based on lunar surface field strength prediction, characterized in that: The following steps are involved: Step 1: Input the lunar surface topography, lunar surface geographic location (x, y, z), and the image to be transmitted to the adaptive coding image transmission system; Step 2: According to the lunar surface topography and lunar surface geographic location (x, y, z) input in step 1, deploy the main base station LCT, and theoretically analyze the received signal strength and the receiving end signal strength P at each location on the lunar surface. r (x, y, z), and construct a lunar surface packet loss channel; Step 3: The transmitter and receiver establish an image unequal error protection transmission system through discrete wavelet transform and RaptorQ coding; Step 4: The receiving end feeds back the geographic location to the transmitting end in real time, and the transmitting end adjusts the coding strategy in real time through the AKVS algorithm in the adaptive RaptorQ coding; Step 5: At the transmitter, DWT is used to implement block transmission of image data. s =[b0, b1, ..., b 15 ]; Step 6: At the receiving end, restore the wavelet coefficients through RaptorQ decoding, and reconstruct the image by restoring the image data through inverse discrete wavelet transform; Step 7: Count the transmission redundancy Overhead after the restored image, and calculate the peak signal-to-noise ratio (PSNR) of the restored image to obtain the transmission effect of the adaptive coding image transmission system.

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