A method for encoding and decoding distributed process monitoring sources with low computational complexity and high reliability
A technology of computational complexity and encoding and decoding methods, which is applied in the field of distributed process monitoring information sources with low computational complexity and high reliability in encoding and decoding, can solve the problems of low decoding reliability and effectiveness, low decoding success rate, etc., so as to improve decoding Reliability, Reduced Computational Complexity, Effect of Low Computational Complexity
Active Publication Date: 2021-06-18
CHINA UNIV OF MINING & TECH
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However, the algorithm relies too much on one side information, which will lead to low decoding reliability and effectiveness. When the side information sensor communication is interrupted, no side information will be available. When the side information has little correlation with the signal to be recovered, the decoding will be successful. very low rate
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[0040] Such as image 3 with Figure 4 As shown, a distributed process monitoring information source low computational complexity and high reliability encoding and decoding method, wherein the relevant parameters are as follows: the set of side information is S={s 1 ,s 2 ,...s q ,...s Q}, q=1, 2, ..., Q, where s q ∈R N , Q is a real number greater than or equal to 1, N is a real number greater than or equal to 1, R N Represents a vector whose dimension is N; the set of signals to be encoded is W={w 1 ,w 2 ,...w l ,...w L}, l=1, 2, ..., L, where w l ∈R N , L is a real number greater than 1; signal w l The observation matrix with Φ l Indicates that Φ l is an M l ×N matrix of size, M 1 l is a sparse binary observation matrix; y l is the observation matrix Φ l to signal w l observed value of y l = Φ l w l ; Δy lq is the difference value between the signal observation value and the side information observation value, Δy lq =y l -Φ l the s q ; For using ...
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The invention discloses a distributed process monitoring information source with low computational complexity and high reliability encoding and decoding method. Aiming at the problem of high computational complexity of using random observation matrix at the encoding end, the present invention uses a sparse binary observation matrix at the encoding end to convert non-correlated The multiplication operation in the linear measurement is changed to an addition operation, which reduces the complexity of the encoding calculation and reduces the energy consumption of the algorithm. It is very suitable for independent encoding of sensor nodes; for the problem that the decoding end relies too much on one side information and the decoding reliability is low, this paper The invention proposes a distributed decoding and recovery algorithm based on multilateral information. The main solution is to use multiple side information, sort the side information according to the priority through the two indicators of the estimated sparsity of the difference between signals and the recovery residual, and use the optimal side information Improve decoding accuracy, use suboptimal side information when the optimal side information is not available, and so on, improve decoding reliability.
Description
technical field [0001] The invention relates to the field of signal processing, in particular to a method for encoding and decoding distributed process monitoring information sources with low computational complexity and high reliability Background technique [0002] Distributed information sources can be divided into two types: distributed real-time information sources and distributed process information sources in terms of monitoring requirements. Distributed real-time monitoring sources refer to sources that require high real-time information, such as gas, wind speed, negative pressure, etc. These sensor nodes need to output a sampling value and transmit it regularly in a short period of time, requiring real-time encoding and decoding . Distributed process monitoring sources refer to sources that do not require high real-time information, such as coal mine goaf temperature, channel waves, microseismic, etc. These sensor nodes do not need real-time transmission, and can b...
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IPC IPC(8): H03M7/30
CPCH03M7/3062
Inventor华钢刘海强黄冬勃徐永刚尹洪胜李璐姜代红
OwnerCHINA UNIV OF MINING & TECH