An Efficient Lossless Telemetry Data Compression and Fault Analysis System Using 2SMLZ and CMOW-DLNN

被引:1
|
作者
Ramalingam, Parameshwaran [1 ]
Thanuja, R. [2 ]
Bhavani, R. [2 ]
Gopalakrishnan, Lakshminarayanan [1 ]
机构
[1] Natl Inst Technol, Dept ECE, Tiruchirappalli, Tamil Nadu, India
[2] SASTRA Deemed Univ, Dept CSE, SRC Campus, Kumbakonam, India
关键词
Telemetry data compression; Fault analysis; Two-stage modified Lempel-Ziv; Enhanced elliptic curve cryptography; Convolutional modified output weight-based deep learning neural network; IMAGE COMPRESSION; VERSATILE;
D O I
10.1007/s11277-021-08799-0
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The quantity of Telemetry Data (TD) is rapidly augmenting because of a large extent of involved parameters in addition to the use of higher sampling frequencies. Therefore, to enhance the transmission efficiency as well as lessen the load on spacecraft resources, effectual Data Compression approaches in space TD are required. Further, the TD is suffered as of some faults. Therefore, to identify that fault data and render secure Data Transmission, the fault-analysis and data security techniques are needed onboard. It is challenging to attain higher Compression Ratios together with higher accuracy in lossless compression together with fault analysis. This paper designs an effective lossless TD compression as well as fault analysis scheme to trounce all these challenges. This scheme comprises:- (i) compression, (ii) encryption and (iii) classification. In the compression stage, the 2SMLZ compress the redundancy removed TD. In the encryption stage, the EECC algorithm encrypts the compressed data, so that the encrypted data can well be transmitted securely to the Ground Station (GS). In the GS, for recovering the original data, the decryption, as well as the decompression process, are carried out. Conversely, the CMOW-DLNN classifier identifies the fault data on the fault analysis stage. The proposed techniques effectively compress the data, as well as securely transmit the data and accurately recognize the fault data, which are experimentally proved by contrasting it with existing techniques.
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页码:2325 / 2345
页数:21
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