毕业论文-基于多源传感器数据的无损压缩算法研究.docx

基于多源传感器数据的无损压缩算法研究摘要近年来，多源传感器系统已经广泛应用在军事、农业，林业等多个领域，然而随之而来的问题是，信息表现的多样性、信息数量的巨大性，信息关系的复杂性，以及要求信息处理的实时性，都已大大超出一般计算机的综合处理能力。对多源传感器数据压缩技术研究，可以有效的解决上述问题，这对多源传感器系统的发展起着重要意义。通过传感器采集的数据通常会进行后续处理，以获得诸如植被、森里砍伐等不同数值指标，此类数据在压缩前后不应该出现任何差异，因此本文以无损压缩算法为主要研究对象。本文首先以信息论的概念为出发点，介绍了数据压缩的概念和基础理论，其中给出了压缩算法性能评价指标，并指出了压缩的极限所在。之后，研究了基于统计的Shannon编码、Huffman编码和算术编码，基于字典的LZW算法，作了理论与算法层面分析。考虑到经典压缩算法通常只关注通用性，并不追求压缩的高效性，而本文希望针对多源传感器数据找出一种更有效的压缩算法。之后，本文通过大量研究发现DEFLATE算法编码特点与传感器数据的“近邻原则”相吻合，这是一种结合了LZ77和Huffman的算法，在理论上它应能更有效的处理多源传感器数据。之后，为了进一步针对待测数据提升算法性能，本文提出在DEFLATE算法匹配过程中，增加“副搜索”功能，这个改进有效的增加找到更长匹配串的可能性，从而进一步降低编码长度。为了将改进的DEFLATE算法与经典压缩算法作对比，本文在最后一章给出了Huffman算法、LZW、改进deflate算法压缩与解压缩的具体实现方法，通过最终对比实验发现，DEFLATE算法对多源传感器数据的压缩性能优越。关键字：多源传感器，DEFLATE算法，副搜索，Huffman算法，LZW算法Research on Lossless Compression Algorithm Based on Multi-sensor DataElectronics and Information Technology 11-1 Gong ShunwangSupervisor Wang PeiAbstractIn recent years, multi sensor system has been widely used in military, agricultural, forestry and other fields, but the ensuing problem is, the diversity of presented information and the enormous amount of information, information on the complexity of the relationship, as well as the requirements of real-time information processing, are significantly higher than general computer integrated processing capabilities. Research on multi-source data compression, can effectively solve the problem, which plays an important role in the development of multi sensor system.Data collected by sensors typically for subsequent processing in order to obtain different numerical indicators such as vegetation, forest felling, such data should not be any difference before and after compression, therefore, lossless compression algorithms are the main object of this thesis. Firstly started from the concepts of Information Theory, introduces the concept of data compression and basic theory, which gives the assessment criteria of compression algorithm performance, and points out the compressed limits. This thesis