水质监测中光谱数据的压缩与重构算法研究

Research on Compression and Reconstruction Algorithm of Spectral Data in Water Quality Monitoring

针对基于无线传感网络传输的紫外-可见光谱法水质监测光谱数据耗时耗能且传输过程存在丢失数据的关键技术问题,在传统小波变换编码的基础上,依据压缩感知框架下压缩感知之子空间追踪(subspace pursuit,SP)重构算法,将压缩感知理论应用于水质监测的光谱数据分析与处理,实现了对水质监测光谱数据的压缩与重构,提高了紫外-可见光谱法水质监测光谱数据传输过程中的抗干扰能力。实验研究结果表明,在满足测量中对水质监测精度要求的前提下,压缩比可达19.5%。同时,传输过程中可允许25%以内的丢包率,这对压缩感知算法解决水质监测光谱数据的存储和传输过程中数据量大、节点能量消耗以及传输过程中丢失数据等问题,具有一定的潜在应用价值。

For wireless sensor network transmits UV-Vis spectrum wasting time and energy. In addition, there is a loss of data in the process of data transmission. The new algorithm combines SP(subspace pursuit) reconstruction algorithm with the traditional wavelet transform coding is applied to the data analysis, not only implements spectrum data compression and re-construction, but also improves the anti-interference in the process of spectral data transmission. Experimental results show that the premise of compression ratio can reach 19.5% in the meet the requirements of project on water quality monitoring accuracy, and allow the packet loss rate within 25% in the process of transmission. This shows that compressed sensing algorithm for spectral data storage and transmission problems, node energy consumption, and losing of data in the process of the transmission has certain application value.