海洋环境监测传感器网络是典型的能量和带宽受限系统，大规模、高分辨率的信息采集需求和受限的网络传输能力之间的严重不对称性，是制约该网络发展的瓶颈问题。传统信息获取方法难以根本解决这一难题，而压缩感知理论为这一瓶颈的突破提供了契机。本项目以压缩感知为理论基础，构建海天一体化海洋环境监测传感器网络的系统模型；然后在压缩感知框架下，以海洋环境监测信息获取流程为线索，突破水下信道多址接入、多路径多跳路由、海面汇聚节点选择、自适应分辨率信息重构等关键技术；最后构建系统级仿真平台和原理性演示验证系统，以验证整体性能，并为将来成果转化奠定基础。本项目旨在利用被测环境要素在相关处理域上的稀疏性，充分挖掘压缩感知在降低网络能耗、带宽需求和实现复杂度方面的潜在优势，从技术体制上有效提高网络资源利用率和海洋环境监测分辨率，从而为下一代大规模、立体化、高分辨率的海洋环境监测传感器网络的建设提供一种有价值的参考方案。

The ocean environmental monitoring sensor network is a typically energy-limited and bandwidth-limited system, the technical bottleneck of which is the asymmetry between the demand for large-scale and high-resolution information gathering and the limited network transmission capability. The traditional information acquisition methods can not solve the above problem radically; while the newly arising compressed sensing theory provides a chance for breaking through the bottleneck. Based on the compressed sensing theory, this project will first build the system model of the ocean environmental monitoring sensor networks with integrated ocean-sky architecture. And then, under the framework of compressed sensing and following the thread of the entire process for ocean environmental monitoring information acquisition, this project will solve the key techniques including the multiple access method in underwater channel, the multi-path and multi-hop routing algorithm, the selection method for sea surface sink node, and the information reconstruction method with adaptive resolution. Finally, the system-level simulation platform and the theoretical demonstration system will be constructed to validate the whole performance and to establish the base for the future achievement transformation. By making full use of the sparsity of the measured elements in the certain domains and exploiting the essential advantages of compressed sensing in reducing the network energy consumption, bandwidth cost and system complexity, this project aims at effectively improving the utilization of network resources and the resolution of ocean environmental monitoring, and accordingly providing a valuable reference scheme for the next generation large-scale, three-dimensional and high-resolution ocean environmental monitoring sensor networks.