基于盲自适应KLT的蒸发波导压缩感知方法

蒸发波导既可促成微波通信、雷达等系统超视距工作，又可能造成异常盲区，因此获知蒸发波导的时空态势是夺取海上制电磁权的关键.若仅靠增大传感器布设密度提升感知分辨率，则费效比高且提升空间有限.压缩感知为从相对稀少的观测数据中获知蒸发波导态势提供了可能.本文提出盲自适应KLT（Karhunen-Loéve Transform）追踪算法，通过少量观测数据，充分挖掘蒸发波导的稀疏性，准确恢复出蒸发波导的分布.理论分析和实验表明，新方法总体性能优于基于DCT（Discrete Cosine Transform）和传统KLT的对照组性能，且新方法在节省九成采样资源的前提下，最终的重构结果能够达到重构信噪比30dB的水平，为海上长时间、大范围蒸发波导态势感知提供了压缩采集的基础.

Compressed Sensing of Evaporation Duct Based on Blind Adaptive KLT Estimation

Evaporation duct helps in the over-the-horizon operating of the communication,radar systems,etc.at the microwave frequency band.In addition,it causes abnormal blind areas,too.Therefore,the evaporation duct situation acquisition is the key to seize the mastery of the electromagnetic.However,if the density of the sensor is increased to improve the sensing resolution,the cost is high and the improvement is limited.Compressed sensing (CS) provides the theoretical basis for the awareness of evaporation duct,which is recovered from a small number of low speed measurements.The blind adaptive Karhunen-Loéve transform (BAKLT) pursuit is able to fully exploit the sparsity and reconstruct the time and space situation of the evaporation duct.The analysis and simulation demonstrate that the BAKLT evaporation duct situational awareness accuracy is better than that of the control group using discrete cosine transform.The reconstructed result of the proposed method is able to reach the reconstructed SNR level of 30dB saving 90% of the sampling resources,and provides the compression basis for the full time global evaporation duct situation acquisition.