无线无源声表面波传感器超分辨率测量方法研究

The passive wireless surface acoustic wave (SAW) resonant sensors have attracted much attention in academic research and engineering application because sensors can be passive, wireless, wide temperature range, rugged, radiation hard and small. The measurement precision (triple standard deviation) of existing passive wireless SAW resonant sensors is determined by the effective time. Their low energy response signals cause short effective time. Moreover, the effective time is extremely deteriorated with increasing distance. It limits the measurement precision of SAW sensor, and makes the precision sharply falls with increasing distance. In order to promote the application of SAW sensor, this paper proposes a convex program based super-resolution frequency estimation method to recover the missing spectral line and enhance frequency resolution. This method both reduces the reliance on effective time and improves the measurement precision of SAW sensors. Especially, due to the improvement of measurement precision in a long distance, the measurement precision of SAW sensor would not drastic changes with distance varies.

无线无源声表面波(Surface Acoustic Wave, SAW)传感器具有诸如：纯无源(无需供能)、无线、适用温度范围宽(-200~1000℃)、耐用性强、体积小等优良特性，使其在学术研究和工程应用领域备受关注。有效信号时间决定了现有无线无源SAW传感器系统的测量精度(3倍标准差)，而无源SAW传感器回波信号能量低，造成其有效信号时间短且会随着距离的增加急剧衰减，这不仅束缚了SAW传感器的测量精度，还使得测量精度会随着距离的增加迅速降低。为推进SAW传感器大规模应用，本项目旨在通过凸优化恢复由于短有效信号时长而在低分辨率原始频谱中消失的谱线，增强频谱分辨率，实现具有超分辨率的回波信号频率估计，从而降低SAW传感器对有效信号时间的依赖，提高测量精度，特别是提高长距离(大于等于5m)下的测量精度，使得无线无源SAW传感器测量精度不会随距离远近剧烈变化。

为了获取超分辨率的测量信息，以原始信息为基础，通过周围环境感知和谱线恢复的方式减少无线无源声表面波(Surface Acoustic Wave, SAW)传感器对有效信号时间的依赖，提高测量精度。使得SAW传感器在工业测量、生物医疗等方面有广泛的应用前景。本项目主要研究了：(1) 超分辨率数学模型建立；（2）超分辨率数学模型的求解。.经过努力研究，取得了良好的研究进展与一些有应用价值的科研成果。.（1）建立无线无源SAW传感器的环境感知频率估计方法，该方法实时在线对回波信号的进行质量评价，根据评估结果确定数字频率有效位数，从而实现一种高精确度，高稳定性的频率估计算法。（2）获得SAW传感器的超分辨率频谱谱线估计方法，建立以全变分范数最小化为基础的代价函数，以频谱信息差值作为约束条件，利用凸规划和赫米特矩阵，通过求解三角多项式获得超分辨率频谱。