Department of Geomatics Engineering, Civil Engineering Faculty, Istanbul Technical University, Istanbul, Turkey.
Sensors (Basel). 2011;11(4):3939-61. doi: 10.3390/s110403939. Epub 2011 Apr 1.
The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors' data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.
传感器所观测到的真实世界现象通常是非平稳的。分析和建模自然时间序列观测的经典线性技术效率低下，应该被理论方面和性能各异的非线性技术所取代。通过采用最合适的技术和策略，对传感器数据进行评估是至关重要的。在本研究中，应用了两种不同的时间序列分析方法，即最小二乘谱分析（LSSA）和小波分析（连续小波变换、交叉小波变换和小波相干算法作为小波分析的扩展），对土耳其国家海平面监测系统的潮汐计传感器记录的海平面观测进行了分析，并对这些方法的优缺点进行了综述。分析工作在安塔利亚-II 和埃雷利潮汐计站的海平面观测数据上进行。在分析中，检测到隐藏在噪声信号中的有用信息，并阐明了两个海平面时间序列之间的共同特征。由于仪器缺陷和停电等问题，潮汐计记录存在时间上的数据空白。考虑到具有空白数据的时频分析的困难，在分析之前，使用神经网络方法对海平面观测数据进行了预处理，预测了缺失部分。最后，记录了潮汐计传感器记录评估非平稳观测的技术的优点和局限性，并提出了一种用于连续传感器观测的分析策略。
