In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitable for analyzing these radar echo signals with micro-Doppler (m-D) components because of its multiresolution characteristics. In this paper, S-transform and the corresponding synchrosqueezing method are used to analyze the ISAR echo signal and perform imaging. Synchrosqueezing is a post-processing method for the time-frequency analysis result, which could retain most merits of S-transform while significantly improving the readability of the S-transformation result. The results of various simulations and actual data will show that S-transform is highly matched with the echo signal for ISAR imaging: the better frequency-domain resolution at low frequencies can concentrate the energy of the rigid body components in the low-frequency region, and better time resolution at high frequencies can better describe the transformation of the m-D component over time. The combination with synchrosqueezing also significantly improves the effect of time-frequency analysis and final imaging, and alleviates the shortcomings of the original S-transform. These results will be able to play a role in subsequent work like feature extraction and parameter estimation.

The Outer Burial Pits of the Han Dynasty Yang Mausoleum is the first fully enclosed site museum in China. The Internet of things sensor installed in the pavilion has accumulated more than 7,000,000 heterogeneous data. Traditional algorithms, such as temperature prediction model, only use statistical value to predict the trend of temperature change, so the data utilization rate is insufficient. In addition, the accuracy of prediction model is also relatively low. The extreme learning machine is a single layer feedback neural network learning method. Using extreme learning machine to analyze and model data is an effective prediction algorithm for preventive protection. However, due to the mismatch between solar calendar characteristics and temperature changes, the accuracy of the prediction model is unsatisfactory. To solve the above problem, a temperature prediction model based on lunar calendar characteristics is proposed in this paper. A large number of measured data show that the model can more accurately predict the future temperature variation trend and detailed characteristics. Its performance is better than that of the model based on solar calendar characteristics. In this paper, a new prediction model is proposed. The advantages of lunar calendar in natural data recording and processing are preliminarily verified, which provides reference for the label selection of follow-up monitoring data.

Under the same principle, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length, as the characteristic of multi-resolution, S transform could reduce the influence of the micro-Doppler component and enhance the imaging effect. This paper presents a method for micro-Doppler feature extraction in Inverse Synthetic Aperture Imaging Laser Radar (ISAIL) imaging. It is accessible and comprehensive, applying Random Sample Consensus (RANSAC) for the separation and reconstruction of micro-Doppler and rigid body signals. Experiments show that the method can effectively remove the micro-Doppler information and obtain a clear target distance-instantaneous Doppler image.