As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers’ attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.

As the number of satellite emergency imaging tasks grows, the main goal of satellites becomes putting forward solutions and meeting users' demands in a relatively short time. This study aims to investigate the problem of emergency task planning for agile satellites. Through the analysis of the problem and its constraints, a model of emergency task autonomous planning was implemented. According to the characteristics of emergency tasks, a strategy to deal with the tasks of different emergency levels and various quantities was proposed. We put forward three algorithms for quick insertion of emergency tasks, i.e., emergency task insertion algorithm (ETIA), general emergency task insertion algorithm (GETIA), and general emergency task planning &insertion algorithm (GETPIA). The experimental result showed that the strategy and algorithms can not only respond quickly to observation tasks but also produce effective planning programs to ensure the successful completion of observation tasks.

The requirement for specific indoor location is more and more pressing. However, traditional approaches can only achieve a low accuracy. In this paper, we put forward a new approach to evaluate the target position with the acoustical Linear Frequency Modulation Signal(LFM). Comparing with other acoustical approaches, it is robust to noise, and most importantly it can realize a high accuracy and a good real-time performance. Besides that, several innovative location methods are also compared and verified to promote the real-time, accurate and robust performance.