Two empirical indoor‐to‐outdoor path loss models to facilitate femtocell network deployment are derived from continuous wave power measurements. A large set of indoor–outdoor transmitter locations in two residential streets in an urban setting and operating at 900 MHz, 2 GHz, 2.5 GHz and 3.5 GHz have been used to derive the model parameters by using singular value decomposition (SVD). The path loss models have been compared and validated against existing models as well as independent measurement data and good comparison is shown. The root mean square error of the residual path loss data obtained from the measurement data, which directly relates to the channel shadowing characteristics, is compared and validated with known results and has led to new model parameters being proposed. The expressions derived from the modelling can be used in system‐level simulators, as well as for shadowing interference analysis of two‐tier heterogeneous networks operating in indoor–outdoor scenarios at or close to the operating frequencies considered. In this study, the models extend the operating frequency range compared to related models and introduce SVD as a convenient means of deriving parameters from measured path loss data.

With the rapid development of earth observation satellites, on-orbit data processing is becoming more and more desirable. In this paper, a new on-orbit change detection method for Synthetic Aperture Radar (SAR) images, is proposed via an Extreme Self-paced Learning Machine (ESLM). First, a reflectivityspatial affinity is defined to measure the similarity between two segmented super-pixels, to identify the initial three groups of pixels: strictly changed, strictly unchanged and fuzzy pixels. Then a new extreme self-paced learning machine is developed, by gradually selecting the most confident changed pixels and predicting the changed pixels in an incremental pattern. Moreover, both the labeled and unlabeled samples are explored to realize semi-supervised classification. Different with the available methods, ESLM works in a selfpaced learning pattern and achieves accurate detection, for it can automatically choose the training samples and explore unlabeled samples to enhance the online prediction of changes. Therefore, ESLM has the characteristics of accurate and robust detection, parameter free, low-complexity and rapid implementation, which is very suitable for on-orbit processing. Some experiments are taken on five real benchmark datasets, and the results verify the effectiveness of ESLM. INDEX TERMS Change detection, synthetic aperture radar, extreme self-paced learning machine, affinity propagation super-pixel clustering, manifold regularizer.

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A novel online antenna array calibration method is presented in this paper for estimating direction-of-arrival (DOA) in the case of uncorrelated and coherent signals with unknown gain-phase errors. Conventional calibration methods mainly consider incoherent signals for uniform linear arrays with gain-phase errors. The proposed method has better performance not only for uncorrelated signals but also for coherent signals. First, an on-grid sparse technique based on the covariance fitting criteria is reformulated aiming at gain-phase errors to obtain DOA and the corresponding source power, which is robust to coherent sources. Second, the gain-phase errors are estimated in the case of uncorrelated and coherent signals via introducing an exchange matrix as the pre-processing of a covariance matrix and then decomposing the eigenvalues of the covariance matrix. Those parameters estimate values converge to the real values by an alternate iteration process. The proposed method does not require the presence of calibration sources and previous calibration information unlike offline ways. Simulation results verify the effectiveness of the proposed method which outperforms the traditional approaches.