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Compressive sensing (CS) shows that, when a signal is sparse or compressible with respect to some basis, a small number of compressive measurements of the original signal can be sufficient for exact (or approximate) recovery. Distributed CS (DCS) takes advantage of both intra-and intersignal correlation structures to reduce the number of measurements required for multisignal recovery. In most cases of audio signal processing, only mixtures of the original sources are available for observation under the DCS framework, without prior information on both the source signals and the mixing process. To recover the original sources, estimating the mixing process is a key step. The underlying method for mixing matrix estimation reconstructs the mixtures by a DCS approach first and then estimates the mixing matrix from the recovered mixtures. The reconstruction step takes considerable time and also introduces errors into the estimation step. The novelty of this paper lies in verifying the independence and non-Gaussian property for the compressive measurements of audio signals, based on which it proposes a novel method that estimates the mixing matrix directly from the compressive observations without reconstructing the mixtures. Numerical simulations show that the proposed method outperforms the underlying method with better estimation speed and accuracy in both noisy and noiseless cases.Index Terms-Audio signals, distributed compressive sensing (DCS), independent component analysis (ICA), kurtosis, mixing matrix estimation.

Path planning is a global optimization problem aims to program the optimal flight path for Unmanned Aerial Vehicle (UAV) that has short length and suffers from low threat. In this paper, we present a Mixed-Strategy based Gravitational Search Algorithm (MSGSA) for the path planning. In MSGSA, an adaptive adjustment strategy for the gravitational constant attenuation factor alpha (α) is presented firstly, in which the value of α is adjusted based on the evolutionary state of the particles. This helps to adaptively balance the exploration and exploitation of the algorithm. In addition, to further alleviate the premature convergence problem, a Cauchy mutation strategy is developed for MSGSA. In this strategy, only when the global best particle cannot be further improved for several times the mutation is executed. In the MSGSA based path planning procedure, we construct an objective function using the flight length cost, threat area cost, and turning angle constraint to decrease the flight risk and obtain the smoother path. For performance evaluation, the MSGSA is applied to two typical simulated flight missions with complex flight environments, including user-defined forbidden flying areas, Radar, missile, artillery and anti-aircraft gun. The obtained flight paths are compared with that of the standard Gravitational Search Algorithm (GSA) and two improved variants of GSA, i.e. gbest-guided GSA (GGSA), and hybrid Particle Swarm Optimization and GSA (PSOGSA). The experimental results demonstrate the superiority of the MSGSA based method in terms of the solution quality, robustness, as well as the constraint-handling ability.

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In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

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