A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

Due to the rapid development of small satellite technology and the advantages of LEO satellite with low delay and low propagation loss as compared with the traditional GEO satellite, the broadband LEO constellation satellite communication system has gradually become one of the most important hot spots in the field of satellite communications. Many countries and satellite communication companies in the world are formulating the project of broadband satellite communication system. The broadband satellite communication system is different from the traditional satellite communication system. The former requires a higher transmission rate. In the case of high-speed transmission, if the low elevation constellation is adopted, the satellite beam will be too much, which will increase the complexity of the satellite. It is difficult to realize the low-cost satellite. By comparing the complexity of satellite realization under different elevation angles to meet the requirement of terminal speed through link computation, this paper puts forward the conception of building broadband LEO constellation satellite communication system with high elevation angle. The constraint relation between satellite orbit altitude and user edge communication elevation angle is proposed by theoretical Eq. deduction. And the simulation is carried out for the satellite orbit altitude and edge communication elevation angle.

This letter presents a novel approach for automatic multimodal affect recognition. The audio and visual channels provide complementary information for human affective states recognition, and we utilize Boltzmann zippers as model-level fusion to learn intrinsic correlations between the different modalities. We extract effective audio and visual feature streams with different time scales and feed them to two component Boltzmann chains respectively. Hidden units of the two chains are interconnected to form a Boltzmann zipper which can effectively avoid local energy minima during training. Second-order methods are applied to Boltzmann zippers to speed up learning and pruning process. Experimental results on audio-visual emotion data recorded by ourselves in Wizard of Oz scenarios and collected from the SEMAINE naturalistic database both demonstrate our approach is robust and outperforms the state-of-the-art methods.

Laser Doppler Vibrometers (LDVs) enable the acquisition of remote speech signals by measuring small-scale vibrations around a target. They are now widely used in the fields of information acquisition and national security. However, in remote speech detection, the coherent measurement signal is subject to environmental noise, making detecting and reconstructing speech signals challenging. To improve the detection distance and speech quality, this paper proposes a highly accurate real-time speech measurement method that can reconstruct speech from noisy coherent signals. First, the I/Q demodulation and arctangent phase discrimination are used to extract the phase transformation caused by the acoustic vibration from coherent signals. Then, an innovative smoothness criterion and a novel phase difference-based dynamic bilateral compensation phase unwrapping algorithm are used to remove any ambiguity caused by the arctangent phase discrimination in the previous step. This important innovation results in the highly accurate detection of phase jumps. After this, a further innovation is used to enhance the reconstructed speech by applying an improved waveform-based linear prediction coding method, together with adaptive spectral subtraction. This removes any impulsive or background noise. The accuracy and performance of the proposed method were validated by conducting extensive simulations and comparisons with existing techniques. The results show that the proposed algorithm can significantly improve the measurement of speech and the quality of reconstructed speech signals. The viability of the method was further assessed by undertaking a physical experiment, where LDV equipment was used to measure speech at a distance of 310m in an outdoor environment. The intelligibility rate for the reconstructed speech exceeded 95%, confirming the effectiveness and superiority of the method for long-distance laser speech measurement.

This paper analyzes the ergodic capacity of the MIMO multi-keyhole channel, assuming that the channel state information (CSI) is available only at the receiver. We first derive new closed-form expressions for marginal probability density function (pdf) of the single unordered eigenvalue as well as joint pdf of ordered eigenvalues of the channel matrix in a simple and general framework. With these statistical results, we then present an exact closed-form expression for the ergodic capacity. We analyze tight bounds on the exact capacity and propose a new tight lower bound. We also investigate the asymptotic capacity performances in low-signal-to-noise-ratio (SNR) and high-SNR regimes to gain further insights. All our results apply for arbitrary number of keyholes and antennas. Numerical simulations are presented to validate our theoretical analysis.