The reentry plasma sheath propagation environment is nonstationary and has high-dynamic characteristics. The communication signal suffers serious parasitic modulation effect since the time-varying degree of the channel is on the same order as the telemetry symbol rate. This paper proposes an effective transmission scheme to cancel the plasma sheath channel (PSC) distortion by utilizing the short spreading and adaptive nonstationary channel equalization technology. The proposed scheme spreads the telemetry signal and inserts a small number of pilot signals to determine whether the nonstationary state of the channel is changed by the channel amplitude attention value of the pilot signals. A novel adaptive nonstationary stochastic gradient recursive minimum mean square error (NSGR-MMSE) algorithm is then proposed to estimate the other channel response values in each current state. Finally, the channel equalization is performed to mitigate the damage from the parasitic modulation effect. The proposed method can accommodate the nonstationary characteristics of the channel and the small-scale channel distortion simultaneously with small bandwidth efficiency scarification and minimal algorithm complexity cost. The simulation results show that the proposed transmission scheme can ensure reliable communication under two typical reentry PSC models. Furthermore, the performance of the proposed adaptive NSGR-MMSE algorithm is improved by at least 3 dB versus the classic MMSE algorithm under the effective transmission scheme.

Synchronization of large acquisition bandwidth brings great challenges to the traditional second-order phase-locked loop (PLL). To address the contradiction between acquisition bandwidth and noise suppression capability of the traditional PLL, a new second-order PLL coupled with a nonlinear element is proposed. The proposed nonlinear second-order PLL regulates the loop noise bandwidth adaptively by the nonlinear module. When a large input–output phase error occurs, this PLL reduces the frequency offset quickly by taking advantage of the large bandwidth. When the phase error is reduced by the loop control, the proposed PLL suppresses noises by using the small bandwidth to increase the tracking accuracy. Simulation results demonstrate that the tracking speed of the proposed PLL is increased considerably, and its acquisition bandwidth is increased to 18.8 kHz compared with that of the traditional second-order PLL (4 kHz).

Due to the severe impact of dynamic plasma sheath on electromagnetic (EM) waves, the communication quality of hypersonic vehicles deteriorates, even resulting in communication blackouts during the reentry process. In this article, the hypersonic vehicle downlink integrated channels that cascade the plasma sheath channel and Rice fading channel, i.e., the downlink telemetry channels, are first established, and then the demodulation method and performance of typical telemetry modulations including the M-ary phase shift keying (MPSK), M-ary amplitude shift keying (MASK), and M-ary frequency shift keying (MFSK) are studied. Considering the high dynamics of the plasma sheath, the pilot signals are used to detect the signal correctly for MPSK, and the non-coherent demodulation is adopted for MASK and MFSK. Especially, an adaptive maximum likelihood (ML) receiver for ASK is proposed based on a reduced reversible jump Markov chain Monte Carlo (RRJ-MCMC) algorithm to estimate the probability density function (pdf) of the received signal. The simulation results show that the communication performance of PSK and ASK is improved with the modulation order M decreasing; the performance of non-coherent FSK is improved with the modulation order M increasing. Meanwhile, the performance follows the order of NC-MFSK (M ≥ 8) > BPSK > NC-4FSK > NC-2FSK > 2ASK. For NC-8FSK, when the electron density peak value Ne $_{{peak}} = {2$ , 4, 6, 8, 10} x 10¹⁸ m⁻³, the signal to noise ratio (SNR) required to reduce the bit error rate (BER) to 10⁻⁶ are 17, 18, 19, 21, and 34 dB, respectively.

Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspectral dataset requires huge resources. To tackle these problems, this paper proposes a new method with low requirements for the scale of the dataset that involves correcting the inter-class differences of hyperspectral images and eliminating the redundant information of spatial–spectral features. Firstly, the algorithm introduces the spatial information of hyperspectral images into the classification task through the superpixel definition based on the entropy rate, which not only reduces the spatial redundancy information of hyperspectral images but also uses the similarity of spatial neighbors to increase the differences between classes and alleviate the differences within classes. Secondly, under the theoretical guidance of similar spectral fluctuation trends of similar objects, a feature extraction method based on fast Fourier transform is proposed to alleviate spectral feature redundancy and further eliminate the inter-class differences of the algorithm. Finally, to verify the improvement effect of the proposed idea on the traditional classification method, the idea was applied to the traditional SVM algorithm, and experiments were carried out based on the PaviaU and Indian Pines hyperspectral datasets. The simulation results show that the proposed (ERS–FFT–SVM) algorithm shows a significant improvement in classification accuracy when compared with the traditional classification algorithm and is able to perform the small sample classification of hyperspectral images.