In this work, a parametric-based approach is proposed to perform joint range alignment and phase adjustment based on the intention of fully exploiting the energy of all the scatterers in the moving ...target and the two-dimensional coherent accumulation gain of both range and azimuth compressions. To that end, first, translational motion is modeled as a polynomial signal, and inspired by the fact that all the scatterers in the moving target experience the same translational range history, the phase difference operation and keystone transform (KT) are utilized to transform the energy of all the scatterers into one range cell. Second, by the virtue of the fractional Fourier transform (FrFT), the energy of all the scatterers is coherently accumulated into a peak point, and from which the polynomial coefficients can be obtained accurately. With the estimated polynomial coefficients, the dechirp operation and KT are applied jointly to compensate range misalignment and phase error. The analysis of the proposed method shows that it is of low computational complexity due to avoiding multidimensional search and improves the output SNR providing satisfactory low SNR performance. The experimental results are provided to demonstrate the performance of the proposed method compared with the state-of-the-art algorithms.
False data injection (FDI) attacks, as a new class of cyberattacks, bring a severe threat to the security and reliable operation of the smart grid by damaging the state estimation of the power ...system. To address this issue, an extreme learning machine (ELM)-based one-class-one-network (OCON) framework is proposed for detecting the FDI attacks in this paper. Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on the IEEE 14 bus system using real load data from New York independent system operator. The simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks but also efficiently recovers the data injected by false data.
•Centrifuge shaking-table tests were conducted on shield tunnel models.•Effects of vertical motion and segmental lining were investigated.•Vertical excitation modified the propagation of the ...horizontal seismic motion.•Tunnel seismic responses were considerably influenced by vertical excitation.•Dynamic soil-structure interaction was considerably influenced by segmental lining.
A series of centrifuge shaking-table tests were carried out to investigate the seismic responses of shield tunnels in dry sand. Both horizontal and horizontal plus vertical seismic excitations were considered in the tests. Shield tunnel models consisting of segments connected by bolts were employed, and continuous tubes were used as a comparison in the testing program. The results showed that the dynamic bending moments of the tunnel models were significantly influenced by the input seismic excitations and the type of tunnel models. Vertical seismic excitation might considerably increase the dynamic bending moments in the lining, and the segmental models had different moment distributions from the continuous ones. The joint opening displacements of the segment tunnel models, the ground accelerations and the earth pressures were also measured in the centrifuge shaking table tests, which further highlight the influences of the vertical excitations and the segmental characteristics of shield tunnel linings.
The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart ...devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.
This letter addresses the problem of synthetic aperture radar (SAR) image recovery in the presence of radio frequency interference (RFI), which degrades SAR image quality if it is not effectively ...suppressed. In this letter, the RFI is modeled as the superposition of multiple complex sinusoids such that the RFI suppression problem is transformed to a frequency estimation problem. To accurately estimate the amplitudes of the sinusoids and their corresponding frequencies in the case of a low number of range samples, the frequency sparsity in the frequency domain is successfully exploited. From the estimated amplitudes and frequencies, the RFI can be reconstructed and then used for suppression. To recover the signal of interest (SOI) and by utilizing the estimated RFI, a joint estimation is derived to simultaneously perform the RFI suppression and the SOI recovery. This joint approach can effectively suppress the RFI even if it overlaps with the SOI in both the time and frequency domains. The common threshold decision approach is not required for our joint estimation to reduce the RFI. Simulation results and real-world experiments are presented to demonstrate the superior performance of the joint estimation algorithm.
The inverse synthetic aperture radar (ISAR) imaging for nonuniformly rotating target has always been a challenging task due to the time-varying Doppler parameter, especially in the low ...signal-to-noise ratio (SNR) environment. In this paper, a novel ISAR imaging algorithm for nonuniformly rotating targets in the low SNR environment based on the parameter estimation approach is presented. First, the received signal in this work in a range bin is modeled as a multicomponent cubic phase signal (CPS) after motion compensation. Two approaches, namely coherently integrated modified cubic phase function (CIMCPF) and coherently integrated modified high-order ambiguous function (CIMHAF), are proposed to, respectively, estimate the second-order and the third-order coefficients in the CPS. Thanks to the coherent integrations developed in both CIMCPF and CIMHAF, they demonstrate excellent low SNR performance. Moreover, to efficiently implement the proposed approach, the nonuniform fast Fourier transform (NUFFT) is utilized in this work. Due to the usage of the NUFFT, the computational cost is reduced, and the search procedure also dispenses with the nonuniformly spaced signal. Finally, CIMCPF and CIMHAF are applied to produce ISAR image for a maneuvering target based on the CPS model. Several numerical examples, analyses of the noise tolerance performance for the proposed approaches, and ISAR imaging results demonstrate the superior performance of the proposed method.
Abstract
Land-based delivery platform technology for artillery weapons is a comprehensive technology to study the adaptability of artillery weapon and land-based delivery platform, and its function ...is to is to provide transportation and loading platform for artillery and realize tactical mobility and battle mobility of artillery. This paper reviews the important technical progress of China’s artillery weapons in land-based delivery platform technology, contrasts and analyses the research progress at home and abroad, and outlooks the development trend and countermeasures in the field of land-based delivery platform technology for China’s artillery weapons.
The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of ...low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.
Thanks to the excellent feature representation capabilities of neural networks, target detection methods based on deep learning are now widely applied in synthetic aperture radar (SAR) ship ...detection. However, the multi-scale variation, small targets with complex background such as islands, sea clutter, and inland facilities in SAR images increase the difficulty for SAR ship detection. To increase the detection performance, in this paper, a novel deep learning network for SAR ship detection, termed as attention-guided balanced feature pyramid network (A-BFPN), is proposed to better exploit semantic and multilevel complementary features, which consists of the following two main steps. First, in order to reduce interferences from complex backgrounds, the enhanced refinement module (ERM) is developed to enable BFPN to learn the dependency features from the channel and space dimensions, respectively, which enhances the representation of ship objects. Second, the channel attention-guided fusion network (CAFN) model is designed to obtain optimized multi-scale features and reduce serious aliasing effects in hybrid feature maps. Finally, we illustrate the effectiveness of the proposed method, adopting the existing SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0). Experimental results show that the proposed method is superior to the existing algorithms, especially for multi-scale small ship targets under complex background.
This paper aims at achieving a joint estimation of direction-of-arrival (DOA) and array perturbations, such as gain and phase uncertainty, mutual coupling, and sensor location error, which ...deteriorate the performance of the DOA estimation if not carefully handled. To that end, in this paper, the array perturbations represented by a perturbation matrix as multiplicative noise to the array manifold are then reformulated to facilitate the perturbation compensations. One great finding on the perturbation matrix is that it is a sparse matrix, which contains a lot of zero elements and only few nonzero elements. With this reformulation, the perturbation compensation problems turn into sparse matrix completion problems. Then, by utilizing the sparsity of both the DOAs and perturbation matrix, a joint estimation of DOAs and array perturbations is proposed under a unified optimization framework. In addition, numerical studies are presented to demonstrate the effectiveness of the joint estimation.
PDF
