Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Deep Learning for Covert Communication

In recent years,deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance.In this paper,we employ deep learning to establish covert communication systems,enabling the transmission of signals through high-power signals present in the prevailing environment while maintaining covertness,and propose a convolutional neural network(CNN)based model for covert communication receivers,namely Deep CCR.This model leverages CNN to execute the signal separation and recovery tasks commonly performed by traditional receivers.It enables the direct recovery of covert information from the received signal.The simulation results show that the proposed Deep CCR exhibits significant advantages in bit error rate(BER)compared to traditional receivers in the face of noise and multipath fading.We verify the covert performance of the covert method proposed in this paper using the maximum-minimum eigenvalue ratio-based method and the frequency domain entropy-based method.The results indicate that this method has excellent covert performance.We also evaluate the mutual influence between covert signals and opportunity signals,indicating that using opportunity signals as cover can cause certain performance losses to covert signals.When the interference-tosignal power ratio(ISR)is large,the impact of covert signals on opportunity signals is minimal.

Anti-self-discharge ultrathin all-inorganic electrochromic asymmetric supercapacitors enabling intelligent and effective energy storage

Electrochromic asymmetric supercapacitors(EASs), incorporating electrochromic and energy storage into one platform, are extremely desirable for next-generation civilian portable and smart electronic devices. However, the crucial challenge of their fast self-discharge rate is often overlooked, although it plays an important role in practical application. Unfortunately, very limited research on EAS has focused on this critical problem. Here, an ultrathin all-inorganic EAS with excellent anti-self-discharge performance and superior electrochromic behavior is designed and manufactured by introducing a thin nanofunctional layer at the electrode/electrolyte interface. The prototype all-inorganic EAS exhibited a wide working voltage of 2.2 V, a high energy/power density(81.2mWh·cm^(-3)/0.688 W·cm^(-3)and 30.6 mWh·cm^(-3)/11.02W·cm^(-3)), along with outstanding electrochemical and electrochromic performance even at high temperatures.Remarkably, the introduced Ta2O5layer can efficiently prohibit the redistribution and diffusion of the movable ions at the fully charged state, endowing the all-inorganic EAS with a tardy self-discharge rate of 12.6 mV·g^(-1),which is an extremely low value when compared with previous reported research. Significantly, the ultrathin allinorganic EASs could also well maintain a slow self-discharge rate and their original electrochemical characteristics under various environmental temperatures. We envision that the novel strategy of electrode/electrolyte interface engineering can effectively deal with the severe self-discharge challenge of EAS, and provide more opportunities for their practical applications.