Mid-wavelength infrared (MWIR) detection and long-wavelength infrared (LWIR) detection constitute the key technologies for space-based Earth observation and astronomical detection. The advanced ability of infrared (IR) detection technology to penetrate the atmosphere and identify the camouflaged targets makes it excellent for space-based remote sensing. Thus, such detectors play an essential role in detecting and tracking low-temperature and far-distance moving targets. However, due to the diverse scenarios in which space-based IR detection systems are built, the key parameters of IR technologies are subject to unique demands. We review the developments and features of MWIR and LWIR detectors with a particular focus on their applications in space-based detection. We conduct a comprehensive analysis of key performance indicators for IR detection systems, including the ground sampling distance (GSD), operation range, and noise equivalent temperature difference (NETD) among others, and their interconnections with IR detector parameters. Additionally, the influences of pixel distance, focal plane array size, and operation temperature of space-based IR remote sensing are evaluated. The development requirements and technical challenges of MWIR and LWIR detection systems are also identified to achieve high-quality space-based observation platforms.
We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.
Phone number recycling (PNR) refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner. It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms. Specifically, a new owner of a reassigned number can access the application account with which the number is associated, and may perform fraudulent activities. Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users. Thus, alternative solutions that depend on only the information of the applications are imperative. In this work, we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers. Our analysis on Meituan’s real-world dataset shows that compromised accounts have unique statistical features and temporal patterns. Based on the observations, we propose a novel model called temporal pattern and statistical feature fusion model (TSF) to tackle the problem, which integrates a temporal pattern encoder and a statistical feature encoder to capture behavioral evolutionary interaction and significant operation features. Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the baselines, demonstrating its effectiveness in detecting compromised accounts due to reassigned numbers.
Many areas are now experiencing data streams that contain privacy-sensitive information. Although the sharing and release of these data are of great commercial value, if these data are released directly, the private user information in the data will be disclosed. Therefore, how to continuously generate publishable histograms (meeting privacy protection requirements) based on sliding data stream windows has become a critical issue, especially when sending data to an untrusted third party. Existing histogram publication methods are unsatisfactory in terms of time and storage costs, because they must cache all elements in the current sliding window (SW). Our work addresses this drawback by designing an efficient online histogram publication (EOHP) method for local differential privacy data streams. Specifically, in the EOHP method, the data collector first crafts a histogram of the current SW using an approximate counting method. Second, the data collector reduces the privacy budget by using the optimized budget absorption mechanism and adds appropriate noise to the approximate histogram, making it possible to publish the histogram while retaining satisfactory data utility. Extensive experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage costs and improves data utility compared to other existing algorithms.
This study investigates how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems. First, a deception attack with limited energy (DALE) is introduced under the framework of distributed extended Kalman consensus filtering (DEKCF). Next, a hypothesis testing-based mechanism to detect the abnormal data generated by DALE, in the presence of the error term caused by the linearization of the nonlinear system, is established. Once the DALE is detected, a new rectification strategy can be triggered to recalibrate the abnormal data, restoring it to its normal state. Then, an attack-resilient DEKCF (AR-DEKCF) algorithm is proposed, and its fusion estimation errors are demonstrated to satisfy the mean square exponential boundedness performance, under appropriate conditions. Finally, the effectiveness of the AR-DEKCF algorithm is confirmed through simulations involving multi-unmanned aerial vehicle (multi-UAV) tracking problems.
This paper concerns the event-triggered distributed cross-dimensional formation control problem of heterogeneous multi-agent systems (HMASs) subject to limited network resources. The central aim is to design an effective distributed formation control scheme that will achieve the desired formation control objectives even in the presence of restricted communication. Consequently, a multi-dimensional HMAS is first developed, where a group of agents are assigned to several subgroups based on their dimensions. Then, to mitigate the excessive consumption of communication resources, a cross-dimensional event-triggered communication mechanism is designed to reduce the information interaction among agents with different dimensions. Under the proposed event-based communication mechanism, the problem of HMAS cross-dimensional formation control is transformed into the asymptotic stability problem of a closed-loop error system. Furthermore, several stability criteria for designing a cross-dimensional formation control protocol and communication schedule are presented in an environment where there is no information interaction among follower agents. Finally, a simulation case study is provided to validate the effectiveness of the proposed formation control protocol.
This article investigates the event-triggered adaptive neural network (NN) tracking control problem with deferred asymmetric time-varying (DATV) output constraints. To deal with the DATV output constraints, an asymmetric time-varying barrier Lyapunov function (ATBLF) is first built to make the stability analysis and the controller construction simpler. Second, an event-triggered adaptive NN tracking controller is constructed by incorporating an error-shifting function, which ensures that the tracking error converges to an arbitrarily small neighborhood of the origin within a predetermined settling time, consequently optimizing the utilization of network resources. It is theoretically proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), while the initial value is outside the constraint boundary. Finally, a single-link robotic arm (SLRA) application example is employed to verify the viability of the acquired control algorithm.
A low-profile dual-broadband dual-circularly-polarized (dual-CP) reflectarray (RA) is proposed and demonstrated, supporting independent beamforming for right-/left-handed CP waves at both K-band and Ka-band. Such functionality is achieved by incorporating multi-layered phase shifting elements individually operating in the K- and Ka-band, which are then interleaved in a shared aperture, resulting in a cell thickness of only about 0.1λL. By rotating the designed K- and Ka-band elements around their own geometrical centers, the dual-CP waves in each band can be modulated separately. To reduce the overall profile, planar K-/Ka-band dual-CP feeds with a broad band are designed based on the magnetoelectric dipoles and multi-branch hybrid couplers. The planar feeds achieve bandwidths of about 32% and 26% at K- and Ka-band respectively with reflection magnitudes below −13 dB, an axial ratio smaller than 2 dB, and a gain variation of less than 1 dB. A proof-of-concept dual-band dual-CP RA integrated with the planar feeds is fabricated and characterized which is capable of generating asymmetrically distributed dual-band dual-CP beams. The measured peak gain values of the beams are around 24.3 and 27.3 dBic, with joint gain variation <1 dB and axial ratio <2 dB bandwidths wider than 20.6% and 14.6% at the lower and higher bands, respectively. The demonstrated dual-broadband dual-CP RA with four degrees of freedom of beamforming could be a promising candidate for space and satellite communications.
The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant band-width and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.