Uncrewed Aerial Vehicle Detection: A Channel State Information Anomaly-Based Approach

The performance of networks is greatly affected by the available network state information, such as network topology, channel state, and traffic information. Previous research on network performance analysis is based on the assumption that complete and precise network state information is available. However, in reality, precise information is difficult to obtain, and it requires a large amount of bandwidth resource to maintain accurate information. In this paper, we study networks with memoryless states and address the following question: Given the true network state and a performance measure, what is the minimum information required to achieve a given network performance? We propose a general information-theoretic framework, which can be applied to memoryless network and network protocol, to study the effect of information on network performance. We find that the minimum required information is equal to the mutual information between the true network state and the decision of the controller. To illustrate our approach, we use the framework to determine the lower bound on the channel state information in wireless networks, and then find the coding rate to achieve optimal net data rate. We also study the lower bound on the traffic information required in a distributed network, propose an encoding scheme for exchanging the state information, and then study the gap between the performance of the proposed scheme and the theoretical bound.

[Abstract ] The Virtual UAV Leader is a new co ncept for semi autonomous control of UAV (Un inhabit ed Aerial Vehicles) swarms that bridges the gap between conventional manual or waypoint control of a single UAV and fully autonomous, cooperative control of UAVs in large numbers. The development rational e for this concept, its architecture, and the implications for its operational use are discussed. The Virtual UAV Leader is proposed as a simulation of a UAV whose state information can provide a reference for multiple real UAVs in a squadron or swarm. C ontrol of the real UAVs would be divided into two major modes, Referenced and Autonomous, which would be selectable by the ground commander. The reference mode of control would send deterministic “delta commands” relative to the state of the Virtual UAV Leader to the squadron members to perform maneuvers such as formation flying. The Autonomous control mode, when invoked by the ground commander, would allow the UAVs to control themselves based on high level mission objectives using non deterministic algo rithms. The key advantage of the Virtual UAV Leader is its intrinsic override capability that enables the operator to “call back” the autonomous swarm and return the UAVs to orderly, deterministic control. It is asserted that this capability enables a me thod of incremental testing of stochastic control systems that has not been available previously and will aid the verification of “behavioral -based” non deterministic control systems, which cannot be satisfactorily verified by current verification tools. Additionally, formation flying could be operationally performed under the Referenced mode. Examples of such operations include 1) a longitudinal “string” of UAVs flying over a point of interest to obtain time lapse photographs of a developing situation an d 2) a lateral “spread” of UAVs flying over a front line to gather simultaneous video across the entire front. While a physical UAV leader is subject to battle damage and destruction, the Virtual Leader can never be shot down -- thereby providing a stabile , robust reference for swarm operations. Initial implementation of this concept will place the simulation in the ground station; ultimate implementations would place the Virtual UAV Leader onboard each UAV and be shared in a cooperative, autonomous system . The Virtual UAV Leader would enable operation of multiple UAVs from a single ground control station, keep the human intelligence close to the operation, and provide a practical system that can be deployed in the near term both for developmental testing and for field operations.

In this paper, a class of state-dependent relay channel with orthogonal channels from the source to the relay and from the source and the relay to the destination is studied. The two orthogonal channels are corrupted by two independent channel states S R and S D, respectively, which are known to both the source and the relay. The lower bounds on the capacity are established for the channel either with non-causal channel state information or with causal channel state information. Further, we show that the lower bound with non-causal channel state information is tight if the receiver output Y is a deterministic function of the relay input X r, the channel state S D, and one of the source inputs X D, i.e., Y = f(X D, X r, S D), and the relay output Y r is restricted to be controlled by only the source input X R and the channel state S R, i.e., the channel from the source to the relay is governed by the conditional probability distribution P Y r | X R , S R . The capacity for this class of semi-deterministic orthogonal relay channel is characterized exactly. The results are then extended to the Gaussian cases, modeling the channel states as additive Gaussian interferences. The capacity is characterized for the channel when the channel state information is known non-causally. However, when the channel state information is known causally, the capacity cannot be characterized in general. In this case, the lower bound on the capacity is established, and the capacity is characterized when the power of the relay is sufficiently large. Some numerical examples of the causal state information case are provided to illustrate the impact of the channel state and the role of the relay in information transmission and in cleaning the channel state.

<p class="keywords">Currently increasing UAV operation significantly changes the view of conventional aviation. Unmanned aerial vehicles have become part of air traffic and therefore, its operation should be adequately controlled through related legislative framework and law enforcement procedures. Considering the fact, that single unmanned aircrafts will be soon replaced by swarms, it is necessary to get prepared for all possible UAs applications and define all rules including also emergency and law enforcement procedures in case that public safety is endangered.</p><p class="keywords">This paper summarizes recent regulatory framework for UAVs in EU and US and points out a concealed weakness of legislative requirements. The legislative scope addressed in this paper is limited primarily to civil aviation. The second part stresses the security threat created by an uncontrolled or violently-controlled UA. Aerial vehicles detection and disposal methods are described in the last part of paper.</p>

This article focuses on aerial vehicle detection and recognition by a wide field of view monocular vision system that can be installed on UAVs (unmanned aerial vehicles). The objects are mostly observed on the background of clouds under regular daylight conditions. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The described algorithm is able to detect small targets, but unlike many other approaches is designed to work with large-scale objects as well. The suggested algorithm is also intended to recognize and track the aerial vehicles of specific kind using a set of reference objects defined by their 3D models. For that purpose a computationally efficient contour descriptor for the models and the test objects is calculated. An experimental research on real video sequences is performed. The video database contains different types of aerial vehicles: airplanes, helicopters, and UAVs. The proposed approach shows good accuracy in all case studies and can be implemented in onboard vision systems.

We study the content delivery problem between a transmitter and two receivers through erasure links, when each receiver has access to some random side-information about the files requested by the other user. The random side-information is cached at the receiver via the decentralized content placement. The distributed nature of receiving terminals may also make the erasure state of two links and indexes of the cached bits not perfectly known at the transmitter. We thus investigate the capacity gain due to various levels of availability of channel state and cache index information at the transmitter. More precisely, we cover a wide range of settings from global delayed channel state knowledge and a non-blind transmitter (i.e. one that knows the exact cache index information at each receiver) all the way to no channel state information and a blind transmitter (i.e. one that only statistically knows cache index information at the receivers). We derive new inner and outer bounds for the problem under various settings and provide the conditions under which the two match and the capacity region is characterized. Surprisingly, for some interesting cases the capacity regions are the same even with single-user channel state or single-user cache index information at the transmitter.

In a fading communication channel, it is often beneficial to feedback Channel State Information (CSI) and Receiver State Information (RSI) to the transmitter. The CSI generally refers to information about the channel condition available at the receiver, while the RSI in this work is defined as information about the receive’s estimate of the message. The RSI can be used to improve reliability, e.g., through retransmission. This paper considers multi-carrier transmission through a doubly-selective Rayleigh fading channel, and studies the trade-off between the feedback of CSI and RSI under total feedback constraint. In particular, the CSI feedback specifies which groups of sub-channels to activate with equal power, and the RSI feedback determines retransmissions of a codeword. The problem is how to allocate feedback bits between CSI and RSI in order to maximize the error probability exponent. It is found that the optimal trade-off exhibits phase transitions which depends critically on the coherence time and the total amount of feedback. Specifically, the first feedback bits should be CSI up to a critical amount. Additional feedback bits, if available, should be allocated to RSI first, and then to both CSI and RSI. For the model considered, as the amount of feedback exceeds a certain threshold, additional RSI feedback is not beneficial unless CSI feedback increases accordingly.

In many domestic and military applications, aerial vehicle detection and super-resolution algorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle Detection Network (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles from low-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scale Generative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasing resolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x using MsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss to encourage the target detector to be sensitive to the subsequent super-resolution training. The network jointly learns hierarchical and discriminative features of targets and produces optimal super-resolution results. We perform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTA datasets. The experimental results show that our proposed framework achieves better visual quality than the state-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy of aerial vehicle detection.

Vehicle detection in aerial images has a wide range of applications, and the majority of vehicle detection methods use the bounding-box approach for localization. But the bounding-box approach usually yields low precision and recall rates, especially under the dense vehicle density situations where the close spatial proximity of the vehicles confuses the bounding-box detectors. This letter proposes a Fully Convolutional Lightweight Pyramid Network (FCLPN) to detect vehicles in visible-spectrum aerial images. Unlike the bounding-box approach, FCLPN performs pixel-level localization and classification. FCLPN trained on the DLR-3K dataset is directly tested on DLR-3K, VEDAI, COWC, and VAID datasets to validate its generalization strength. Experimental results show that FCLPN performs better than state-of-the-art methods in aerial vehicle detection in terms of Precision, F1 score, and mAP.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through fullspace signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

The design of spatio-temporal power allocation schemes is considered for space-time coding over spatially correlated multiple-input multiple-output (MIMO) channels. The focus is on linear dispersion (LD) space-time codes that are constructed to maximize the mutual information between the input of the space-time encoder and the output of the channel. While perfect channel state information (CSI) is assumed at the receiver, three cases are considered for the CSI at the transmitter: 1) perfect CSI is available, 2) only statistical CSI is available, and 3) partial CSI in the form of a B-bit quantized channel information along with the statistical information is available. In all the three cases, it is shown that the optimal temporal power allocation is uniform. The optimal spatial power allocation for the case where only statistical CSI is available was studied previously in [1] where it was shown to be a nontrivial function of the spatial correlation. Here, the cases of perfect and partial CSI are studied. For the perfect CSI case, it is shown that it is optimal to excite only one spatial mode. For the partial and statistical CSI cases, the optimal allocation excites multiple modes, in general. However, it is attractive to use a low-complexity scheme that excites only the dominant spatial mode. We show that this low-complexity scheme is near-optimal in two settings: 1) large receive antenna asymptotics, and 2) for fixed antenna dimensions, when the transmit and receive covariance matrices are ill- and well-conditioned, respectively. Based on the optimal schemes for the extreme cases of perfect and statistical CSI, low-complexity spatial power allocation for the case of partial CSI is considered. Simulation results indicate that even in this case, exciting one spatial mode leads to a minimal loss in performance over the optimal spatial power allocation scheme.

Indoor Positioning via WLAN Channel State Information and Machine Learning Classification Approaches

We study the multiple access channel (MAC) with perfect channel state information (CSI) at the base station and different types of CSI at the mobiles, i.e. without CSI, with long-term CSI and with perfect CSI. The average sumrate of the MAC depends on the statistics of the channel realizations and on the transmission strategy and CSI at the mobiles. In this work, we analyse the impact of the fading distributions on the achievable average sumrate. We show for the case in which the mobiles have no CSI, that the maximum sumrate is obtained for fading channels which have second order moments equally distributed. For the case in which the mobiles have information about the long-term statistics of the fading channels in terms of the fading variances, we derive the optimum power allocation and show that in this case the sumrate is maximised if one user has largest second order moment. Finally, in the case in which the mobiles have perfect CSI, only the best user is allowed to transmit. For this type of CSI, the average sumrate is maximal if one user has large fading variance and all other users have very low fading variances. We illustrate these results by numerical simulations.

We study network coding for wireless networks with finite channel state information (CSI) at intermediate nodes (relays). Based on the CSI of the relay-sink channels, we adapt the network codes at the relays. For a specific network with two sources, four relays and two sinks, the analytic results show that one bit CSI of all relay-sink channels (global CSI) can reduce complexity (field size), and simultaneously decrease the erasure probability. Then, we generalize the results to relay networks with M users, N relays and J sinks. We show that fixed network codes without CSI cannot achieve instantaneous min-cut, i.e., min-cut under current channel state. We also show that with one bit global CSI, we can achieve instantaneous min-cut by adapting the network codes using an alphabet size L, where L is the number of sinks connecting to a relay. Yet, the fixed MDS network codes use an alphabet size L( M−1 N−1). For the networks with perfect or imperfect source-relay channels, adaptive network codes with one bit global CSI have lower erasure probability than the codes without CSI. Thus, one bit global CSI can reduce the erasure probability, and simultaneously reduce coding complexity.

Channel state information (CSI) is an important part of the adaptation process in orthogonal frequency division modulation (OFDM) where the modulation and coding schemes are adapted according to the channel state of each subcarrier. However, reporting the CSI for each subcarrier can result in considerable overhead. Besides coding and modulation, another scheme that affects the overall throughput of the system is the utilized automatic repeat request (ARQ) process. If hybrid ARQ (HARQ), such as chase combining, is utilized, all the transmitted bit energy can be harnessed by the receiver by combining the erroneously received code block with the consecutive copies transmitted by the ARQ process. The feedback information can be reduced by grouping the subcarriers into subbands with one feedback per subband. The subbands can further be grouped into one or several blocks with a joint CSI feedback per block. The subbands still have their own ARQ process to cope with the possible mismatch between the joint CSI and the actual subbands state. However, this raised the question of what is the best basis on which the joint CSI should be determined since there are subbands with different channel states in a block. In this paper we study the impact of different decision variables for the feedback information. We propose the use of rank ordering to find the subband state that maximizes the total throughput. Our results suggest that the use of the maximum subband CSI as the decision variable is desirable in case of fast mobility and low signal-to-interference + noise ratio (SINR) while the minimum value subband provides better performance in a high SINR region. However, in practical ranges of SINR and mobility speed, some value in between should be chosen. We also show that using block feedback achieves higher throughput than individual subband feedback when the number of feedback bits is small.