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An approach for the synthesis of sum and difference patterns in monopulse antennas is described. The proposed approach provides a significant reduction in the complexity of the beam‐forming network that is fulfilled by reducing the number of array elements and keeping the elements at the edges of the array that share common excitations for both sum and difference modes. An iterative constrained optimisation method is used where the non‐convex lower‐bound constraints on the beam pattern are cast as an equivalent multi‐convex optimisation problem while concurrently minimising a reweighted l1‐norm of the magnitudes of the elements in the beam‐forming weight vector. Thus, better radiation performance of beam pattern (e.g. narrower beamwidth, lower peak sidelobe level), a much narrower spatial aperture and a smaller number of elements can be achieved compared with the case of uniformly spaced arrays. To compensate for array imperfection in practice, robust beam pattern constraints are derived in the optimisation stage using a worst‐case performance optimisation technique. Numerical examples show the effectiveness and advantages of the proposed synthesis approach.

In this letter, we study the resource allocation problem in a multiuser multi-antenna system, in which the energy supply of the transmitter consists of the grid energy and harvested energy. Our objective is to maximize the long-term sum throughput under the constraint of energy supply by optimizing beamforming vectors and energy allocation. Considering the challenges of imperfect channel state information (CSI) and large action/state spaces, we propose a dimension reduction deep reinforcement learning (RL) method to solve the optimization problem. In the proposed algorithm, the beamforming vectors are first determined based on imperfect CSI, and then policy-based RL is employed to find the optimal mapping between transmit powers and the low dimensional system state. Simulation results demonstrate the superiority of the proposed algorithm over traditional ones in terms of steady-state performance and learning speed.

Multi-Spectral Image(MSI) denoising is an important preprocessing procedure to improve the performance of high-level processing. Tensor-based approach is one of the most popular methods for MSI denoising, since MSIs can be seen as multi-dimension arrays containing both spatial and spectral information. There are two main information in MSI, Global Correlation along Spectrum(GCS) and Nonlocal Self Similarity across space(NSS). Most tensor based approaches exploited these two characteristics by low-rank regularizations, mainly based on CANDERCOMP/PARAFAC(CP) decomposition and Tucker decomposition. However, they did not show a clear physical meaning. In this paper, we exploit the fact that pixels in MSI often cover several different materials and so that tensor data is mixed. Based on this, we divide tensor into several sub-tensors and propose a novel low rank regularization called Whole and Sub-Sparsity(WSS): GCS is modeled in the sub-tensors and NSS is modeled in the original tensor, which shows a clear physical meaning. Besides, to solve our model, we develop the corresponding algorithm by employing alternating direction method of multipliers(ADMM) framework. Experiment results show that our method is competitive compared to all state of the art MSI denoising methods.

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works.

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory, the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

A new approach for the synthesis of one-dimensional (1D) and 2D reconfigurable sparse arrays generating sum and difference power patterns is presented in this study. In the case of 1D arrays, the design procedure provides solutions in which only one set of amplitude coefficients is required and the reconfigurability is obtained by modifying only the excitation phases. In the case of 2D arrays, the simplification of the antenna system is investigated by sharing some excitations for the sum and difference channels. An iterative scheme is used where the array response in the main lobe direction is cast as a multi-convex problem at each step that the non-convex lower bound constraint is relaxed while concurrently minimising a reweighted objective function based on the magnitudes of the element excitations. To ensure the robustness of the array, worst-case performance optimisation technique and white noise gain constraint are introduced in the array. Numerical tests and electromagnetic simulations, referred to known optimal solutions, show that the proposed design is able to obtain good radiation performance with a smaller number of elements.

The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes a weighted decision-level fusion architecture to take advantage of data from multiple radar sensors, and an online feature reliability evaluation method is also used to comprehensively generate sensor weight coefficients. The weighted decision-level fusion method can overcome the deficiency of a single sensor and enhance the recognition rate for warheads in the midcourse phase by considering the changes in the reliability of the sensor’s performance caused by the influence of the environment, location, and other factors during observation. Based on the simulation dataset, the experiment was carried out with multiple sensors and multiple bandwidths, and the results showed that the proposed model could work well with various classifiers involving traditional learning algorithms and ensemble learning algorithms.

In this paper, a new array synthesis approach is developed for the design of reconfigurable sparse arrays radiating sum and difference patterns. The proposed approach provides a significant...

Group testing is applied to recover a small defective subset of items by a number of tests much smaller than the total population. In this paper, we study group testing from the Bayesian perspective. The state of all the items is manipulated as a random variable, the probability function of which is updated iteratively. We also propose an algorithm which designs the measurement vectors adaptively to decrease the number of tests, compared to non-adaptive methods. The measurement vector is chosen by maximizing the expectation of update gain in each test. We also propose a fast approximation method, which updates the probability function of each item independently to decrease the computational cost when designing the measurement vector within each test. Furthermore, the expected value of the required numbers of tests for the proposed methods are deduced theoretically. The deduced results are appropriate for both the noise-free case and the case with noise, even in the situation where both the additive noise and dilution noise exist. We also carry out simulations to compare the proposed algorithms and existing algorithms in the literature, the results of which reveal that the proposed algorithms require fewer tests and are robust in the presence of noise.

Delay/disruption tolerant networking (DTN) [1] is proposed as an internetworking architecture for reliable data delivery despite a long propagation delay and/or lengthy link disruptions. DTN targets challenging networking environments where traditional networking protocols such as the terrestrial Transmission Control Protocol/Internet Protocol (TCP/IP) typically do not work effectively. The challenging problem of mission control and data delivery in space communications is a typical application scenario of the DTN technology. Reliable and highly efficient data delivery of DTN relies heavily on the bundle protocol (BP) [2]. BP is intended to establish an overlay network that is able to withstand intermittent data link connectivity in the delivery of DTN data units, termed bundles. BP adopts a store-and-forward mechanism and a custody transfer option for transmission reliability.

In the field of radar emitter recognition, with the wide application of modern radar, the traditional recognition method based on typical five feature parameters cannot achieve satisfactory recognition results in a complex electromagnetic environment. Currently, many new feature extraction methods are presented, but few approaches have been applied for feature evaluation or performance comparison. To deal with this problem, a feature evaluation and selection method was proposed based on set pair analysis (SPA) theory and analytic hierarchy process (AHP). The main idea of this method is to use SPA theory to solve problems regarding the construction of the decision matrix based on AHP, as it relies heavily on expert’s subjective experience. The aim was to improve the objectivity of the evaluation. To check the effectiveness of the proposed method, six feature parameters were selected for a comprehensive performance evaluation. Then, the convolutional neural network (CNN) was introduced to validate the recognition capability based on the evaluation results. Simulation results demonstrated that the proposed method could achieve the feature analysis and evaluation more reasonably and objectively.

Delay/disruption tolerant networking (DTN) was proposed as a networking architecture for reliable data delivery despite an extremely long propagation delay and frequent/lengthy link disruptions. The challenging problem of mission control and data delivery in deep-space explorations is a typical application scenario of the DTN technology. Reliable data delivery of DTN relies heavily on its core bundle protocol (BP). Performance evaluation and improvement of BP for deep-space communications are presently underway. The setting of the retransmission time-out (RTO) timer of BP is critical for reliable and highly efficient file transfer in a deep-space communication environment. In this paper, we present analytical modeling of an optimal RTO timer interval for the best goodput performance of BP in deep-space communications characterized by a very long signal propagation delay and lossy data links. A model is developed to compute the RTO timer interval that will result in the best goodput performance of BP normalized with respect to the total data load transmitted in order to achieve successful delivery of an entire file. Experimental validation using a PC-based testbed indicates that the RTO timer interval indicated by the model achieves the best normalized goodput performance of BP. The model predicts performance in all the experimented scenarios.

This letter addresses multisensor data fusion under the Gaussian noise. Under the Gauss-Markov model assumptions, data fusion based on maximum likelihood estimation (MLE) is the minimum variance unbiased estimator. Nonetheless, we propose a linear fusion algorithm based on the random matrix theory, which yields a biased estimator. The proposed estimator has a lower mean squared error (MSE) than the MLE fusion method when the dimensionality of signal is larger than the number of sensors, which is the typical use case in modern fusion systems. The fusion coefficients are directly solved in the proposed method without iteration, and this method can be considered as an approximate implementation of the linear minimum MSE (LMMSE) estimator. Numerical simulations demonstrate the performance gain of the proposed fusion method.

Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, learned simultaneous sparse coding. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization cannot still obtain the correct sparsity solution under some practical problems including image inverse problems. In this letter, we propose a nonconvex weighted $\ell _p$ minimization based group sparse representation framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding algorithm is adopted to solve the nonconvex $\ell _p$ minimization problem. In addition, to improve the accuracy of the nonlocal similar patch selection, an adaptive patch search scheme is proposed. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and weighted nuclear norm minimization, but also results in a competitive speed.

Wireless energy transfer is a promising solution to provide convenient and steady energy supplies for low-power relays. This paper investigates the simultaneous information and energy transfer in fading relay channels, where the relay has no fixed energy supply and replenishes energy from radio frequency signals transmitted by the source. Assume that the relay can switch among energy harvesting, information decoding, and information retransmission in each channel fading state. Our objective is to maximize the ergodic throughput by optimizing the mode switching rule and transmit power jointly under the data and energy causality constraints. When the source knows channel state information (CSI) of all links, to make the problem tractable, for the relay, we neglect the causality constraints during the transmission, and only consider the total data and energy constraints. We thus obtain an upper bound on the ergodic throughput by solving a convex optimization problem. Numerical results show that the achievable rate is very close to the upper bound when we apply the optimized parameters to a practical system. When the source only knows CSI of partial links, the whole transmission process is divided into two phases: the source transmits in the first phase and the relay decodes and forwards received bits using the harvested energy in the second phase. The throughput maximization problem is solved by combing convex optimization, fractional programming, and linear search. We also consider the simplified network topology when a direct link between the source and destination is unavailable. In this network, we propose algorithms based on bisection method to obtain the optimal parameters in information/energy transfer scheduling and power control when the source knows full or partial CSI. The simulation results reveal that the throughput gain brought by wireless powered relaying in different system configurations when the source knows full or partial CSI. Moreover, the effect of the relay position is discussed.

In this paper, we investigate the transmission schemes of space data systems for optimized network capacity in an integrated terrestrial-satellite network (ITSN) with a twolayered space segment. First, a theoretical model of the network capacity is developed to evaluate the strategy of utilizing both direct and relayed transmissions. Second, we consider the ideal and the resource-constrained scenarios in which the corresponding network capacity is modeled with respect to the scheduling scheme. In particular, closed form and semi-closed form solutions to the difficult integer programs are achieved via rigorous mathematical analysis. The proposed model is general for exploring the capacity of various satellite network deployments whose solutions have not been obtained in prior studies. Furthermore, we verify the potential capacity of the different transmission schemes based on the proposed solutions and prove that the system’s network capacity can be significantly improved by the hybrid transmission scheme. The theoretical framework proposed in this paper is expected to provide constructive insights in the design for the future space segments of ITSN.

Velocity measurement is a basic task of radars. The target velocity is usually estimated according to the Doppler frequency shift. While traditional Doppler methods are unsuitable for high-speed targets, since the serious range migration between adjacent echoes causes phase wrapping. The serious range migration also interferes the coherent integration to improve the accuracy of the velocity estimation. A velocity measurement method based on Keystone transform using entropy minimization is studied to solve this problem. This method applies Keystone transform to the echo to calculate the ambiguity degree with the help of entropy minimization. The proposed algorithm estimates the ambiguity degree with no error at a wider range of SNR than the traditional method. The ambiguous Doppler frequency is obtained according to the slow time. Theoretical analyses and simulations show that this method has very high precision.

Aiming at the problem of reliability reduction of signal sorting in terms of the traditional five parameters and intrapulse feature in a complex electromagnetic environment, a new signal sorting method based on radar coherent characteristics is proposed. The main idea of this method is using spectrum analysis to obtain the spectrum images of coherent and noncoherent signals. Image-processing technology is used to extract the feature difference between the two spectrum images, and the central-moment feature is introduced to describe this difference. Through simulation analysis, the feasibility of using the central-moment feature as the coherent feature for signal sorting was proved. In order to check the effectiveness of the proposed feature, a number of simulations were conducted to demonstrate the sorting capability in terms of the coherent feature. From the simulations, it can be seen that the proposed feature not only can be used as a new feature for signal sorting but also that it can be utilized as a supplement for five typical parameters and the intrapulse feature to improve the sorting accuracy rate. Simulations also showed the proposed method could achieve satisfactory sorting results in a low signal-to-noise ratio (SNR). When the SNR was 5 dB, the sorting accuracy rate could reach 98%.

A novel coherent integration method for the wideband radar is proposed in this paper based on two-dimensional frequency correction. The method realizes the motion compensation by data re-alignment in the fast time frequency-Doppler domain and can be implemented quickly and efficiently based on chirp-Z transform. The proposed method is validated by simulation and measured data. The work in this paper provides a new and effective way for coherent integration in wideband radar.

The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

Gaofen-3 (GF-3) is China’ first C-band multi-polarization synthetic aperture radar (SAR) satellite, which also provides the sliding spotlight mode for the first time. Sliding-spotlight mode is a novel mode to realize imaging with not only high resolution, but also wide swath. Several key technologies for sliding spotlight mode in spaceborne SAR with high resolution are investigated in this paper, mainly including the imaging parameters, the methods of velocity estimation and ambiguity elimination, and the imaging algorithms. Based on the chosen Convolution BackProjection (CBP) and PFA (Polar Format Algorithm) imaging algorithms, a fast implementation method of CBP and a modified PFA method suitable for sliding spotlight mode are proposed, and the processing flows are derived in detail. Finally, the algorithms are validated by simulations and measured data.

Currently, localization has been one of the research hot spots in Wireless Sensors Networks (WSNs). However, most localization methods focus on the device-based localization, which locates targets with terminal devices. This is not suitable for the application scenarios like the elder monitoring, life detection, and so on. In this paper, we propose a device-free wireless localization system using Artificial Neural Networks (ANNs). The system consists of two phases. In the off-line training phase, Received Signal Strength (RSS) difference matrices between the RSS matrices collected when the monitoring area is vacant and with a professional in the area are calculated. Some RSS difference values in the RSS difference matrices are selected. The RSS difference values and corresponding matrix indices are taken as the inputs of an ANN model and the known location coordinates are its outputs. Then a nonlinear function between the inputs and outputs can be approximated through training the ANN model. In the on-line localization phase, when a target is in the monitoring area, the RSS difference values and their matrix indices can be obtained and input into the trained ANN model, and then the localization coordinates can be computed. We verify the proposed device-free localization system with a WSN platform. The experimental results show that our proposed device-free wireless localization system is able to achieve a comparable localization performance without any terminal device.

Wireless energy transfer (WET) is a promising technique to prolong the lifetime of an energy-constrained wireless network. In this paper, we consider a two-hop communication system with multiple relay nodes powered by WET. To minimize the transmission time, we propose an optimal transmission scheme, in which the relay nodes adopt a “harvest-then-decode and forward” policy. The optimal transmission time allocation of the source and each relay node is calculated by a linear program under causality constraints on both data and energy arrivals. The analytical and simulation results reveal the relationship between the optimal time allocation and system parameters.

SUMMARY The quality of channel state information at the transmitter (CSIT) is critical to MIMO beamforming systems. However, in practical wireless systems, CSIT suffers from imperfections originating from quantization effects, feedback error and feedback delay. In this paper, we study the impact of feedback error and delay on the symbol error rate of MIMO beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. We derive an symbol error rate upper bound that is tight for a good beamformer. We also quantify the diversity gain and array gain loss due to the feedback error and delay. The codebook design method that is applicable to the beamforming systems with error or delay feedback is discussed. Both analytical and simulation results show that feedback error and delay will make the system behave badly at high signal-to-noise ratios. Copyright © 2012 John Wiley & Sons, Ltd.

In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance.