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The traditional reserve planning method implicitly includes inertia in the frequency response reserve without realizing separate and accurate planning for inertia reserve, and cannot consider the risk preference of the decision maker, which leads to the homogenization of small probability extreme events. This paper refines the reserve classification system, adds inertia reserve for the first time, and proposes an inertia reserve capacity planning method considering the risk preference of decision makers. Based on the Sequential Monte Carlo method, component state models for conventional units, wind turbines, and photovoltaic units are established, and system load shedding sets are obtained through probabilistic production simulation. Based on the inertia response characteristics, the system frequency response model with multiple types of source-storage-load regulation resources is established to track the system frequency trajectory after disturbance. The minimum inertia demand is determined based on Rate of Change of Frequency (RoCoF) constraints, and then an inertia mismatch penalty model is constructed to measure the loss caused by RoCoF exceeding limits. The total cost-benefit model of inertia reserve considering risk preferences is then established, and the feasibility of the planning scheme is verified. Based on the improved IEEE RTS79 case studies, it is demonstrated that the proposed inertia reserve capacity planning method can flexibly formulate the optimal planning scheme under different risk ranges according to the decision maker’s risk preferences and the grid’s risk tolerance capability, avoiding the extreme events to be submerged in the massive and frequent occurrence of ordinary events, and effectively improving the risk response capability of the power system to extreme events, thus providing a more targeted decision basis for system planning, scheduling, and operation control.

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method.

Abstract 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.

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.

As a powerful statistical image modeling technique, sparse representation has been successfully applied in various image restoration applications. Most traditional methods depend on ℓ1-norm optimization and patch-based sparse representation models. However, these methods have two limits: high computational complexity and the lack of the relationship among patches. To solve the above problems, we choose the group-based sparse representation models to simplify the computing process and realize the nonlocal self-similarity of images by designing the adaptive dictionary. Meanwhile, we utilize Ipnorm minimization to solve nonconvex optimization problems based on the weighted Schatten p-norm minimization, which can make the optimization model more flexible. Experimental results on image inpainting show that the proposed method has a better performance than many current state-of-the-art schemes, which are based on the pixel, patch, and group respectively, in both peak signal-to-noise ratio and visual perception.

In this paper, an iterative procedure for the synthesis of sparse arrays radiating focused beampattern is presented. The proposed approach provides a significant reduction in the complexity of the beam forming network, which is fulfilled by reducing the number of antenna elements in the array. An iterative scheme is used where the prescribed pattern response in the mainlobe is cast as a multi-convex problem at each step that the nonconvex lower bound constraint is relaxed while including a reweighted l1-norm minimization based on the magnitudes of the elements. Thus, a sparse array with fewer elements (compared to other methods) and a better performance of beam pattern (e.g., narrower 3-dB beamwidth, lower maximum sidelobe level) is produced. The resulting sparse array is able to generate a steerable pencil beam, matching a given power mask and avoid to constraint the fitting of any a priori defined reference beam pattern. The practical array imperfections are also compensated in the optimization stage by using worst-case performance optimization technique. Examples concerning the design of linear and planar arrays show relevant savings of array elements with respect to conventional array techniques.

In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0

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.

Wideband direction-of-arrival (DOA) estimation is a key part in array signal processing. The existing algorithms for the wideband DOA estimation are often studied in the situation of uniformly distributed energy. In addition, all the frequency bins are weighted equally in these algorithms. However, these algorithms perform unsatisfactorily when encountering wideband colored signals with nonuniform energy spectrum. To improve the performance of DOA estimation for wideband colored signals, we proposed two weighting methods, which are based on the perturbed subspace theory and random matrix theory, respectively. The two methods weight the space spectrum from all the frequency bins according to the mean square error of DOA estimation in each frequency bin. The numerical results show that the random matrix theory based method performs well, due to the inference premise that the dimensions of matrices increase at the same rate. The perturbed subspace-based method, which is concise in calculating the weights, shows high accuracy only at high-signal-to-noise ratio and with adequate snapshots. The effectiveness of the two algorithms are also demonstrated by comparing them to various existing algorithms and the Cramér–Rao bound.

Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a color image and treat these groups as tensors with each color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real color image denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy images verify the effectiveness of our method.

In a low-angle tracking scenario, it is difficult to get an accurate estimation of the direction-of-arrival (DOA) due to the presence of reflection path. Direct and reflected signals are highly correlated, which degrades the performance of conventional DOA estimation methods. To solve this problem, we develop a new method for DOA estimation by leveraging the 12,1-norm minimization technique in a sparse recovery theory. To be more specific, we treat the direct signal and the corresponding reflected signal as a group, where we encourage group sparsity by applying 12,1-norm minimization. We use the information of reflected signal to overcome the effect of reflection path. Moreover, we also propose another sparse recovery-based method which uses information of the predetermined reflection path coefficient under some certain conditions. A method of choosing the regularization parameter is proposed to ensure the robust sparse recovery. Meanwhile, the Cramer-Rao bound (CRB) of low-angle signals is also derived as there is no direct derivation of it. Numerical results demonstrate that the proposed methods can yield superior performance in solving the DOA estimation than the existing methods.

Most existing velocity measurement methods can only calculate the radial velocity by Doppler effect. Here, the authors study the method of measuring the lateral velocity. It is found that the lateral velocity influences the changing rate of Doppler frequency over time, which inspires them to measure the lateral velocity by analysing the Doppler spectrum or the quadratic phase of the echo. The relationship between the echoes and the lateral velocity is modelled in this study. Also the mean square error of lateral velocity measurement, including the model error and the estimation error, is also deduced. The influence of the observation time is studied according to the mean square error.

To deal with the problem of reliability degradation of radar emitter identification (REID) based on the traditional five parameters in a complex electromagnetic environment, a new feature extraction method based on the autocorrelation function of coherent signals, which makes full use of the coherent characteristic of modern radar emitters, is proposed in this paper. The main idea of this paper is utilizing the instantaneous autocorrelation function to obtain the correlation results of coherent and noncoherent signals. To this end, a new feature parameter, named the ratio of the secondary peak value to the main peak value (SMR), is defined to describe the difference of correlation results between coherent and noncoherent signals. Through simulation analysis, the feasibility of using SMR as the coherent feature for REID is verified. In order to evaluate the effectiveness of the coherent feature, an analytical hierarchy process (AHP) was introduced to compare the comprehensive performance of the coherent feature and the existing parameters, and then convolution neural network (CNN) and support vector machine (SVM) were selected as the classifier to check the recognition capability of the proposed feature. Simulation results show that the proposed feature can not only be used as a new feature for REID but can also be used as a supplement to existing feature parameters to improve the accuracy of REID as it is more insensitive to the signal-to-noise ratio (SNR) and signal modulation type changes.

This paper proposes two modified space time adaptive processing (STAP) methods based on piecewise sub-apertures and data constraints for non-stationary interference cancellation in synthetic aperture radar (SAR) applications. In these methods, the entire synthetic aperture time is divided into several sub-apertures so that the interference can be considered as stationary in each sub-aperture. At the same time, the consistency of the echo phase in the slow time domain is preserved by the data constraint to ensure the null depth of the antenna pattern for non-stationary interference cancellation and the performance of azimuth focusing in SAR. The proposed algorithms are validated through the model simulation and measured data.

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.

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.