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When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction.

This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction. [ABSTRACT FROM AUTHOR]

In this study, we investigated two waveform design problems for a dual-functional radar communication (DFRC) system, taking into consideration different constrained communication quality-of-service (QoS) requirements. Our objective was to minimize the mean-square error (MSE) of radar beampattern matching as the cost function. To this end, the multi-user interference (MUI) energy constraint and constructive interference (CI) constraint were, respectively, formulated to ensure the communication QoS. It is important to note that we designed a strict per-user MUI energy constraint at each sampling moment to achieve more accurate control over communication performance. Additionally, we introduced a constant-modulus constraint to optimize the efficiency of the radio frequency (RF) amplifier. To tackle the nonconvex waveform design problems encountered, we employed the alternative direction methods of multipliers (ADMM) technique. This allowed us to decompose the original problem into two solvable subproblems, which were then solved using the majorization–minimization (MM) method and geometrical structure. Finally, we obtained extensive simulation results which demonstrate the effectiveness and superiority of the proposed algorithm.

Abstract Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques.

In practical applications, limited independent and identically distributed training snapshots brings a serious challenge in space-time adaptive processing (STAP), especially in the nonhomogeneous environments. Motivated by the significant spatial smoothness and sparsity commonality of weight vectors among related STAP tasks, we propose a novel STAP algorithm based on multi-task learning. In the proposed algorithm, the weight vectors corresponding to neighboring range bins of interest are kept consistent, and all weight vectors are constrained to share a common feature. Then, an alternating direction method of multipliers (ADMM) is used to solve the proposed algorithm, and the convergence of the algorithm is guaranteed. In addition, in case that the feature matrix is unknown or we want to learn a better feature matrix so that the associations among STAP tasks can be enhanced, we also provide an extension of the proposed algorithm to jointly optimize the feature matrix and weight matrix. Simulation results demonstrate the effectiveness of the proposed strategies.

We consider the synthesis of discrete finite-alphabet inputs for secure communications in multiple-input-single-output wiretap channels. We aim that the source with the synthesized transmissions can communicate with a legitimate receiver and degrade the performance of a potential eavesdropper. To this end, we formulate an optimization problem to minimize the Euclidean distance between the reference inputs and the synthesized inputs. We devise an iterative algorithm to tackle the non-convex design problem with constant-envelope constraints. Moreover, we extend the proposed methodology to deal with low peak-to-average-power ratio transmissions. The numerical examples demonstrate that the proposed algorithms can achieve the requirement of secrecy communications efficiently.

The energy dispersion of traditional bistatic multiple-input multiple-output (MIMO) radar severely degrades the angle estimation performance. To overcome this limitation, we propose to design the transmit beamspace matrix and apply the design result to jointly estimate the target directions of departure (DOD) and directions of arrival (DOA). The essence of this method is to minimize the distance between a desired transmit steering vector and the actual one within a given angular sector. Moreover, we enforce two inequality constraints to achieve approximately uniform power distribution across the transmit array elements. In addition, we carefully devise the desired transmit steering vector to expand the virtual transmit aperture and further improve the DOD estimation performance. Furthermore, we use tensor-decomposition and a phase-searching method to estimate the target DODs and DOAs. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

The existing methods to design orthogonal waveforms for multiple-input multiple-output radar mainly focus on the optimisation of autocorrelation and cross-correlation properties. Their performance will degrade severely in the presence of Doppler shifts. To overcome this limitation, the authors take an unknown Doppler shifts range into consideration and formulate a new waveform optimisation problem. Since the optimisation problem is highly non-linear, the authors propose an algorithm, called sequential cone programming, to tackle it. The key idea is to use the first-order Taylor expansion to approximate the constraints at each iteration. The authors show that the approximation can be solved via second-order cone programming. In addition, the autocorrelation peak sidelobe level and cross-correlation peak level could be further reduced by setting an appropriate threshold function. Simulation results demonstrate the efficiency of the proposed method compared with state-of-art methods.

This study investigates the waveform design problem for a distributed multiple-input-multiple-output (MIMO) radar operating in spectrally crowded environments. The goal is to synthesise waveforms to minimise the target estimation error and improve the spectral coexistence with other radio frequency systems (e.g. communication systems). To this end, a waveform design problem based on minimising the mean square error (MSE) of target estimation has been formulated under the energy and spectral constraints. An algorithm to synthesise the optimal spectrally constrained waveforms with the minimum MSE has been proposed. It has been shown that the synthesised waveforms have superior estimation performance (in terms of MSE) and ensure significantly enhanced spectral compatibility.