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The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between the target and the (N+1=10) receivers’ locations. The method facilitates the problem of target position and can be used as a counter-parallel method for spherical interpolation (SI) and spherical intersection (SX) methods in time difference of arrival (TDOA) and radar systems. The performance of the method is examined in 2D target localization using numerical analysis under the distribution of receivers in the pentagram array. The simulations are conducted using four different far-distance targets and comparatively large-area distributed receivers. The RD measurements were distorted by two different values of Gaussian errors based on ionosphedriec time delays of 20 and 50 nsec owing to the different receivers’ positions. The findings highly verified the validity of the method for addressing the problem of target localization. Additionally, a theoretical accuracy study of the method is given, which solely relies on the RD measurements.

In order to study the effect of the velocity pulse on the dynamic response of the train-bridge system of the high-speed railway simple supported beam bridge, the velocity pulse is simulated by the trigonometric function method and superimposed with the far-field earthquake without pulse to synthesize the pulse with different pulse types, pulse periods and pulse peaks. A 10 × 32m typical high-speed railway simple supported beam bridge is considered an case illustrating study. Then, the dynamic response of train-track-bridge coupling system is calculated by train-track-bridge seismic analysis software TTBSAS. Afterwards, the influence of pulse near-field earthquakes parameters and vertical components on dynamic response of train-bridge system and the safety of the train on the bridge are discussed in detail.The new derailment evaluation index is adopted to evaluate driving safety under earthquakes. The train safety control of simply supported beam bridge under the action of near-field earthquake is studied. The results show that the impact of pulse ground motion on the dynamic response of the train-track-bridge coupling system is significantly higher than that of no-pulse ground motion, especially the impact on the bridge and rail subsystem is more significant than that of train subsystem. Under the excitation of ground motion intensity of 0.05g ∼ 0.15g, the safe speed threshold of pulse near-field ground motion is smaller than that of far-field ground motion. When the ground motion intensity is 0.20g ∼ 0.30g, the safe speed threshold of pulse near-field ground motion and far field ground motion is 200km/h. So, the pulse near-field earthquake poses a greater threat to the safety of the train on the bridge than the far-field earthquake. Therefore, the influence of pulse near-field earthquakes should be considered in the seismic design. The research results of this paper can provide theoretitcal support for the design of a high-speed railway bridge in the near-field area.

With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. The solution involves the inherent structure of the sensing vector to reconstruct the autocorrelation sequence of inputs from sub-Nyquist samples, which requires only parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time without the need to pre-estimate the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, allowing for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, thus making it a more practical solution for real-time and distributed wideband spectrum sensing applications.

Antenna arrays are used for signal processing in sonar and radar direction of arrival (DOA) estimation. The well-known array geometries used in DOA estimation are uniform linear array (ULA), uniform circular array (UCA), and rectangular grid array (RGA). In these geometries, the neighboring elements are separated by a fixed distance λ/2 (λ is the wavelength), which does not perform well for d greater than λ/2. Uniform rectangular arrays introduce grating lobes, which cause poor DOA estimation performance, especially for wideband sources. Random sampling arrays are sometimes practically not realizable. Periodic geometries require numerous sensors. Based on the minimization of the number of sensors, this paper developed a novel pentagram array to address the problem of DOA estimation of wideband sources. The array has a fixed number of elements with variable element spacing and is abbreviated as (FNEVES), which offers a new idea for array design. In this study, the geometric structure is designed and mathematically analyzed. Also, a DOA signal model is designed based on a spherical radar coordinate system to derive its steering manifold matrix. The DOA estimation performance comparison with ULA and UCA geometries under the multiple signal classification (MUSIC) algorithm using different wideband scenarios is presented. For further investigation, more simulations are realized using the minimum variance distortionless (MVDR) technique (CAPON) and the subtracting signal subspace (SSS) algorithm. Simulation results demonstrate the effectiveness of the proposed geometry compared to its counterparts. In addition, the SSS, through the simulations, provided better results than the MUSIC and CAPON methods.

The task of modeling and analyzing intercepted multifunction radars (MFRs) pulse trains is vital for cognitive electronic reconnaissance. Existing methodologies predominantly rely on prior information or heavily constrained models, posing challenges for non-cooperative applications. This paper introduces a novel approach to model MFRs using a Bayesian network, where the conditional probability density function is approximated by an autoregressive kernel mixture network (ARKMN). Utilizing the estimated probability density function, a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains. Simulation results affirm the proposed method's efficacy in modeling MFRs, outperforming the state-of-the-art in pulse train denoising and change point detection.

In recent years, radar systems have advanced significantly, offering environmental adaptation and multi-task capabilities. These developments pose new challenges for electronic intelligence (Elint) and electronic support measures (ESM), which need to identify and interpret sophisticated radar behaviors. This paper introduces a Deep Multi-Intentional Inverse Reinforcement Learning (DMIIRL) method for the identification and inverse cognition of cognitive multi-function radars (CMFR). Traditional Inverse Reinforcement Learning (IRL) methods primarily target single reward functions, but the complexity of CMFRs necessitates multiple reward functions to fully encapsulate their behavior. To this end, we develop a method that integrates IRL with Expectation-Maximization (EM) to concurrently handle multiple reward functions, offering better trajectory clustering and reward function estimation. Simulation results demonstrate the superiority of the proposed method over baseline approaches.

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods.

The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM).Recent supervised deep learning-based methods have achieved great success in a such classification task.However, those methods require enough labeled samples to work properly and in many circumstances, it is not available.To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels.Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features.Next,the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features.And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation. The simulation results show that the proposed algorithm can effectively extract class-dependent features, outperforming several unsupervised clustering methods, even reaching performance on par with the supervised deep learning-based methods.

The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate. For some applications, spectrum estimation only requires second-order statistics. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. By exploring the sensing vector inherent structure, the autocorrelation sequence of inputs can be reconstructed from sub-Nyquist samples by only utilizing the parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time, without the need for pre-estimating the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, which allows for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, making it a more practical solution for real-time and distributed wideband spectrum sensing applications.