Change Point Detection for Fine-Grained MFR Work Modes with Multi-Head Attention-Based Bi-LSTM Network

An improved method of reducing the input impedance of a four-arm sinuous antenna is demonstrated for a wide gain bandwidth. The proposed antenna has a dual-linear polarized response for electronic support measures (ESM) systems and is designed with a planar substrate. The structure for reducing the input impedance is developed based on the transmission and coupled line theory and is shown to be in excellent agreement with theoretical predictions. The proposed antenna achieves an impedance bandwidth in the range of 0.45–6 GHz with a reference impedance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 ~\Omega $ </tex-math></inline-formula> . Simulations for comparison according to the difference in input impedance of designed antennas with feeding structures are also presented. Finally, the performance of the fabricated antenna with wideband balun is verified by measuring co- and cross-polarization radiation patterns. As a result, the proposed antenna achieves a wide gain bandwidth (>2.5 dBi) and a cross-polarization isolation (>−13 dB) of more than 12:1 in the frequency range over 0.6 GHz, which can be used in ESM systems.

Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection used in streaming data settings are unsupervised and, as a consequence, lack any information regarding what kind of changes we want to detect or if some kinds of changes are safe to ignore. This often results in poor change detection performance, as many spurious changes are falsely detected as change points. We present a novel change point detection framework that uses a small number of labeled change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points. Our method can be used to learn a sparse metric which can be useful for both feature selection and interpretation in high-dimensional change point detection settings. Experiments on simulated as well as real world sequences show that our proposed method can substantially improve change point detection performance over existing unsupervised change point detection methods using only few labeled change point instances.

The multi-function radar (MFR) is capable of transmitting complex signals and achieving beam agility according to different tasks, thus leading to many challenges in the work mode recognition field. This paper, therefore, develops an Encoder-Decoder model based on the gated recurrent units (GRU) network to achieve work mode recognition. It involves the use of the Encoder structure to extract the temporal features and the work mode transition regulations of the intercepted pulse group sequence while leveraging the Decoder structure to decode the features and transition regulations as a part of the input for the next decoding process. Additionally, the label substitution (LS) method is utilized for improving the ability of the Decoder structure to recognize work mode under non-ideal situations. Simulation results show that the proposed method is less sensitive in the presence of non-ideal situations and shares better performance than existing work mode recognition methods.

The paper deals with the problem of the multifunction radar resources management (MFR RM). MFR RM consists of target ranking and tasks scheduling. The paper is focused on the data fusion approach to the target ranking. The data from the radar (object's velocity, range, altitude, direction etc.), IFF system (identification friend or foe) and ESM system (electronic support measures - information concerning a threat's electro magnetic activities) are used to decide of the importance assignment for each defected target. The problem is also known as a threat assessment. The main problem consists of the multiplicity of various types of the input information. The information from the radar is of the probabilistic or ambiguous imperfection type and the IFF information is of evidential type. To take advantage of these information sources an advanced data fusion system is necessary. The paper describes a comparison between the fuzzy and the neural network systems which were used in order to perform the threat assessment.

Capturing correlation changes by applying kernel change point detection on the running correlations

Modern industrial assets (e.g., generators, turbines, engines) are outfitted with numerous sensors to monitor key operating and environmental variables. Unusual sensor readings, such as high temperature, excessive vibration, or low current, could trigger rule-based actions (also known as faults) that range from warning alarms to immediate shutdown of the asset to prevent potential damage. In the case study of this article, a wind park experienced a sudden surge in vibration-induced shutdowns. We utilize fault data logs from the park with the goal of detecting common change points across turbines. Another important goal is the localization of fault occurrences to an identifiable set of turbines. The literature on change point detection and localization for multiple assets is highly sparse. Our technical development is based on the generalized linear modeling framework. We combine well-known solutions to change point detection for a single asset with a heuristics-based approach to identify a common change point(s) for multiple assets. The performance of the proposed detection and localization algorithms is evaluated through synthetic (Monte Carlo) fault data streams. Several novel performance metrics are defined to characterize different aspects of a change point detection algorithm for multiple assets. For the case study example, the proposed methodology identified the change point and the subset of affected turbines with a high degree of accuracy. The problem described here warrants further study to accommodate general fault distributions, change point detection algorithms, and very large fleet sizes.

Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature.

The structure design and switching regulation of pulse groups in multi‐function radars (MFRs) are closely related to the work mode. The sequential extraction and recognition of MFR pulse group structure is a fundamental task to analysing and interpreting the work modes and behaviour intentions of an MFR. In this study, the temporal structure of MFR pulse group is expressed hierarchically, which is intensively modelled based on regular grammar. Besides, a corresponding hierarchical automaton is established to sequentially extract and recognise the MFR pulse group structure in pulse train. The hierarchical automaton used for pulse group recognition has a two‐layer structure. The bottom layer of the hierarchical automaton realises the sequential input of pulses and recognition of pulse subgroups, and the sequential input of pulse subgroups and recognition of pulse groups are realised at the top layer. The simulation results demonstrate that the proposed method performs satisfyingly in recognising the pulse group structure and is robust to not only pulse noises but also to the emitter number and pulse group scale.

This paper covers development trends in communication and radar electronic warfare (EW). Threat scenario and role of EW sensors in developing Electronic Order of Battle (EOB) are described. EW is subdivided into Electronic Support Measure (ESM), Electronic Counter Measure (ECM) and Electronic Counter-counter Measure (ECCM) systems. Developments in communication ESM and ECM systems and their constituents are described. Under COM ECM, jammer system configuration and realisation of the constituent subsystems, jamming strategies used for effectively countering various types of communications are described. Developments in critical subsystems and configurations of typical radar EW systems are discussed. Finally the status of the indigenous developments in ESM, ECM and integrated systems are described.

Environmental health studies are of great interest in research to evaluate the mortality-temperature relationship by adjusting spatially correlated random effects as well as identifying significant change points in temperature. However, this relationship is often not expressed using parametric models, which makes identifying change points an even more challenging problem. This paper proposes a unified semiparametric approach to simultaneously identify the nonlinear mortality-temperature relationship and detect spatially-dependent change points. A unified method is proposed for the model estimation, spatially dependent change points detection, and testing whether they are significant simultaneously by a permutation-based test. We operate under the assumption that change points remain constant, yet acknowledge the uncertainty regarding their precise number. These change points are influenced by the smoothing of an unknown function, which in turn relies on a smoothing variable and spatial random effects. Consequently, the detection of change points may be influenced by spatial effects. In this paper, several simulation studies are conducted to evaluate the performance of our proposed approach. The advantages of this unified approach are demonstrated using epidemiological data on mortality and temperature.

Modern industrial systems are becoming increasingly complex, posing severe challenges to traditional fault diagnosis methods. Traditional multivariate statistical analysis methods struggle with dynamic non-stationary data, incipient faults, and unknown fault types due to their stationarity assumptions and dependence on historical fault data. Existing research lacks systematic comparison between change point detection and traditional multivariate analysis methods, particularly regarding temporal dynamics capture and early warning capabilities. This study aims to compare the application characteristics and scenarios of change point detection versus traditional multivariate analysis in industrial fault diagnosis. Through systematic analysis of both methods' principles and characteristics, this work reveals that change point detection demonstrates superior performance in temporal dynamics capture, early warning, adaptability, and data efficiency, providing theoretical foundation for method selection and elucidating future directions for intelligent fault diagnosis in complex industrial environments.

This paper investigates integrated waveform design for radar systems to simultaneously achieve a low probability of intercept (LPI) by an adversary electronic support measure (ESM) system while maintaining communications with other radar nodes. A reconfigurable intelligent surface (RIS) is exploited and jointly designed to enhance the achievable performance of the whole system. LPI detection based on the ESM’s feature analysis on the radar waveform is achieved under the constraints of a desired signal-to-interference-and-noise ratio for the radar detection and a desired signal-to-noise ratio-based quality of service for the communication channels. To deal with the resulting non-convex optimization problem, a suboptimal composite algorithm with polynomial complexity is proposed. The initial feasible solution of the algorithm is efficiently obtained through an asymptotic optimization problem. Finally, the complexity of the proposed algorithm is analyzed. Simulation results including comparisons with baselines highlight the effectiveness of the proposed scheme.

Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-theart detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors and realize that standard prediction aggregation techniques, e.g., averaging, are suboptimal and fail to account for problem peculiarities. Alternatively, we introduce WWAggr - a novel task-specific method of ensemble aggregation based on the Wasserstein distance. Our procedure is versatile, working effectively with various ensembles of deep CPD models. Moreover, unlike existing solutions, we practically lift a long-standing problem of the decision threshold selection for CPD.

With the development of electronic technology and the improvement of actual combat requirements, radar, as the essential electronic reconnaissance equipment, its related systems and technologies continue to update and develop to meet the growing combat requirements, which brings great challenges to radar working mode analysis and recognition. This paper recognizes radar working mode as the main research content and analyzes the radar signal and typical working mode. This paper mainly carries on the analysis and modeling from the point of view of signal parameters, focusing on the relationship and difference between radar typical working modes. The hierarchical model is used to realize the working pattern recognition, and the working pattern recognition is divided into the PDW layer, parameter modulation pattern, antenna scanning mode layer, and working mode layer. To aim at the problem of parameter modulation pattern, the parameter modulation pattern is identified by training the LSTM classification network. In order to solve the problem of radar mode recognition, this paper adopts the system clustering method and adds the numerical characteristics of the parameters to cluster analysis based on identifying the parameter modulation pattern and antenna scanning type.

Sliding window change point detection based dynamic network model inference framework for airport ground service process