Multifunction Radar Research Articles

Multi-function vortex array radar

In the realm of automation systems, multi-function radars serve as essential sensory components for self-driving vehicles and airbornes. Effective resource allocation management is crucial, requiring a high level of versatility to accomplish multiple tasks, especially, for increasingly miniaturized hardware. Here, we advance a balanced protocol for detecting, positioning, and tracking moving targets in real-time. Our protocol integrates efficient data processing methods with robust hardware. Specifically, detection signals are modulated by optical vortices for imaging, and real time processing of the image field facilitates target positioning and tracking. Moreover, the protocol extends its utility to serve as a topographic laser profiling system for natural landscapes, highlighting its adaptability. This adaptability and versatility well position the proposed protocol to support a wide range of applications, spanning self-driving vehicles and aerial systems, underscoring its potential significance across multiple platforms.

BiLSTM-Filt: Neural network for radar word segmentation

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

Photonic generation and programmable switching of variable-band chirped waveforms with immunity to power fading.

A photonic approach for generating and programmable switching variable-band chirped waveforms by using a dual-parallel Mach-Zehnder modulator (DPMZM) is proposed and experimentally demonstrated. By coupling binary switching codes with the baseband chirped signal and applying them into the RF input port of DPMZM, the variable-band chirped waveforms can be generated and fast switched. The switching speed is consistent with the input code bit rate. Moreover, by properly adjusting the bias voltages, the generated signals can be anti-dispersion transmitted over different distances. Full experimental verification on the generation and programmable switching of the chirped waveforms centered at 2.1, 6.3, and 8.4 GHz or 1.5, 4.5, and 6 GHz over 10 or 20 km standard single-mode fibers with 4 or 100 Mbps switching rates are successfully carried out. The proposed approach features compact architecture, programmable switching capability and immunity to power fading, which are significant in distributed multifunction radar networks with optical fiber-based transmission.

Efficient Jamming Policy Generation Method Based on Multi-Timescale Ensemble Q-Learning

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate.

Investigations into optimum non‐periodic bursts scheme in radar pulse train designed for fixed‐wing drone detections by multi‐function radar

AbstractDrone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.

WITHDRAWN: A Novel Wideband Circular Polarized Filtering Antenna based on Left Hand Metamaterial

Across a broad frequency range, this reconfigurable antenna can be used for 5G, multi-function radars and automobile applications. To obtain ultra-wideband (UWB), the monopole antenna patch is suitably etched, and a stub is inserted to the feed line. In the meantime, an electromagnetic band gap (EBG) and rectangle slots are added in the partial ground to expand its operational bandwidth, which ranges from 2.7GHz to 23GHz. Size shrinkage rises to 65% when step feed is used in conjunction with partial grinding. Loading left-hand metamaterial (LHM) behind the monopole patch and etching slots at the corners of the rectangular patch results in circular polarization and an increase in gain. By placing a filter across the feedline, the recommended antenna's frequency and bandwidth can be adjusted as needed, enabling bandwidth reconfiguration. The via loaded in the patch extends the axial ratio (AR) bandwidth from 5GHz to 21GHz.The recommended antenna's step feed has a vertical slit that improves impedance matching over a wide operating spectrum.

Object detection based deinterleaving of radar signals using deep learning for cognitive EW

In a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.

Multifunction Radar Working Mode Recognition With Unsupervised Hierarchical Modeling and Functional Semantics Embedding Based LSTM

Multifunction Radar Working Mode Recognition With Unsupervised Hierarchical Modeling and Functional Semantics Embedding Based LSTM

Pulse‐level work state recognition of multifunction radar based on MC‐RSG

AbstractAccurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse‐level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC‐RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions.

Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of a non-cooperative MFRs. However, due to the increased flexibility of MFRs, radar work modes with emerging new modulations and control parameters always appear, and the supervised classification method suffers performance degradation or even failure. Unsupervised learning and clustering of MFR pulse sequences becomes urgent and important. This paper establishes a unified multivariate MFR time series feature extraction and clustering framework for MFR work mode recognition. At first, various features are collected to form the feature set. The feature set includes features extracted through deep learning based on recurrent auto-encoders, multidimensional time series toolkit features, and manually crafted features for radar inter-pulse modulations. Subsequently, several feature selection algorithms, combined with different clustering and classification methods, are used for the selection of an “optimal” feature subset. Finally, the effectiveness and superiority of the proposed framework and selected features are validated through simulated and measured datasets. In the simulated dataset containing 20 classes of work modes, under the most severe non-ideal conditions, we achieve a clustering purity of 73.46% and an NMI of 84.28%. In the measured dataset with seven classes of work modes, we achieve a clustering purity of 86.96% and an NMI of 90.10%.

Down-conversion and frequency spectrum convolution of the broadband signal based on active mode-locking technology.

A multifunction processor for a broadband signal based on the active mode-locking optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. The central frequency down-conversion and frequency spectrum convolution of the target broadband signal (TBS) are realized by just tuning the wavelength of the optical carrier or by the time domain product, respectively. To achieve the central frequency down-conversion of the TBS, an optical tunable delay line (OTDL) is adopted to match the delay time of the OEO loop with the repetition period of the TBS. Then the spectrum convolution of the TBS is produced by just injecting a lower frequency signal consistent with the free spectral range (FSR) of the OEO loop. Moreover, the frequency convolution repetition is also greatly increased by harmonic mode-locking injection. The equivalent bandwidth of the TBS is enlarged by ∼50 times, benefiting from the frequency convolution. The central frequency conversion flexibility and the bandwidth compatibility are also discussed in detail. This work provides a multifunction processor system and may have potential usage in multifunctional integrated radar systems.

Task Scheduling in Cognitive Multifunction Radar using Model-based DRL

Task Scheduling in Cognitive Multifunction Radar using Model-based DRL

Syntactic modeling and neural based parsing for multifunction radar signal interpretation

Syntactic modeling and neural based parsing for multifunction radar signal interpretation

Multi-Function Radar Modeling: A Review

Multi-Function Radar Modeling: A Review

Multiple Pattern Synthesis in Four-Dimensional Antenna Arrays Using the Quasi-Newton Memetic Optimization Method

The evolution in wireless radio communication systems has fueled the development of array antenna designs toward the direction of inventive, economical, and outcome-driven solutions. This article explored unorthodox techniques for designing array antennas for multiple radiation pattern synthesis by eradicating the required attenuators and phase shifters. An economical alternate design model is proposed with “Time-modulated” antennas capable of scanning and steering the beam electronically. “Time” is employed as a four-dimensional antenna parameter, and hence, the “Time-modulated” antennas are referred to as four-dimensional (4D) antennas. The idea is conceptualized by controlling high-speed switches attached to the antennas in a periodic way to produce the desirable excitation and phase tapering. So, the requirement of costly phase shifters and attenuators is eliminated. This work expanded the idea of “Time-modulation” for synthesizing multiple radiating beams such as monopulse, scanned beams, and shaped beams for multifunction radar systems. An appropriate switch sequence is designed to produce a concurrent sum-difference pattern for monopulse radars. Simultaneous scanning patterns and shaped flat-top patterns are also proposed for multifunction radars. To achieve the goals, two sets of 4D linear arrays are designed and the modulation parameters switch ON time and periods are optimized with a quasi-Newton memetic GA (Genetic algorithm). All the optimal outcomes are reported and also compared with other counterpart outcomes to show the efficacies of the “Time-modulation” concept for the said applications.

Self-Supervised Contrastive Learning for Extracting Radar Word in the Hierarchical Model of Multifunction Radar

Self-Supervised Contrastive Learning for Extracting Radar Word in the Hierarchical Model of Multifunction Radar

Unsupervised Techniques for Identifying the Mode of a Multi-Functional Radar with Varying Pulse Sequences

Unsupervised Techniques for Identifying the Mode of a Multi-Functional Radar with Varying Pulse Sequences

Online few-shot learning for multi-function radars mode recognition based on backtracking contextual prototypical memory

Modern multi-function radars (MFRs) can flexibly generate multiple fine-grained working modes for different missions through programmable parameters. Precise recognition of these working modes by analyzing the parameter combinations lays foundation for situational awareness. This recognition becomes more challenging to electromagnetic reconnaissance system when prior information of radiation source is unavailable and effective labeled signal data is not sufficient in adversarial scenarios. The presence of incremental mode involved in radar data flow requires that the recognition model enables to dynamic adjust and online perceive current data. This issue incorporates online learning to few-shot learning to accomplish efficiently recognition to a novel mode with the support of small amount of data, and still retain model's ability to recognize existing modes. This paper designed a backtracking contextual prototypical memory (BCPM) network for online MFR mode recognition. The proposed method learns temporal and spatial information from data stream as a reference predicting the signal segments as existing working modes or a novel mode. The BCPM network also designs a backtracking module and an alignment regularization term to utilize the data without annotation adequately and obtain a more reliable category representation. The experimental results verified the proposed method's good performance and high robustness to non-ideal factors and distractors.

Joint Optimization of Jamming Type Selection and Power Control for Countering Multifunction Radar Based on Deep Reinforcement Learning

Developing intelligent jamming methods to combat the multi-function radar (MFR) has become a vital task in electronic warfare, because the MFR can tune into different working modes according to surrounding environment information. In this paper, we present a solution to the limitations of conventional jamming methods, including strong prior knowledge dependence and inaccurate selection strategies.Specifically, we study a joint optimization of jamming type selection and power control task (JO-JTSPC) for a general radar countermeasure scenario. In particular, we first model the sequential decision-making task JO-JTSPC as a Markov Decision Process (MDP). Subsequently, considering the differences in the designed action space, we accordingly develop two algorithms, i.e., dueling double deep Q-learning (D3QN) and hybrid proximal policy optimization (HPPO), to solve the optimization problem. Taking into consideration the threat level of various MFR working modes and the corresponding required jamming effect, we elaborately design the reward function of MDP as a weighted summation of the mode switching factor, jamming performance factor and jamming power factor. Further, the learned polices of these algorithms are derived based on the designed reinforcement learning (RL) elements. Extensive simulation results demonstrate that the proposed algorithms can learn highly adaptive polices in the radar countermeasure scenarios and achieve good jamming performance.

Design of Cognitive Jamming Decision-Making System Against MFR Based on Reinforcement Learning

Electronic countermeasures are developing towards intelligence. The multifunctional radar changes its working state in real time according to the task requirements. The traditional jamming decision-making method can not quickly adjust the jamming mode according to the jamming effect and environmental changes. It is not suitable for complex and changeable multifunctional radar. For this problem, a cognitive jamming decision-making system based on reinforcement learning is designed. For the evaluation of jamming effect, an evaluation method based on Improved Sparrow Search Algorithm-Support Vector Machine (ISSA-SVM) is proposed, which can evaluate the jamming effect online. The results are fed back to the jammer to provide basis for jamming decision-making. For the jamming decision-making process, the interference experience table is combined with Heuristic Accelerated Q-Learning (HAQL). A jamming decision-making method based on adaptive HAQL algorithm is proposed, which adaptively adjusts the jamming mode and jamming power according to the change of radar threat level. A one-to-one interference scenario is established and simulated. The results show that the system can realize the closed-loop cognitive interference of learning and confrontation.