Radar Mode Research Articles

Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations

As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using CP SAR. This study aims to explore the potential of C-band CP SAR for mangrove monitoring applications, with the objective of identifying the most effective CP SAR descriptors for mangrove discrimination. A systematic comparison of 52 well-known CP features is provided, utilizing CP SAR data derived from the reconstruction of C-band Gaofen-3 quad-polarimetric data. Among all the features, Shannon entropy (SE), a random polarimetric constituent (VB), Shannon entropy (SEI), and the Bragg backscattering constituent (VG) exhibited the best performance. By combining these four features, we designed three supervised classifiers—support vector machine (SVM), maximum likelihood (ML), and artificial neural network (ANN)—for comparative analysis experiments. The results demonstrated that the optimal polarimetric feature combination not only reduced the redundancy of polarimetric feature data but also enhanced overall accuracy. The highest accuracy of mangrove extraction reached 98.04%. Among the three classifiers, SVM outperformed the other classifiers in mangrove extraction, while ML achieved the highest overall classification accuracy.

The Future of Radar Space Observation in Europe—Major Upgrade of the Tracking and Imaging Radar (TIRA)

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34m Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe.

Adaptive Multi-Function Radar Temporal Behavior Analysis

The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset. As a transitional phase towards Cognitive Multi-Functional Radar (CMFR), Adaptive Multi-Function Radar (AMFR) possesses the capability to emit identical waveform signals across different working modes and states for task completion, with dynamically adjustable waveform parameters that adapt based on scene information. From a reconnaissance perspective, the valid signals received exhibit sparsity and localization in the time series. To address this challenge, we have redefined the reconnaissance-focused research priorities for radar systems to emphasize behavior analysis instead of pattern recognition. Based on our initial comprehensive digital system simulation model of a radar, we conducted reconnaissance and analysis from the perspective of the reconnaissance side, integrating both radar and reconnaissance aspects into environmental simulations to analyze radar behavior under realistic scenarios. Within the system, waveform parameters on the radar side vary according to unified rules, while resource management and task scheduling switch based on operational mechanisms. The target in the reconnaissance side maneuvers following authentic behavioral patterns while adjusting the electromagnetic space complexity in the environmental aspect as required. The simulation results indicate that temporal annotations in signal flow data play a crucial role in behavioral analysis from a reconnaissance perspective. This provides valuable insights for future radar behavior analysis incorporating temporal correlations and sequential dependencies.

Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

Ubiquitous radar has significant advantages over traditional radar in detecting and identifying low, slow, and small (LSS) targets in a strong clutter environment. It effectively addresses challenges faced in low-altitude target monitoring within the low-altitude economy (LAE). The working mode of ubiquitous radar, which tracks first and identifies later, provides high-resolution Doppler data to the target identification module. Utilizing high-resolution Doppler data allows for the effective identification of LSS targets. To meet the needs of real-time classification, this paper first designs a real-time classification process based on sliding window Doppler data. This process requires the classifier to classify targets based on multiple rows of high-resolution Doppler spectra within the sliding window. Secondly, a multi-channel parallel perception network based on a 1D ResNet-SE network is designed. This network captures features within the rows of sliding window data and integrates inter-row features. Experiments show that the designed real-time classification process and multi-channel parallel perception network meet real-time classification requirements. Compared to the 1D CNN-MLP multi-channel network, the proposed 1D ResNet-MLP multi-channel network improves the classification accuracy from 98.71% to 99.34%. Integrating the 1D Squeeze-and-Excitation (SE) module to form the 1D ResNet-SE-MLP network further enhances accuracy to 99.58%, with drone target accuracy, the primary focus of the LAE, increasing from 97.19% to 99.44%.

An adaptive synthetic method for long sequence radar mode recognition

AbstractRadar work mode recognition is crucial to analyse radar behaviour and intention. There are some challenges limiting the recognition of long sequences with multiple mode classes. First, the performance of recognition method relies on precise segregation of intercepted sequence, which is often unfeasible in reality. Second, the states at the boundaries of adjacent modes may create extraneous mode samples that intervenes the recognition. Third, current methods fail to deal with the scenarios where multiple modes share the same state sequence. To address these problems, a novel forward matching method (FMM) is proposed, comprising a shortest path method (SPM) for intra‐mode recognition, a matching strategy, and an adjustment mechanism. SPM is to provide potential recognition for short fragments of the given long sequence. The matching strategy is to assess the availability of current recognition. The adjustment mechanism tunes the segregation and improves the subsequent recognition. FMM offers several distinct advantages. First, the model can explicitly characterise the mode transition probability and is totally interpretable. Second, FMM can distinguish intentional ambiguities, alleviate mosaic ambiguity and probability deviation associated with inter‐mode recognition. Third, FMM is extendable to integrate with other intro‐mode recognition methods to cater to various scenarios.

Multi‐function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network

AbstractIn modern electronic warfare, multi‐function radar work mode recognition is increasingly crucial. However, the challenges posed by complex electromagnetic environments, such as lost pulses, spurious pulses, and measurement errors, along with the reliance of traditional multi‐task learning strategies on clean samples, make it difficult for existing algorithms to achieve satisfactory recognition performance in real‐world scenarios. To address these issues, this paper introduces a novel residual shrinkage reconstruction recurrent neural network (RS‐RRNN). The network uses a Gated Recurrent Unit as its backbone to extract temporal features and enhances feature extraction by reconstructing the GRU's input, while also reducing dependence on clean samples. These features are then processed through a residual shrinkage structure to reduce noise, which significantly improves the model's robustness in non‐ideal scenarios. Simulations demonstrate that RS‐RNN has better performances in accuracy and robustness than existing networks.

The method of threat assessment and interference distribution within the framework of cognitive electronic warfare

Problem statement. Electromagnetic spectrum control (EMC) plays an important role in conflict radar. The EMS operational area has unique characteristics that can be used to gain superiority over the enemy. The use of advanced developments in cognitive electronic warfare (CRAB) makes it possible to achieve a dominant position in the use of EMS. The combination of artificial intelligence and machine learning technologies with advanced electronic warfare (EW) technologies has made it possible to take an important step in ensuring maneuverable operations in a duel situation aircraft (LA) – radar station (radar). The presented approach demonstrates the increased efficiency of the CRE due to the use of the threat assessment and interference distribution method (SDR). Goal. The purpose of the article is to show how modern technologies provide a noise distribution strategy that allows aircraft to survive in a dueling situation, despite its complexity. Results. It is shown that the ESD method uses various radar jamming modes, such as: interaction between types and sources of jamming, relative frequency and bandwidth, uncertainty of the threatening environment, and also models the evaluation of radar effectiveness from search to guidance. Practical significance. The article confirms that the conflict radar scenario, which takes into account threat assessments for aircraft and determines the distribution of interference for radar, allows us to determine the optimal interference strategy.

Real-Time Millimeter-Wave Imaging With Linear Frequency Modulation Radar and Scattered Power Mapping

Real-Time Millimeter-Wave Imaging With Linear Frequency Modulation Radar and Scattered Power Mapping

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.

Radar Target Classification Using Enhanced Doppler Spectrograms with ResNet34_CA in Ubiquitous Radar

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target’s Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%.

Intrapulse Modulation Radar Signal Recognition Using CNN with Second-Order STFT-Based Synchrosqueezing Transform

Intrapulse modulation classification of radar signals plays an important role in modern electronic reconnaissance, countermeasures, etc. In this paper, to improve the recognition rate at low signal-to-noise ratio (SNR), we propose a recognition method using the second-order short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST2) combined with a modified convolution neural network, which we name MeNet. In particular, the radar signals are first preprocessed via the time–frequency analysis and STFT-based FSST2. Then, the informative features of the time–frequency images (TFIs) are deeply learned and classified through the MeNet with several specific convolutional blocks. The simulation results show that the overall recognition rate for seven types of intrapulse modulation radar signals can reach 95.6%, even when the SNR is −12 dB. Compared with other networks, the excellent recognition rate proves the superiority of our method.

Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.

Assessment of Sentinel-6 SAR mode and reprocessed Jason-3 sea level measurements over global coastal oceans

With dedicated coastal processing strategies and advanced Delay-Doppler technique, the quality of altimeter data from Low-Resolution Mode (LRM) and Synthetic Aperture Radar (SAR) mode altimeters in coastal areas have been greatly improved. In this study, we present a new 20-Hz along-track sea level anomaly (SLA) dataset of Jason-3 within 100 km to the global coastlines using the modified SCMR (Seamless Combination of Multiple Retrackers) processing strategy. The new reprocessed Jason-3 dataset, along with Sentinel-6 Michael Freilich (MF) SAR mode data, are evaluated and validated over global coastal oceans. The evaluation results show that the modified SCMR has significantly increases the data availability by 16%–67% for Jason-3 when compared to the official SGDR MLE4 dataset, especially in the last 5 km to the coast. The resultant data availability retains >90% beyond 5 km to the coast and 80% within 5 km to the coast, which is slightly higher (2%–10%) than that obtained by the Sentinel-6 MF. Most importantly, the modified SCMR mitigates the hump artifacts observed for the SLA spectrums of LRM altimeters, which makes the noise level of 20-Hz SLA estimates from Jason-3 (5.52 cm) comparable with that from Sentinel-6 MF (5.42 cm). This result demonstrates that the modified SCMR strategy would improve the LRM altimeters' capability of monitoring the mesoscale eddies. The evaluation results also show that the Sentinel-6 MF SAR mode data obtain higher data precision but lower data availability than reprocessed Jason-3 LRM data, especially in the 0–5 km coastal strip and mid-to-high latitude (>40°N or < 40°S) regions. Although the quality of altimeter data in the 0–5 km coastal strip has been significantly improved, the validation results against tide gauges demonstrate that the degraded performance still occurs when compared to the results beyond 5 km offshore. The significant discrepancy between tide gauge records and altimeter data is found in places such as sheltered bays and archipelagos where the land contamination is severe, and thus the development of dedicated coastal retrackers and corrections for SAR mode altimeters is still of great importance. Finally, the good consistency between reprocessed Jason-3 LRM and Sentinel-6 MF SAR mode altimeter datasets has been found by examining the inter-mission SLA biases (−0.12 ± 0.01 m).

Cross-domain prototype similarity correction for few-shot radar modulation signal recognition

The new classes of radar signals are increasingly difficult to acquire under non-cooperative environments, which makes it difficult to support convolutional neural network training with limited labeled samples. The few-shot learning (FSL) methods have shown great performance in classification with limited labeled samples, but the FSL methods ignore that the class distributions between the new and original tasks are significantly different, resulting in a massive challenge in identifying new radar signals. To solve this problem, a few-shot radar modulation signal recognition method based on cross-domain prototype similarity correction (CDPSC) is proposed. Specifically, a residual feature tokenizer transformer (RFTT) model embedded with a pooling token generation block is designed to focus on the important features and improve the ability to represent samples. Meanwhile, the proposed domain prototype similarity mapping (DPSM) strategy adaptively learns the class mapping, reduces the inter-domain difference through feature distribution alignment, and effectively corrects the target domain prototypes. In addition, we introduce a sample prototype embedding (SPE) strategy in the training phase, which can reduce the intra-class distance and increase the inter-class distance. Experimental results demonstrate that the CDPSC method is superior to typical FSL methods in recognition accuracy under different sample numbers.

Unsupervised recognition of radar signals combining multi-block TFR with subspace clustering

In the realm of radar systems, the proliferation of new modulation techniques introduces an increased level of complexity in the identification of both existing and potentially novel modulation schemes. Conventional supervised recognition methodologies, which rely heavily on labeled datasets, exhibit limitations in distinguishing amongst a multitude of unlabeled signals. This paper introduces a novel framework that synergizes Subspace Clustering (SC) with Multi-Block Time-Frequency Representations (TFRs, denoted as mT), referred to as SC-mT. This framework leverages subspace clustering for the unsupervised categorization of radar modulations and employs an innovative multi-block strategy to augment classification precision. The process commences with the generation of TFR datasets for radar signals, followed by the construction of two distinct multi-block models, Model-A and Model-B. These models segment the radar TFRs into overlapping multi-block sets, utilizing the concept of random receptive fields. The subspace clustering algorithm is then applied to each block within the sub-TFR sets to procure an affinity matrix. The culmination of this process involves the aggregation of the affinity matrices from all blocks, facilitating the derivation of classification outcomes via spectral clustering. Empirical analyses affirm that the sequential integration of the SC-mT algorithm surpasses the classification efficacy of various conventional and cutting-edge algorithms. Notably, this algorithm attains a classification accuracy surpassing 90% for ten unlabeled signals, even in scenarios where the signal-to-noise ratio (SNR) is as low as -4 dB.

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%.

Design of non-uniform frequency-phase hybrid modulation radar waveform and segmented complementary mismatch filtering

Design of non-uniform frequency-phase hybrid modulation radar waveform and segmented complementary mismatch filtering

Suppression Method for Smeared Spectrum Jamming Based on Lv’s Distribution Combined with Biorthogonal Fourier Transform

Smeared spectrum (SMSP) jamming is a deceptive jamming technique commonly used against linear frequency modulation (LFM) radar. This technique produces high-density false targets that resemble a comb shape after pulse compression processing, resulting in both deceiving and suppressing effects. In order to suppress jamming effectively, a new antijamming technique has been proposed that combines Lv’s distribution (LVD) with biorthogonal Fourier transform (BFT). The main idea of this antijamming method is based on a three-step process: “parameter estimation—echo modulation—identification and suppression.” The first step involves obtaining the delay and chirp rate of the jamming from the LVD matrix of the radar echo. Based on the estimated parameter information, a reference signal is designed to modulate the jamming into a complete chirp signal. In the second step, biorthogonal Fourier transform is used to distinguish between the target’s echo and jamming based on the difference in the chirp rate. Finally, jamming is filtered using narrow-band filtering in the chirp rate domain, and the original echo is recovered by using biorthogonal inverse Fourier transform (IBFT). Simulation results demonstrate that the proposed method achieves high accuracy in estimating jamming parameters and is capable of suppressing false targets under high jamming-to-signal ratio (JSR) conditions.

Radar Modulation Recognition of Intra-Pulse Overlapping Signals Based on Object Detection

Radar Modulation Recognition of Intra-Pulse Overlapping Signals Based on Object Detection

Research on Jamming Technology for Dense False Targets in Linear Frequency Modulation Radar

Research on Jamming Technology for Dense False Targets in Linear Frequency Modulation Radar