Intelligent Radar System Research Articles

Semantic Feature-Enhanced Graph ATtention Network for Radar Target Recognition in Heterogeneous Radar Network

Radar target recognition (RTR), as a key technique of intelligent radar systems, has been widely investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Considering that most existing methods are based on a single radar or the homogeneous radar network, we extend RTR to the heterogeneous radar network to improve the robustness of RTR, which uses the radar cross Section (RCS) signals at low SNRs by further exploiting the frequency-domain information. In this article, a Semantic Feature-Enhanced Graph ATtention Network (SFE-GAT) is proposed, which extracts semantic features from both the source and transform domains via the long short-term memory (LSTM) and GAT layers, then fuses them in the semantic space using an attention mechanism, and further distills higher-level semantic features using a GAT layer before classification. Extensive experiments are carried out to validate that the proposed SFE-GAT model can greatly improve the RTR accuracy in the low SNR region.

Accurate Deep CNN-Based Waveform Recognition for Intelligent Radar Systems

Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically challenged by practical problems of scalability and reliability. Deep learning (DL) with the ability to automatically learn the representational features is leveraged to handle the aforementioned obstacles effectively. In this work, we proposed a high-accurate waveform recognition method for intelligent radar systems by developing a novel residual-attention multiscale-accumulation convolutional network (RamNet). By deliberately incorporating the residual connection and attention connection in selective-feature improvement blocks, RamNet can enrich high-impact features without vanishing gradient. Moreover, a structural multiscale-accumulation connection is deployed to improve feature utilization by gathering the high-relevant features at multiple signal resolutions. Experimental results from exhaustive simulation demonstrate that RamNet recognizes waveform robustly under rigorous channel impairments and presents superior performance compared to traditional ML and state-of-the-art DL models.

Multi-Target Robust Waveform Design Based on Harmonic Variance and Mutual Information

Cognitive radar is an intelligent radar system, and adaptive waveform design is one of the core problems in cognitive radar research. In the previous studies, it is assumed that the prior information of the target is known, and the definition of target spectrum variance has not changed. In this paper, we study on robust waveform design problem in multiple targets scene. We hope that the upper and lower bounds of the uncertainty range of robustness are more close to the actual situation, and establish a finite time random target signal model based on mutual information (MI). On the basis of the optimal transmitted waveform and robust waveform based on MI, we redefine the target spectrum variance as harmonic variance, and propose a novel robust waveform design method based on harmonic variance and MI. We compare its performance with robust waveform based on original variance. Simulation results show that, in the situation of multiple targets, compared to the original variance, the MI lifting rate of robust waveform based on harmonic variance relative to the optimal transmitted waveform in the uncertainty range has great improvement. In certain circumstances, robust waveform based on harmonic variance and MI is more suitable for more targets.

Categorical Analysis Of Logical Networks For Radar Recognition Systems

The purpose of the research is to create universal methods for designing intelligent recognition systems for low-visibleradar targets, to develop a general model of logical network, to adapt it to the needs of practical application in intelligentradar systems and to improve methodology of air objects detection. Also, the aim of the research is to solve problemsrelated to the relationship between the theory of categories and predicates algebra, mathematical kit of the artificialintelligence technology. The categorical model of logical networks is considered as an object of research. Mathematicalmodels of formalization and presentation of information, as well as predicate and modified categories are chosen by themethod. The formalization of the studied models by the declarative language created in the course of research is carriedout, allows to investigate models by means of the general universal model of a logical network.

Ground vehicle target signature identification with cognitive automotive radar using 24–25 and 76–77 GHz bands

The authors present cognitive automotive radar (CARr) as a significant capability to the sensing technology suite of autonomous driving vehicles. CARr is a closed-loop intelligent radar system utilising the automotive frequency bands of 24–25 and 76–77 GHz for the purposes of ground vehicle target signature identification. The authors consider specific cases of ground vehicle recognition and ground vehicle class identification in the presence of aspect angle uncertainty using forward-looking automotive radar. The transmit-adaptive waveforms are based from signal-to-noise ratio and mutual information optimisation metrics. In this study, two new adaptive waveform techniques with the flexibility to accommodate angular uncertainty probability distributions are introduced. The classification performance of these new waveforms is compared against the receive-adaptive wideband pulsed and other transmit-adaptive waveforms in the presence of angular uncertainties characterised by the uniform and truncated normal distributions. To ensure the validity of results, high-fidelity electromagnetic simulated radar cross-section signatures generated from scaled-to-true-physical-size ground vehicle computer-aided design models are utilised.

Adaptive Waveform Design for Multiple Radar Tasks Based on Constant Modulus Constraint

Cognitive radar is an intelligent system, and it can adaptively transmit waveforms to the complex environment. The intelligent radar system should be able to provide different trade-offs among a variety of performance objectives. In this paper, we investigate the mutual information (MI) in signal-dependent interference and channel noise. We propose a waveform design method which can efficiently synthesize waveforms and provide a trade-off between estimation performance and detection performance. After obtaining a local optimal waveform, we apply the technique of generating a constant modulus signal with the given Fourier transform magnitude to the waveform. Finally we obtain a waveform that has constant modulus property.

Multi-Mode Radar Target Detection and Recognition Using Neural Networks

In typical radar systems, the process of recognizing a target requires human involvement. This human element makes radar systems not fully reliable due to unstable performance that varies between operators. This paper describes an intelligent radar system which addresses this problem in a border surveillance environment. The proposed radar system is capable of automatically detecting and then classifying different targets using an artificial neural network trained with the Levenberg-Marquardt algorithm. The training and test sets presented to the neural network are composed by high-resolution Inverse Synthetic Aperture Radar pictures obtained by the radar's detection module. Simulation results show that the intelligent radar system can reliably detect and distinguish the different objectives. Moreover, the radar system can outperform human operators and another radar system that deals with similar objectives. These results indicate that future intelligent systems can potentially replace human radar operators in this critical security setting.

Towards intelligent radar systems

Based on the author's previously published work that relates to Bayes' decision making in a fuzzy and random environment [ Fuzzy Sets and Systems 35 (1990) 197–212] and the work addressing the problem of fuzzy set ordering over the real line [ Fuzzy Sets and Systems 50 (1992) 237–246], the theory of fuzzy sets is applied here to radar detection problems. This has the purpose of providing the possibility for accommodating the operation of radar systems according to the particular situation and thus, making these systems, in some sense, intelligent. First, the prior probabilities and cost assignments that are involved in the Bayes risk expression are fuzzified. Second, in addition to fuzzy prior probabilities and costs, an unknown fuzzy signal amplitude is considered. This leads to a restoration of the usefulness of the Bayes criterion in the area of radar, one that has lately been neglected to the advantage of the Neyman-Pearson criterion. Also, a parallelism between the fuzzy approach and the minimax Bayes risk approach is drawn.