Cognitive Radar Research Articles

Game Theory and Reinforcement Learning in Cognitive Radar Game Modeling and Algorithm Research: A Review

Game Theory and Reinforcement Learning in Cognitive Radar Game Modeling and Algorithm Research: A Review

Latent-Maximum-Entropy-Based Cognitive Radar Reward Function Estimation With Nonideal Observations

Latent-Maximum-Entropy-Based Cognitive Radar Reward Function Estimation With Nonideal Observations

Are there subintentional actions?

AbstractWhen I fiddle with my hair, or adjust my posture, it is plausible that these activities fall well below my cognitive radar. Some have argued that these are examples of ‘sub‐intentional actions’, actions which are not intentional under any description at all. If true, they are direct counterexamples to the dominant view on which the difference between actions and other events is their intentionality. In this paper, I argue that the case for sub‐intentional actions fails. Firstly, I show that the main argument for the sub‐intentionality of these actions has a structural fault. Secondly, I argue that two apparently natural ways to remedy this fail. Thirdly, I argue that one of the main arguments for thinking of the phenomena as actions undermines thinking of them as sub‐intentional. Finally, I argue that a natural defensive move for the defender of sub‐intentional actions actually undermines the theoretical significance of the view. Ultimately, my aim is to show that although the case for sub‐intentional actions seemed both simple and compelling, it is in fact deeply troubled.

TRENDS OF THE MICROWAVE PHOTONIC RADARS

Today, the world's leading countries are intensively working on the development of new generation radars - microwave photonic radars. Microwave photonic radars make it possible to significantly reduce the mass and size characteristics of radar stations, to increase the information capability and range of target detection due to the reduction of losses in long communication lines when using optical fiber, to ensure high immunity due to the significantly lower sensitivity of optical-electronic equipment and fiber-optic lines of communication connection to external electromagnetic influences. Microwave photonics provides wide bandwidth, flat response, low loss transmission, multi-dimensional multiplexing, ultra-fast analog signal processing and immunity to electromagnetic interference. Radar implementation in the optical domain can provide better resolution, coverage, and speed performance, which would be difficult to implement with traditional electronics. The review article examines the state of development and system architectures of such photonic radars as optoelectronic hybrid radars, all-optical radars, multifunctional microwave photonic radar systems, distributed microwave photonic radars, software-defined radars, and cognitive radars. New technologies in this field and possible future directions of research are discussed. As an example, a broadband microwave photon radar reproduced on the basis of a microcircuit is considered. The broadband signal generator and receiver are built into the silicon crystal on the insulator. A high-precision distance measurement with a resolution of 2.7 cm and an error of less than 2.75 mm was obtained. Visualization of multiple targets with complex profiles has been implemented. But the performance of most integrated microwave photonic chips is not yet satisfactory for practical radar applications. Monolithic integration of key microwave photonic subsystems is also not mature enough for practical applications, so hybrid integration of devices fabricated on their optimal integration platforms is of practical interest. At present, indium phosphide, silicon nitride and silicon on insulator are the three leading platforms for photonic integration

Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

AbstractTraditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

Transfer-Based DRL for Task Scheduling in Dynamic Environments for Cognitive Radar

Transfer-Based DRL for Task Scheduling in Dynamic Environments for Cognitive Radar

Adaptive Algorithms in Radar Signal Processing

The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency.

Deep Reinforcement Learning for Cognitive Radar Spectrum Sharing: A Continuous Control Approach

Deep Reinforcement Learning for Cognitive Radar Spectrum Sharing: A Continuous Control Approach

Worst-Case Riemannian Optimization with Uncertain Target Steering Vector for Slow-Time Transmit Sequence of Cognitive Radar

Worst-Case Riemannian Optimization with Uncertain Target Steering Vector for Slow-Time Transmit Sequence of Cognitive Radar

Timely Target Tracking: Distributed Updating in Cognitive Radar Networks

Timely Target Tracking: Distributed Updating in Cognitive Radar Networks

Cognitive Radar Waveform Design Method under the Joint Constraints of Transmit Energy and Spectrum Bandwidth

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure.

Design of anti‐jamming decision‐making for cognitive radar

AbstractWith the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

Memory‐based deep reinforcement learning for cognitive radar target tracking waveform resource management

AbstractA cognitive radar (CR) system can offer enhanced target tracking performance due to its intelligence on the perception‐action cycle, wherein a CR adaptively allocates the limited transmitting resources based on its perception of surrounding environments. To effectively manage the transmit waveform resource for the target tracking task, CR resource management problem is formulated under the partially observable Markov decision process framework. The sequential decision‐making and the inherent partial observability for target tracking problem are considered. In the proposed method, a long short‐term memory (LSTM)‐based twin delayed deep deterministic policy gradient (TD3) algorithm is developed to effectively solve the problem. A reward function is designed considering Haykin's cognitive executive attention mechanism for radar systems such that the CR resource management policy has stability in the decision of transmit waveform, which follows the principle of minimum disturbance. Simulation results demonstrate the superiority of the proposed LSTM memory‐based TD3 with improved target tracking performance and increased mean rewards for CR.

Multiple-Criteria Operational Reliability Performance Metric of a Metacognitive Tracking Radar

The concept of metacognition has been proposed and applied to radar system analysis to enhance the classical cognitive radar paradigm. Metacognition allows a cognitive radar to have self-awareness about its cognitive processes. To accurately compare various cognitive processes and select the best under the operational scenario, a performability metric capable of comparing system performance over various scales and units is proposed and analyzed. This correspondence expands previous work on radar operational reliability to provide a meta-reliability metric for a metacognitive tracking radar. The approach is tested and validated on a radar which tracks a target performing a composite maneuver involving constant velocity, constant turn, and constant acceleration.

Waveform Adaptation for Target Classification Using HRRP in a Cognitive Framework

Waveform adaptation is a key-feature for modern radar systems and essential for cognitive radar. In this work we present a concept for the enhancement of the classification performance by using optimised transmit waveforms and a Gaussian template matching high range resolution profile (HRRP)classifier. A straight forward approach is presented, aiming to improve specific parts of the confusion matrix which will be exploited within a cognitive framework. The optimisation includes different types of uncertainties and is designed during a training process to be accessed by a library. Taking different uncertainties into account, the calculation of the expected performance, the optimisation, the range side lobe constraint and the time-domain realisation is explained. A non-linear frequency modulation (NLFM) waveform is used since it provides a compression gain with range resolution and a constant envelope. Based on an electromagnetic simulation the concept is validated for different ground targets and aspect angle uncertainties. The adaptation is compared to a commonly used linear frequency modulation (LFM). The results of the mean performance improvement reached an enhancement between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${8.8}{\%}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${20.9}{\%}$</tex-math></inline-formula> .

Cognitive Dynamic Systems: A Review of Theory, Applications, and Recent Advances

The field of cognitive dynamic systems (CDSs) is an emerging area of research, whereby engineering learns from neuroscience. Under this framework, engineering systems are configured in a manner that mimics the human brain and improves the utility and performance of traditional systems. In essence, a CDS builds on Fuster’s paradigm of cognition and is fulfilled with the presence of five cognitive processes: the perception-action cycle, memory, attention, intelligence, and language. When augmented with these processes, a system can be classified as a CDS and is afforded the capabilities of processing information and learning from experience through continued interactions with the environment. Tremendous benefit from adopting the CDS framework has been observed in the literature, especially in the fields of cognitive radio and cognitive radar. More recently, the framework has been extended to other areas, such as control theory, risk control, and the Internet of Things; where the potential for drastic performance improvements has been evident in the literature. This comprehensive article presents a thorough background and exposition of the CDS framework and each field where it has been applied. In addition, we provide a comprehensive review of the recent advancements and related works in each domain by summarizing the key facts relating to the methodologies, findings, and limitations of the surveyed papers. Our novel contributions involve being the first source of centralized information on this topic and forming the foundation for future research efforts by presenting suggestions regarding worthwhile avenues for further investigation.

Joint recognition and parameter estimation of cognitive radar work modes with LSTM-transformer

The recent developed cognitive radars can implement flexible work modes with programmable modulation types and optimized modulating values for each mode definition parameter. Automatic analysis of these work modes is a significant challenge for modern electromagnetic reconnaissance receivers. In this paper, a Multi-Output Multi-Structure (MOMS) learning-based processing framework is proposed for Joint inter-pulse automatic Modulation Recognition and Parameter Estimation (JMRPE-MOMS). We propose a label construction method as a feature interpretation method of the network to facilitate MOMS learning and utilize the correlations between labels for performance gain. Moreover, an LSTM-Transformer is designed to mine deep time-series characteristics, which can model local and global relationships and reduce quantization loss. The proposed framework can perform joint modulation recognition and parameter estimation (JMRPE) tasks simultaneously with flexible output structures including scalar output and vector output with fixed or variable sizes. Extensive simulations are performed based on the simulated radar work modes defined with pulse repetition interval (PRI) sequences. The simulation results validate the effectiveness and superiority of the proposed method especially under non-ideal electromagnetic environments.

Neural Network Component of Modern Information System on Mobile Platforms: LPI Cognitive Radar System

The problem of building modern systems for collecting, processing and presenting information for moving platforms, characterized by the presence of a neural network with deep learning, sensors with preprocessing, systems processing and presenting information, is considered. In modern systems the physics of the processes does not change, and accordingly, the algorithm for extracting signals from under the noise doesn't change either but is supplemented by a neural network that learns in the process of processing information to perform an applied task. Implementation example shown the introduction of artificial intelligence technology for the design a cognitive radar on a moving platform facilitates the transition from adaptive systems to cognitive ones. Keywords: artificial Intellect, deep learning, neural network, cognitive radar, multiprocessor, Frequency modulation continuous wave, Radar Cross-Section, Solid State Transmitter.

Natural Intelligence as the Brain of Intelligent Systems

This article discusses the concept and applications of cognitive dynamic systems (CDS), which are a type of intelligent system inspired by the brain. There are two branches of CDS, one for linear and Gaussian environments (LGEs), such as cognitive radio and cognitive radar, and another one for non-Gaussian and nonlinear environments (NGNLEs), such as cyber processing in smart systems. Both branches use the same principle, called the perception action cycle (PAC), to make decisions. The focus of this review is on the applications of CDS, including cognitive radios, cognitive radar, cognitive control, cyber security, self-driving cars, and smart grids for LGEs. For NGNLEs, the article reviews the use of CDS in smart e-healthcare applications and software-defined optical communication systems (SDOCS), such as smart fiber optic links. The results of implementing CDS in these systems are very promising, with improved accuracy, performance, and lower computational costs. For example, CDS implementation in cognitive radars achieved a range estimation error that is as good as 0.47 (m) and a velocity estimation error of 3.30 (m/s), outperforming traditional active radars. Similarly, CDS implementation in smart fiber optic links improved the quality factor by 7 dB and the maximum achievable data rate by 43% compared to those of other mitigation techniques.

A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar

Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.