Search

As one of the most promising next-generation radars, Moving platform based Distributed Aperture Radar (MDAR) cannot only coherently combining distributed apertures to obtain the same detection performance of a large aperture, but also enhance the detection and anti-damage capabilities through mobility and flexible deployment. However, time and phase synchronization among radars should be done before coherently combining due to internal clock differences and external propagation path differences. Moreover, grating lobes will generate as the distance between multiple radars usually exceeds half a wavelength, which affects the estimation accuracy of target angle. To obtain Coherent Parameters (CPs), this paper established a cognitive framework for MDAR based on closed-loop structure. And a multi-pulse correlation CPs estimation method considering motion conditions is proposed to improve estimation accuracy. In the meanwhile, an unambiguous angle estimation method based on array configuration accumulation is proposed considering platform motion characteristics. Finally, based on the simulation verification and the proposed framework, a prototype of a 3-node ground Moving platform based Distributed Coherent Aperture Radar (MDCAR) system is designed and experiments are conducted. Compared to a single radar, a maximum value of 14.2 dB signal-to-noise ratio improvement can be achieved, which can further enhance range detection accuracy. Besides, unambiguous angle estimation is also realized under certain conditions. This work is expected to provide support for the research and development of MDCAR.

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks. [ABSTRACT FROM AUTHOR]

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.

In order to improve the tracking performance of cognitive radar, this paper proposes a waveform selection algorithm for cognitive radar tracking assisted by amplitude information. Firstly, the framework of waveform parameter selection for cognitive radar tracking is discussed. Secondly, the performance of amplitude-assisted tracking probability data association is analyzed, and the method of waveform parameter selection is proposed. Finally, combined with the Interactive Multiple Model Unscented Kalman Filter (IMMUKF), an amplitude information assisted waveform selection algorithm for cognitive radar tracking is proposed. Simulation results show that the proposed algorithm can effectively improve the target tracking performance and reduce the probability of target tracking loss.

The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to study the waveform selection of radar seeker in the terminal guidance and tracking phase, this paper proposes a waveform selection algorithm in the guidance and tracking process of cognitive radar seeker. Firstly, we establish the processing flow of radar seeker waveform selection by combining the seeker guidance and observation filtering models; Secondly, we use the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to solve the nonlinear tracking problem. We minimize tracking error by searching for waveform parameters based on the maximum mutual information criterion. The simulation results show that the waveform selection algorithm can effectively improve the seeker tracking accuracy and provide stable guidance overload.

Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.

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.

Based on the obtained knowledge through ceaseless interaction with the environment and learning from the experience, cognitive radar continuously adjusts its waveform, parameters, and illumination strategies to achieve robust target tracking in complex and changing scenarios. Its waveform design has been receiving attention to improve tracking performance. In this paper, we propose a novel framework of cognitive radar waveform selection for the tracking of high-maneuvering targets. The framework considers the combination of Constant Velocity (CV), Constant Acceleration (CA), and Coordinate Turn (CT) motions. We also design Criterion-Based Optimization (CBO) and Entropy Reward Q-Learning (ERQL) methods to perform waveform selection based on this framework. To provide the optimum target tracking performance, it merges the radar and target into a closed loop, updating the broadcast waveform in real-time as the target state changes. The suggested ERQL technique achieves about the same tracking performance as the CBO while using much less processing time than the CBO, according to numerical results. The proposed ERQL method significantly increases the tracking accuracy of moving targets as compared to the fixed parameter approach.

In the spectrum-congested environment, weak target obscuration can be addressed by simultaneously shaping the ambiguity function and waveform spectrum based on cognitive radar a priori information. In this article, we investigate joint waveform design to suppress slow-time ambiguity function (STAF) disturbance power and overlapping frequency band energy. Thus, a novel design criterion involving minimizing the interested range-Doppler blocks of STAF and energy spectrum density stopband is constructed, which is subject to the waveform energy and peak-to-average ratio constraints. To cope with the resulting complex quartic optimization problem, a waveform design approach is proposed which utilizes the iterative sequential quartic optimization algorithm framework to obtain a closed-form solution at each iteration. Finally, the designed waveforms can suppress the interested range-Doppler block level while satisfying the spectral coexistence requirements. Numerical simulation results verify that the proposed method has higher STAF disturbance suppression performance than state-of-the-art methods. Meanwhile, this method also possesses the ability to reject narrow-band spectrum interference.

Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract A multiplatform radar network (MRN) composed of multiple transmitting and receiving facilities is advantageous over monostatic radar in moving target detection (MTD) and tracking due to the diversity gain. Within the framework of the Internet of Battlefield Things (IoBT), the authors propose that the airborne platforms equipped with the cognitive multimodal radar (CMR), the subarray‐based MIMO radar (SMR), the active electronically scanned array (AESA) radar, and the adaptive radar countermeasures (ARC) should team up in target detection and tracking. To obtain better target detection and tracking performance, the probing signals from the CMR, the SMR, and the AESA radar are adaptively optimised based on the scenario under consideration. The ARC consists of two parts: 1) RF signal sensing and recognition with deep neural network (DNN), that is, to detect, identify, and localise multiple radio frequency emitters; 2) intelligent fake return creation and jamming to confuse the enemy radars in real time in the field. This work aims to serve as a good reference for future researchers interested in developing MRN with diverse nodes and the general teaming of radars on the battlefield.

The Cognitive Radar is almost a new research topic, in which most efforts have been done for its conceptual description and the adaptive waveform design feature of these radars. While other aspect of adaptive action for optimum performance of cognitive radars has been ignored. In this paper, a framework for adaptive time resource management in Cognitive Radars is proposed. The main purpose of this paper is proposing an algorithm for time resource management, with incorporation of adaptive waveform design capability of cognitive radars, to enhance the radar performance for an efficient time resource usage. After developing the equations for radar time resource management using adaptive waveform design for the first time, an implementable algorithm is proposed and its performance is simulated and analysed. The results show that using the proposed algorithm resulted in a more efficient time resource management compared to the existing ones. [ABSTRACT FROM AUTHOR]

Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a 3D high-resolution target image with a reduced sampling cost. Firstly, based on the known knowledge of the target and WBI, provided by previous measurements, optimal sparse sampling in the 3D signal domain is conducted to reduce the number of sub-pulses and transceiving antennas by solving an optimization problem. Then, the detection and removal of the interfered signal components are conducted to provide the WBI information for following measurements and the interference-free signal cube for the target imaging process. Finally, by using the tensor-based smoothed L0 algorithm, the 3D high-resolution image of the target is obtained, providing the target information for the next measurement. Based on these three steps, a cognitive sparse imaging loop is formed for MIMO radar under WBI situations. The simulation and experiment results demonstrate the effectiveness and advantage of the proposed methods. [ABSTRACT FROM AUTHOR]

In this article, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation techniques. Traditional statistical approximation-based methods will be reviewed followed by more accurate physics-based stochastic transfer function clutter models that facilitate site-specific simulations anywhere on earth. The various factors that go into the computation of these transfer functions will be presented, followed by several examples across multiple RF applications. Finally, we introduce a radar challenge dataset generated using these tools that can enable testing and benchmarking of all cognitive radar algorithms and techniques. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionThis paper proposes a Bayesian surprise learning algorithm that internally motivates the cognitive radar to estimate a target's state (i.e., velocity, distance) from noisy measurements and make decisions to reduce the estimation error gradually. The work exhibits how the sensor learns from experiences, anticipates future responses, and adjusts its waveform parameters to achieve informative measurements based on the Bayesian surprise.MethodsFor a simple vehicle-following scenario where the radar measurements are generated from linear Gaussian state-space models, the article adopts the Kalman filter to carry out state estimation. According to the information within the filter's estimate, the sensor intrinsically assigns a surprise-based reward value to the immediate past action and updates the value-to-go function. Through a series of hypothetical steps, the cognitive radar considers the impact of future transmissions for a prescribed set of waveforms–available from the sensor profile library–to improve the estimation process.Results and discussionNumerous experiments investigate the performance of the proposed design for various surprise-based reward expressions. The robustness of the proposed method is compared to the state-of-the-art for practical and risky driving situations. Results show that the reward functions inspired by estimation credibility measures outperform their competitors when one-step planning is considered. Simulation results also indicate that multiple-step planning does not necessarily lead to lower error, particularly when the environment changes abruptly.

This article deals with the joint design of inter-pulse amplitude, phase and frequency to detect coexisting strong and weak targets in the presence of interference for cognitive radar. First, an amplitude–frequency-phase agile (AFPA) signal model and the corresponding coherent processing method based on phase compensation and non-uniform discrete Fourier transform (NUDFT) are developed. Then, two waveform design criteria, namely the weighted integrated sidelobe level (WISL) and weighted peak sidelobe level (WPSL), are considered to reduce the high sidelobe level in Doppler domain. The waveform is subject to peak-to-average ratio (PAR) constraint to align with current hardware specification, as well as spectrum and signal-to-noise ratio (SNR) loss constraints to enhance interference suppression capabilities. Subsequently, two polynomial-time iterative optimization algorithms are proposed for each of the resulting two non-convex problems. One integrates coordinate descent (CD) and first-order Taylor expansion convex approximation (FTECA), while the other incorporates CD and alternating direction method of multipliers (ADMM), thus updating frequency code and amplitude-phased code alternatively. Finally, numerical simulations demonstrate that the proposed method exhibits significantly better detection performance in the scenario where strong targets are accompanied by weak targets compared to the compressive sensing (CS) approach. • Inter-pulse amplitude, frequency and phase agile waveform. • Inter-pulse agile design via CD-FTECA algorithm for minimizing WISL. • Developing a CD-ADMM framework to track the non-convex problem of minimizing WPSL. [ABSTRACT FROM AUTHOR]

In modern electronic warfare, cognitive radar with knowledge-aided waveforms would show significant flexibility in anti-interference. In this paper, a novel method, named particle swarm-assisted projection optimization (PSAP), is introduced to design phase-coded waveforms with multi-level low range sidelobes, which mainly considers the stability for randomized initialization under the unimodular constraint. Firstly, the mathematical problem corresponding to avoid the range sidelobe masking from multiple non-cooperative targets or interference is formulated by giving different threat levels. Then, based on the alternating direction decomposition idea, the original problem is divided into triple-variable ones where these non-linear approximations can be solved via alternating projections along with FFT. Furthermore, the PSAP method with swarm intelligence, learning factor, and particle-assisted projection could ensure the optimization convergence in a parallel way, which could relax the non-convex constraint and enhance the global exploiting performance. Finally, simulations for several typical scenarios and numerical results are all provided to assess the waveforms generated by PSAP and other prevalent ones. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a set of sequences is developed with a minimized mean sidelobe level (MSL). The problem is formulated for cognitive radars using the $l_{1}$ -norm, where the target cognition determines which sidelobes should be suppressed. The cognitive radar configuration requires fast waveform regeneration in each cognition cycle. In this light, the computational burden of the algorithms developed here is revealed to be situated on the singular value decomposition (SVD) operation. The randomization method is adopted to speed up the proposed algorithms. The obtained fast generation of sequences with the desired autocorrelation is key to its utilization in cognitive radars. We also consider two practically important cases and accommodate the proposed approach to them: unimodular and finite-alphabet sequences. The superiority of the developed algorithms is confirmed both in suppression level and speed through extensive numerical simulations.

Adaptive beamsteering cognitive radar (AB-CRr) systems seek to improve detection and tracking performance by formulating a beam placement strategy adapted to their environment. AB-CRr builds a probabilistic model of the target environment that enables it to more efficiently employ its limited resources to locate and track targets. In this work, we investigate methods for adapting the AB-CRr framework to detect and track large target swarms. This is achieved by integrating the properties of correlated-motion swarms into both the radar tracking model and AB-CRr’s underlying dynamic probability model. As a result, a list of newly CRr-integrated contributions are enumerated: a) improved uncertainty function design, b) incorporates Mahalanobis nearest neighbors multi-target association methodology into AB-CRr, c) introduces a novel Kalman-based consolidated swarm tracking methodology with a common velocity state vector that frames targets as a correlated collection of swarm members, d) introduces an improved uncertainty growth model for updating environment probability map, e) introduces a method for incorporating estimated swarm structure and behavior into the uncertainty update model referred to as “track hinting”, and f) introduces new metrics for swarm search/detection and tracking called swarm centroid track error and swarm tracking dwell ratio. The results demonstrate that AB-CRr is capable of adapting its beamsteering strategy to efficiently perform resource balancing between target search and swarm tracking applications, while taking advantage of group structure and intra-swarm target correlation to resist large swarms overloading available resources.

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A spectrum sharing radar can be guided by a cognitive decision process to determine the optimal radar operating frequency as the spectral environment changes. This decision process utilizes spectrum sensing or spectral prediction to determine the optimal radar transmission for a given situation. The radar transmitter power amplifier performance varies with frequency and bandwidth of the applied waveform, thus adaptive impedance tuners are useful in maximizing the transmitted power and radar range as the transmission frequency range is varied. Since high power handling is required in radar transmissions, and mechanically actuated impedance tuners presently demonstrate the best power handling, the time required to tune is often orders of magnitude greater than the pulse repetition interval. As such, the relatively lengthy impedance tuning operations should be guided to maximize the average output power as the system transitions between different center frequencies, bandwidths, and waveforms over time. This article presents an algorithm that performs impedance tuning with an evanescent-mode cavity tuner based on an average performance gradient computed for multiple transmit pulses. Comparison of test results with traditionally measured amplifier load-pull data shows that the transmitter is effectively optimized for maximum average output power. [ABSTRACT FROM AUTHOR]

The need for cognitive radar systems that automatically adapt their transmit waveform and receive processing to the environment is growing along with the desire for integrating with communications. Indeed, the radio frequency spectrum will continue to become more crowded, and in this context, the new generation of radar systems will require to sense the frequency band and avoid making interference for other systems, like communications. In this paper, we present a spectrum-sharing prototype that demonstrates the application of waveform optimization for a scenario of coexistence between communications and cognitive radar systems. To this end, we define a bi-objective optimization problem for waveform design that provides both spectrum compatibility and small cross-correlation for the waveform set. The optimization problem is non-convex and NP-Hard, where we solve it iteratively using the Coordinate Descent (CD) framework. By sensing the environment in real-time and sequentially optimizing radar transmit waveform, the applicability of the proposed optimization algorithm is illustrated in a custom-built prototype, and its performance is validated and evaluated through SNR measurement for the radar system and throughput calculation for a realistic LTE downlink communication. [ABSTRACT FROM AUTHOR]

This article proposes the Bayesian surprise as the main methodology that drives the cognitive radar to estimate a target's future state (i.e., velocity, distance) from noisy measurements and execute a decision to minimize the estimation error over time. The research aims to demonstrate whether the cognitive radar as an autonomous system can modify its internal model (i.e., waveform parameters) to gain consecutive informative measurements based on the Bayesian surprise. By assuming that the radar measurements are constructed from linear Gaussian state-space models, the paper applies Kalman filtering to perform state estimation for a simple vehicle-following scenario. According to the filter's estimate, the sensor measures the contribution of prospective waveforms—which are available from the sensor profile library—to state estimation and selects the one that maximizes the expectation of Bayesian surprise. Numerous experiments examine the estimation performance of the proposed cognitive radar for single-target tracking in practical highway and urban driving environments. The robustness of the proposed method is compared to the state-of-the-art for various error measures. Results indicate that the Bayesian surprise outperforms its competitors with respect to the mean square relative error when one-step and multiple-step planning is considered. [ABSTRACT FROM AUTHOR]

A multiplatform radar network (MRN) composed of multiple transmitting and receiving facilities is advantageous over monostatic radar in moving target detection (MTD) and tracking due to the diversity gain. Within the framework of the Internet of Battlefield Things (IoBT), the authors propose that the airborne platforms equipped with the cognitive multimodal radar (CMR), the subarray‐based MIMO radar (SMR), the active electronically scanned array (AESA) radar, and the adaptive radar countermeasures (ARC) should team up in target detection and tracking. To obtain better target detection and tracking performance, the probing signals from the CMR, the SMR, and the AESA radar are adaptively optimised based on the scenario under consideration. The ARC consists of two parts: 1) RF signal sensing and recognition with deep neural network (DNN), that is, to detect, identify, and localise multiple radio frequency emitters; 2) intelligent fake return creation and jamming to confuse the enemy radars in real time in the field. This work aims to serve as a good reference for future researchers interested in developing MRN with diverse nodes and the general teaming of radars on the battlefield. [ABSTRACT FROM AUTHOR]

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar’s waveform catalog. [ABSTRACT FROM AUTHOR]

In order for a closed-loop radar system to adapt properly to changing operational conditions, the performance standards and requirements of the radar system must be known. Often, these performance standards are contained in statements made in language, which describe the different goals of the radar system. In this article, we explore the use of language-based cost functions (LBCFs) to encode performance standards made in imprecise language into an objective function, which a fully adaptive radar (FAR) can use to optimize its performance in real time. To enable the LBCFs, we also propose a statement decomposer, which takes in statements as inputs and yields the information required by LBCFs as outputs. A simulation study of a FAR tracking a running human is conducted with four different cost function methodologies: quadratic, weighted global criterion, fuzzy optimization, and LBCFs. Results show that when statements are properly formulated and presented, the statement decomposer is able to successfully extract the relevant information. Results also show that when paired with a previously developed optimization routine, the LBCF system is capable of autonomously creating cost functions that outperform the other cost functions in the simulated FAR target tracking scenario. [ABSTRACT FROM AUTHOR]

This article considers the problem of multitarget detection for massive multiple input multiple output cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multitarget detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_{\mathsf {D}}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamformin optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL-based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance, and rapidly changing scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents an approach for cognitive radar to track the target state despite unknown statistics or sudden changes in noise exposed to uncertain environments. Given the prior knowledge or accurate estimation of the noise, cognitive radar can adaptively adjust the waveform in the transmitter by perceiving the environment. However, the measurement noise usually contains a priori unknown parameters which are difficult to be estimated, or the process noise without empirical model might be improperly configured. Besides, they may change abruptly or time-varying in practice. These issues are prone to occur in cognitive radar, leading to performance decline or failure in tracking ultimately. To alleviate this problem, a robust cognitive radar tracking method based on adaptive unscented Kalman filter (AUKF) is proposed in this article. Specifically, UKF is used to derive the cognitive mathematical model and estimate the states in nonlinear systems. Moreover, a robust adaptive mechanism is designed in the cognitive framework, which is independent of prior information. When the mismatch between the noise in the model and the noise in the environment emerges, the noise covariance can be corrected adaptively. To verify the efficiency of the scheme, maneuvering target tracking experiments are carried out in three uncertain noise scenarios. Simulation results show that the scheme outperforms the existing adaptive UKF and cognitive radar algorithms in terms of intelligence and robustness.

Cognitive radar can sense the battlefield environment and feed this information back to a transmitter by imitating the cognitive learning process of bats to enable self-adaptive detection and processing, which are vital for the future intelligent development of radar. Therein, full utilization of the prior information of the target and environment to design radar waveform for improving the performance of target detection, tracking, and anti-jamming is difficult and has been the focus of cognitive radar development. Therefore, based on different jamming environments, target models, and antenna configurations (e.g., Single Input Single Output (SISO) and Multiple Inputs Multiple Outputs (MIMO)), this study summarizes the key elements and main ideas of waveform design. Furthermore, this study lists the related literature on representativeness from the viewpoint of the use of different jamming environments and target models, aiming at providing reference and basis for cognitive waveform design research in the future.

We investigate three emerging topics essential for the development of cognitive radar (CR) for spectrum sharing: the response time (RT) of the CR, the autonomous regulation of the perception-action cycle (PAC), and the regulation of cognition. The RT measures the latency of all algorithms/hardware and is examined with respect to enabling capabilities of software-defined systems for rapid flexibility and responsiveness. The autonomous regulation of the PAC determines "how fast the CR can interact with the environment" as well as "how fast the CR should interact with the environment." The regulation of the PAC is explored with respect to pulse-to-pulse waveform agility to coexist successfully with dynamic radio frequency emitters in the ambient electromagnetic environment (EME) and the consequence of modifying the waveform within the coherent processing interval. Finally, the regulation of cognition determines how to select a particular CR technique appropriately for a given dynamic environment. This selection requires a high level, or metadecision process to identify the appropriate CR technique as the EME changes over time and we therefore concentrate discussion on the newly emerging topic for radar called metacognitive radar. The exploration of these three topics include a review of past and current research with discussion of possible future research. [ABSTRACT FROM AUTHOR]

This article considers three interrelated adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estimate of us based on the radar's actions and calibrate the radar's sensing accuracy. Second, using revealed preference from microeconomics, we formulate a nonparametric test to identify if the cognitive radar is a constrained utility maximizer with signal processing constraints. We consider two radar functionalities, namely, beam allocation and waveform design, with respect to which the cognitive radar is assumed to maximize its utility and construct a set-valued estimator for the radar's utility function. Finally, we discuss how to engineer interference at the physical layer level to confuse the radar that forces it to change its transmit waveform. The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level, and revealed preferences for identifying utility maximization at the systems level. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The authors present transmit-adaptive jammer nulling waveforms in cognitive radar (CRr) for aircraft response recognition as a form of electronic support in the electronic warfare domain. New forms of knowledge-based noise jammers that function as a form of electronic attack to matched illumination waveforms are also introduced. These knowledge-based noise jamming techniques capitalize on prior knowledge of the target's frequency response to obscure the true target response echo. The results from simulation show that real-time adaptive waveforms suppress the effects of electronic attack noise jammers and the newly introduced knowledge-based noise jammers. High bandwidth radar cross section (RCS) responses via electromagnetic (EM) simulations of true-to-physical-size aircraft are used for simulations to produce CRr classification performance against a comprehensive list of noise jammers.

This paper addresses the waveform design problem to estimate the target impulse response (TIR) of the temporally correlated extended target in a cognitive radar, subject to a detection constraint and a peak-to-average power ratio constraint. Owing to these types of constraints and the convolution operation of the waveform in the time domain, the formulated optimization problem for minimizing the mean square error of the estimated TIR based on Kalman filtering is a complex non-convex problem. To this end, an auxiliary variable is first introduced to modify the original problem, and the non-convex problem is converted to a convex problem with respect to the matrix variable. Then, a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Moreover, the convex problem is further decomposed into three simple sub-problems which can be solved efficiently. Finally, the optimal waveform can be obtained efficiently through cognitive iteration combined with the nearest neighbor method. The simulation results illustrate that the proposed method is superior to the existing method in terms of the estimation performance and computational complexity when designing the constrained waveform.

To improve the detection performance of the radar transmit waveform while enabling the transmitter to perform at its maximal efficiency, a joint design method is proposed for the transmit and receive filter in the presence of signal-dependent clutter with a Peak-to-Average-power Ratio (PAR) constraint of the transmit waveform. First, an optimized model of the radar’s output Signal-to-Interference-plus-Noise Ratio (SINR) for range-extended target detection is established. Second, the analytic expression of the receiver is obtained by converting the optimization problem into the Rayleigh quotient model. The optimal matrix solution is then obtained by transforming the non-convex problem into a convex problem via the semi-definite matrix of the waveform. Finally, the optimal vector solution of the waveform is extracted from the optimal matrix solution by combining the rank-one approximation method combined with the nearest neighbor method. An optimal waveform with a maximal output SINR for a given PAR range is obtained using the proposed method. The SINR value of the waveform when PAR = 2 is the same as the SINR value of the optimized waveform under the energy constraint and is about 0.5 dB higher than the waveform when PAR = 1. Simulation results demonstrate the effectiveness of the proposed method.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing demand for radio frequency (RF) spectrum access poses new challenges for next-generation radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system, which predicts and avoids RF interference (RFI) in real time. The system applies a cognitive perception-action cycle that senses RFI, learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real time and share the spectrum with emitters, such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RFI on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases. [ABSTRACT FROM AUTHOR]