Cognitive Radar Research Articles

Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

Memory‐augmented cognitive radar for obstacle avoidance using nearest steering vector search

Memory‐augmented cognitive radar for obstacle avoidance using nearest steering vector search

Robust Time Resource Management in Cognitive Radar Using Adaptive Waveform Design

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.

Arbitrary manipulations of dual harmonics and their wave behaviors based on space-time-coding digital metasurface

Metasurfaces have developed rapidly in the past decade because their customizable properties are able to regulate wave-matter interaction over a subwavelength scale. Recently, the space-time modulated metasurfaces have attracted significant attention for the additional degree of freedom they offer in the manipulation of the electromagnetic waves in both space and time domains. However, the existing techniques have limited wave control capabilities, which results in just a few feasible schemes, like regulation of only one specific harmonic. Here, we propose to create independent manipulations of arbitrary dual harmonics and their wave behaviors using a space-time-coding digital metasurface. By employing different space-time-coding sequences in the reflection phase of the metasurface, independent phase-pattern configurations of two desired harmonics can be achieved simultaneously, which further leads to independent beam shaping at the two harmonic frequencies. We have developed an analytical theory to offer physical insights into the arbitrary dual-harmonic manipulations of spectra and spatial beams, which is verified by experiments with good agreements. The presented space-time-coding strategy provides a new way to design multifunctional programable systems, which will find potential applications such as cognitive radar and multi-user wireless communications.

Designing adaptive time resource management cost function for cognitive radar

Cognitive radars have the capability of adapting the transmitted waveform with the dynamic state of target and environment, to enhance the radar performance. In this study, a cost function is proposed for designing the waveform parameters in cognitive radars, along managing the radar time resources. The proposed cost function has the capability of adapting to any radar measurement vector. The purpose of the proposed cost function is to design the waveform parameters adaptively, so that a compromise between the different radar measurement errors is made. This compromise between the different radar measurement errors depends on the prior and posterior target state estimations. Both simulated target data and real target data are used for evaluating the proposed cost function. Also the performance of the proposed cost function is compared to the previously presented cost function. Simulation results indicate that by exploiting the proposed cost function, a great saving on time resources is achieved, while tracking error is kept stable. This time resource-saving lies within the range of 10% up to 50% depending on the target manoeuvre scenario. Also, in a non-time resource management case, an improvement in tracking error is achieved.

Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar

Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.

Fully adaptive waveform parameter design for cognitive tracking radars

Cognitive radars have the capability of using the prior environmental information to improve the radar performance. In this study, a cost function for fully adaptive waveform parameters design for cognitive tracking radars is proposed. These parameters include – but not limited to – pulse length, pulse repetition frequency, number of transmitted pulses, and target revisit rate. Thus, the proposed cost function is capable of reducing the tracking error, using adaptive waveform parameters, along the time resource management, with adaptive coherent processing interval and target revisit time. The purpose of the proposed cost function is balancing the different radar measurement errors (which depend on radar transmitted waveform in contrary directions), with respect to the target manoeuvre scenario, to reach the radar tolerable tracking error. Simulation results show that using the proposed waveform parameter design cost function, tracking error is reduced, and an optimised radar time resource usage is obtained.

ОПТИМІЗАЦІЯ СТРУКТУРИ БАГАТОКАНАЛЬНОГО БОРТОВОГО РАДАРУ З СИНТЕЗУВАННЯМ АПЕРТУРИ АНТЕНИ ТА АЛГОРИТМОМ СЕЛЕКЦІЇ ЛІНІЙ ЕЛЕКТРОПЕРЕДАЧ НА ТЛІ ЗЕМНОЇ ПОВЕРХНІ

The task of synthesizing optimal signal processing in synthetic aperture radars equipped with a selection mode of power lines was solved by the maximum likelihood method. The distinguishing features of power lines from the earth's surface selected polarization and Doppler differences. The useful signal at the output of the coherent receiver specified in an analytical form with a complex envelope and high-frequency harmonic oscillations. The background radiation of the underlying surface is presented in a stochastic form and is completely determined by the matrix of correlation functions. The internal noise of a multichannel receiver is described by a delta-correlated random Gaussian process with the same power spectral density in different observation channels. Since the background radiation of the earth's surface is correlated in time and at different polarizations functional likelihood recorded with the inverse matrix inverse correlation functions. The obtained optimal algorithm consists of suppression of passive interference in decor-leveling filters, optimal coherent accumulation of trajectory signals for synthesizing the on-board antenna aperture, and their optimal filtering in filters matched with the reference signal. Simultaneous use of Doppler and polarization differences between wanted signals and passive interference allows you to effectively allocate power lines against the background of reflections from the ground in multichannel synthetic aperture radars. The proposed sequence of operations on the received oscillations can be a functional addition to the operating modes of aerospace-based cognitive radars, adapting by the covariance scattering matrix of the underlying surface known in advance or measured during remote sensing. The block diagram of a multichannel onboard polarization radar with a synthesized antenna aperture is synthesized based on the obtained results. The development of such a radar is relevant for a collision warning system for helicopter pilots, especially in bad weather conditions at any time of the year, day and night.

СИНТЕЗ ОПТИМАЛЬНОГО АЛГОРИТМУ ПОЛЯРИЗАЦІЙНОЇ СЕЛЕКЦІЇ КОРИСНИХ СИГНАЛІВ НА ФОНІ ПАСИВНИХ ЗАВАД В РАДАРІ З СИНТЕЗУВАННЯМ АПЕРТУРИ

Using the method of maximum likelihood, the optimal algorithm of polarization selection of objects on the background of the underlying surface, hydrometeors, urban buildings from aerospace carriers of radio electronic equipment has been synthesized. To solve the problem polarimetric properties of the scattered electromagnetic radiation of different natural environments and anthropogenic objects were analyzed. Has been determined the functional-determined mathematical model of the useful signal, the structure and correlation characteristics of scattered background radiation and white noise inside the multichannel receiver. It is assumed that the wanted signals are measured with phase accuracy in a coherent receiver. When solving the optimization problem, the method of the maximum likelihood functional and the likelihood functional for correlated processes were used, contains the inverse matrix of inverse correlation functions of the observation equation. The obtained signal processing algorithm, in addition to the classical operations of accumulating the trajectory signal with the preservation of the phase structure and its matched filtering, provides for polarization and frequency selection in the line of decorrelation filters. In the case when the internal noise is very small in comparison with external interference and the correlation between the channels of vertical and horizontal polarization approaches unity, almost complete compensation of passive interference occurs. Further spectral rejection of the reflected signals is practically unnecessary and it is possible to restrict ourselves only to matched filtering of the useful signal. In the absence of background radiation correlation, the channels for receiving oscillations of different polarizations become independent and the main operation for the selection of useful signals is spectral rejection of passive interference and matched filtering of the useful signal. The developed algorithm can be implement in cognitive radars due to its adaptability to the parameters of background radiation in accordance with the change in the scattering covariance matrix of the underlying surface. Based on the results obtained, a block diagram of a multichannel polarization radar with synthesized antenna aperture has been developed.

Robust Waveform Design Based on Bisection and Maximum Marginal Allocation Methods with the Concept of Information Entropy

Cognitive radar can overcome the shortcomings of traditional radars that are difficult to adapt to complex environments and adaptively adjust the transmitted waveform through closed-loop feedback. The optimization design of the transmitted waveform is a very important issue in the research of cognitive radar. Most of the previous studies on waveform design assume that the prior information of the target spectrum is completely known, but actually the target in the real scene is uncertain. In order to simulate this situation, this paper uses a robust waveform design scheme based on signal-to-interference-plus-noise ratio (SINR) and mutual information (MI). After setting up the signal model, the SINR and MI between target and echo are derived based on the information theory, and robust models for MI and SINR are established. Next, the MI and SINR are maximized by using the maximum marginal allocation (MMA) algorithm and the water-filling method which is improved by bisection algorithm. Simulation results show that, under the most unfavorable conditions, the robust transmitted waveform has better performance than other waveforms in the improvement degree of SINR and MI. By comparing the robust transmitted waveform based on SINR criterion and MI criterion, the influence on the variation trend of SINR and MI is explored, and the range of critical value of Ty is found. The longer the echo observation time is, the better the performance of the SINR-based transmitted waveform over the MI-based transmitted waveform is. For the mutual information between the target and the echo, the performance of the MMA algorithm is better than the improved water-filling algorithm.

Radar code design to optimise detection under whitened similarity constraint

An active sensing system, such as radar, extracts the useful features of the targets of interest, by transmitting a certain waveform to a particular area and analysing the returned signal. There are two factors that are critical to the system performance, namely the receive filter and the transmitted waveform. Transmitted signal is among the most interesting factor affecting the radar system performance in complex environments. The waveform design problem is an important stage in the performance of cognitive radars. Here, the matched illumination approach is studied which specially addresses the issue of adaptive waveform design to maximise the signal-to-disturbance ratio that finally leads to an increased probability of detection. Different constraints on the designed waveform have been introduced in the literature. However, the focus is on the similarity constraint that controls the ambiguity function and modulus variations of the devised code. In more details, it is shown that this kind of constraint should be written based on the estimated background covariance matrix leading to a proposed whitened similarity constraint. It is shown by simulation that the proposed whitened constraint outperforms the well-known similarity one in resultant ambiguity function properties and probability of detection, assuming the same environmental conditions.

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.

Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics.

A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.

Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison with a nonadaptive case, an adaptive matched filter method, and the clairvoyant case. We also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements.

The Development From Adaptive to Cognitive Radar Resource Management

This article has presented a walk along the cognitive spectrum in the context of radar resource management. It has shown how radar resource management techniques, developed over the last decades, have focused on increasing the complexity of perception-action cycles in order to increase the operational performance of the radar. These techniques can be built upon in the context of cognitive radar, for example, through mission-oriented QoS management or stochastic control. Although basic perception-action cycles exist in operational radars, there are a number of challenges and drawbacks that must be considered before a high level of cognition can be deployed. Care should be taken in the drive toward cognitive radars, as the ultimate goal is not to have a cognitive radar for the sake of being cognitive, but to increase the radar functionality and operational performance

Cognitive Radar Special Issue—Part 2

In the first part of the Special Issue on Cognitive Radar, articles were published covering high-level concepts and cognitive radar architectures as well as experimental results. The second part focuses on applications and case studies of cognitive radar principles in practice.

Cognitive Radar Target Detection and Tracking With Multifunctional Reconfigurable Antennas

In contrast to the feed-forward sensing chain employed by classical radar systems, cognitive radars use the perceived information about the environment to reconfigure their transmissions. While most of the efforts in the literature focus on software-based adaptations, this article proposes adaptive control of a radar hardware, multifunctional reconfigurable antennas (MRAs), for target detection and tracking within the cognitive radar framework. A parasitic layer based MRA has the capability of dynamically changing its EM characteristics (mode of operation), e.g., antenna beam pattern, polarization, center frequency, or a combination of thereof. This work focuses on beam pattern recognition using a general Bayesian cognitive radar framework for target detection and tracking. A cognitive radar controller is designed to select the modes of an actual MRA by minimizing the Cramer lower bound for direction of arrival estimation. Simulation results show that the cognitive reconfiguration of the MRA offers superior tracking performance compared to classical antenna systems with no adaptivity.

A Rule-Based Cognitive Radar Design for Target Detection and Imaging

This article handles the topic of cognitive radar (CR) architecture design in the framework of multifunction radar operating in a resource-constrained and spectrum-constrained environment. Despite the advances in this field of research and its relative technologies, the way humans and echolocation mammals are able to interact with the external environment goes beyond the capability of any available man-made system. A CR can be thought as a system in which the transmitter, receiver, and software parameters can be changed over time in response to the observed scene with the aim to optimize radar performances given limited resources and environment constraints. The radar, therefore, has to reason about what is being observed and has to take decisions about how to use its limited resources to improve its performance. Rules may represent the way the system reasons, while performance encodes the information contained into the received echoes, and can be used to control next actions and system memory. A rule-based cognitive architecture is proposed in this article as a way to design a CR that has to manage its resources dynamically while handling several tasks, such as target detection, imaging, and recognition in a complex and changing scenario.

SINR-Based Time Domain Synthesis Algorithm for Radar

For optimal radar waveform design and multi-target recognition in cognitive radar, a time-domain synthetic algorithm based on the water-filling algorithm is proposed. We consider the energy spectrum distribution of the synthesized signal based on the signal to interference plus noise ratio (SINR) in the presence of clutter. Simulation results show that after adding phase information, the time-domain synthesis algorithm based on SINR can obtain more target information than the general water-filling algorithm.

Assessing Agile Spectrum Management for Cognitive Radar on Measured Data

Radar operation in a spectrally dense environment is nowadays a very challenging problem due to the increasing demand of spectral resources for defence/surveillance applications, remote sensing capabilities, and civilian wireless services. This article explores the technical feasibility of radar waveforms capable of ensuring spectral coexistence with overlaid emitters via a modern digital arbitrary waveform generator. To this end, a specific hardware-in-the-loop test bed is designed to mimic the perception-action cycle necessary for the agile management of the radio spectrum. Then, a spectrum analyzer (SA) and a digital oscilloscope (DO) are used to demonstrate the compliance of the synthesized waveforms with the theoretical counterparts. In particular, the SA is used to establish if the synthesized waveforms fulfil the spectral requirements forced at the design stage. The DO, instead, is used to assess the adherence of synthesized signals ambiguity function (AF) with the theoretical one. For the considered case studies, results highlight that the synthesized waveforms enable spectral compatibility between radar and communication systems and exhibit desirable AF properties.