Illumination waveform design for non-Gaussian multi-hypothesis target classification in cognitive radar

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.

This work investigates the estimation of target scattering coefficients (TSC) in cognitive radar systems with temporally correlated targets. An estimation method based on Kalman filtering (KF) is p...

In cognitive radar system, how the transmitted waveform adapts in response to information regarding the radar environment is an important problem. In this paper, the waveform design for cognitive radar is viewed as an optimization problem. Then a new waveform design method for cognitive radar is proposed, which uses IPM (interior-point method) to carry out the optimization task. The simulation results demonstrate the validity of our algorithm.

The focus of this paper is on the deep reinforcement learning (DRL) based cognitive radar waveform optimization problem, by utilizing the prior knowledge of the environment and adaptively adjusting the system parameters. We first design deep reinforcement learning-based waveform optimization framework, where agent is radar, state is entropy of environment state and action is transmit waveform. Based on the fact that the continuous changing in cognitive radar tracking system, resulting in infinite states of environment and targets, we design a new deep Q-network to map the state-action pair to its Q-values. After training, radar in DRL system will obtain a policy with which it can select the optimal parameter of waveform to transmit according to the entropy of environment state, which is the comentropy of the posterior probability of state. Simulation results illustrate that by using the proposed approach, the precision of target tracking can be improved clearly.

Adaptivity in multi-function radar systems is rapidly increasing, especially when moving towards fully adaptive, cognitive radar systems. However, the large number of available system configurations makes the rigorous verification and certification process during the testing phase, deployment, and after hardware and software upgrades, challenging, if not infeasible. To alleviate the verification process, run-time verification can be applied to oversee the correct function of a system during its operation as done in applications where on-the-fly reconfiguration/adaptation is pervasive, e.g., spacecrafts and self-driving cars. Though possible, the application of run-time verification into a radar system is not straightforward, e.g., when verifying (adaptive) radar resource managers or performance measures, such as track initiation time in dynamic environments. The goal of this paper is to introduce a framework to identify, characterize, and map the various aspects necessary for implementing run-time verification for (components of) multi-function radar systems. The proposed framework can be used by radar practitioners and researchers for applying run-time-verification to adaptive, re-configurable radar systems. In addition, we discuss how run-time verification can be leveraged to gather new insights from operational data to improve functionalities in upcoming update cycles and present an example of a verifier designed using the introduced framework.

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.

Dynamic spectrum allocation will require cognitive radar transmitters to change operating frequency and bandwidth in real time. This will require high-power reconfigurable circuitry to improve radar performance by simultaneously increasing 1) the output power of the transmit waveform and 2) the power-added efficiency of the power amplifier. This circuitry is also used to mitigate cochannel interference by maintaining sufficiently linear performance so that the output waveform conforms to a given spectral mask. In this approach, a 90-W evanescent-mode cavity tuner is reconfigured using a specially designed gradient search to find the best combination of resonant cavity position numbers. This approach will be much more flexible for field use than typical Smith-chart-based load-pull searches, which require a characterization that is susceptible to drift. Experimental results are presented showing that the efficiency, output power, spectral performance, and estimated maximum radar detection range are improved significantly by retuning the matching network at each operating frequency. Additionally, this article discusses innovations to reduce or eliminate time bottlenecks in a cognitive radar system for impedance tuning, reducing the time needed for complete impedance tuning searches from minutes to seconds. This significant timing reduction makes tunable power amplifiers a feasible option for future use in spectrum sharing by cognitive radar systems.

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.

The problem of adaptive orthogonal frequency division multiplexing (OFDM) waveform design in the case of stochastic extended targets in the presence of strong and nonstationary interferences is an essential part of a cognitive radar system. In this article, a new approach is developed based on maximizing the mutual information between the random target impulse response and the received echoes with respect to the transmitted waveform. We use the advantage of OFDM signals and present an adaptive structure to match the complex weights of OFDM signal with the radar scene for making complete use of the transmission power. The interference reflections are utilized rather than eliminating them to design the weights of the transmitted OFDM signal. The optimization algorithm for designing transmitted signals is based on adaptive learning whose essential information is obtained by a feedback between receiver and transmitter. This feedback includes essential target and interference characteristics which extracted from the reflected echoes. Based on changes in interference features, the weights of the transmitted OFDM signal on each subcarrier are designed and transmitted in the next coherent processing interval. Experimental results reveal that the proposed approach achieves a higher rate of performance compared to nonadaptive waveform design approaches.

Cognitive radar systems offer improved performance through mimicking the perception-action cycle in human cognition. While most of the efforts in the area to-date focus on agile waveform design and sensor management, in this paper, we propose adaptive control of novel multifunctional reconfigurable antennas (MRAs) as a mechanism for action within the cognitive radar framework. Reconfigurable parasitic layer based MRAs have the capability of dynamically and simultaneously changing its electromagnetic characteristics (mode of operation), e.g. antenna beam pattern, polarization, center frequency, or a combination of thereof. In this work, a designed and fabricated reconfigurable parasitic layer based MRA is presented. A computationally efficient mode selection scheme is proposed to adaptively select the modes of the MRA for a defined cost such as improved SNR or lower Cramer Rao Bound. A cognitive target tracking framework is proposed through MRA mode selection for cognitive radars. Initial results show that MRAs with mode selection provides enhanced direction-of-arrival (DoA) estimation and cognitive target tracking performance.

One of the major objectives of cognitive radar is to form a dynamic closed feedback loop to adapt the spectrum of transmit waveforms to avoid certain interference. In this paper, we build an automatic closed-loop cognitive radar to support experimental study of the radar system in real-world situations. Convex optimization is applied to jointly design sounding waveforms and the matched filters with spectral power suppressed in arbitrary bands and with low correlation sidelobes as well. Target localization is demonstrated under strong interferences. Experimental results are provided to evaluate the performance of the cognitive radar system.

Due to the rapid development of cognitive radar and the complicated electromagnetic environment, traditional anti-jamming decision-making methods are no longer suitable to modern electronic counter-countermeasures. Reinforcement learning brings a novel solution to this problem. In this paper, a method based on deep reinforcement learning is applied in the anti-jamming decision-making system of cognitive radar. We construct the environment model for cognitive radar and propose a modified deep deterministic policy gradient algorithm for decision-making. The experimental results demonstrate that the proposed method is effective in the application of anti-jamming decision-making system of cognitive radar. Furthermore, the performance analysis shows that the proposed algorithm converges faster than other classical algorithms and more suitable to high-dimensional state and action space problems.

The typical approach to radar target classification is to image the target with waveforms that provide high resolution and low sidelobes, and then to compare the target images to a template library. In this chapter, we reconsider whether imaging-based metrics for waveform design are best for target classification, and develop alternative design strategies that result in waveforms with improved classification ability, but not necessarily a good ambiguity function by traditional notions. After presenting waveform design strategies based on optimizing signal-to-noise ratio or mutual information from a wide-sense stationary (WSS) ensemble of target impulse responses, we apply the design methods to the problem of radar target classification through a two-step process. The first step is to modify the design methodology that was based on WSS targets to account for the finite duration of practical target responses. The second step is to use the target class probabilities and impulse response library to calculate a weighted power spectral variance over target classes, which is then substituted into the design equations. The use of target class probabilities enables the waveform to be adapted in response to previous transmissions. Waveform behaviour and performance are studied over several different clutter and noise scenarios. The target impulse response library for these studies is based on finite-difference time-domain (FDTD) simulation of a publically available CAD model of an F-16 aircraft.

JPT Forum articles are limited to 1,500 words including 250 words for each table and figure, or a maximum of two pages in JPT. A Forum article may present preliminary results or conclusions of an investigation that the present preliminary results or conclusions of an investigation that the author wishes to publish before completing a full study; it may impart general technical information that does not warrant publication as a full-length paper. All Forum articles are subject to approval by a editorial committee. Letters to the editor are published under Dialogue, and may cover technical or nontechnical topics. SPE-AIME reserves the right to edit letters for style and content. A wave-follower buoy has many advantages over nearly all other types of wave-measuring devices, particularly on the open sea or far offshore. The buoy requires only the earth's gravitational field as a frame of reference instead of a pier, a piling, or perhaps a large spar buoy. Any of the ordinary-capacity or resistive-wave gauges must be attached to a solid place. Any type of wave-follower (accelerometer) buoy will exhibit its own resonant dynamic behavior in the presence of a wave field. The engineer in need of wave information generally needs to know its power spectral density to be able to apply the data, whether the problem is wave forces on a structure or something else. It is not sufficient to simply double-integrate the vertical acceleration of the wave buoy and claim to have the desired wave-height information. The objective of this paper is to compare an actual power spectral density in a wave field with that of the power spectral density in a wave field with that of the accelerometer-output power spectral density produced in the same wave field. We will show an empirical spectral transfer function that can be applied to the observed accelerometer spectrum to yield the desired power spectral density of the driving waves. For this purpose, an ideal linear model for the buoy was proposed to keep the system design simple. Even with this incorrect model, the comparison of the precise spectrum with the compensated spectrum out of the buoy is quite good. The buoy used for this study was a Model WF-100 buoy manufactured by Coastal Data Service, Inc. Let x(t) be the actual vertical displacement of the sea surface as a function of time. If y(t) is the output from the buoy accelerometer, then x(t) and Y\y(t) are related as in Eq. 1. x(t) * h(t) = y(t),...................................(1) where h(t) is the time domain representative of desired transfer function and * denotes convolution. In the frequency domain, we have Eq. 2. X (f) x H(f) = Y(f)...................................(2) Using the Fourier transform theory, one obtains Eq. 3. Gy(f) H(f) 2 = ------------, Gx(f) where Gx(f) = X(f) 2 ..................................(3) In this equation, means absolute value, means expected value, Gx(f) is the power spectral density of waves, and Gy(f) is the power spectral density of the accelerometer output. In this experiment, the two desired power spectral densities were generated by a minicomputer, power spectral densities were generated by a minicomputer, and the ratio of the two in Eq. 3 leads to the compensation function, H(f). The linear model is shown in Eq. 4. 1 1/2 sin (1.26f) H(f) = 32.2 [w2] -------- x ------------- 1 + 2f2 1.26f I II III ...................................(4) Factor I in Eq. 4 includes the accelerometer scale constant and w 2 corresponds to straight double integration of sinusoidal components. P. 652

A new method for the generation of a correlated pseudo-random gaussian process with a desired power spectral density or auto-correlation function is presented. Samples of the pseudo-random process are generated at the output of the cascade connection of a pseudo-random number (PN) generator and correlation-shaping finite impulse response filter. The proposed method generates a pseudo-random gaussian process, having arbitrary correlation properties, with a high degree of accuracy. Computer simulation examples are given to demonstrate the performance of the method.