Cognitive Dynamic Systems: A Technical Review of Cognitive Radar

Digital Signal Processing, 2011

2020

Circuits, Systems, and Signal Processing

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

2010 International Waveform Diversity and Design Conference, 2010

In this paper, we propose a cognitive radar system for target tracking in the presence of multipath reflections. We exploit the inherent spatial diversity offered by the multipath environment by constructing a new measurement vector, which we refer to as a virtual measurement vector. We employ broadband Orthogonal Frequency Division Multiplexing (OFDM) signalling at the transmitter and implement adaptive waveform design by minimizing the posterior Cramér Rao bound (PCRB) on the target state estimates to find the optimal weights to be transmitted on each subcarrier bin of the OFDM signal. We demonstrate with numerical simulations that the mean square error in the case of a cognitive radar is significantly lower than the mean square error in the case of a standard radar.

2010

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

2021

We study how a cognitive radar can mask (hide) its cognitive ability from an adversarial jamming device. Specifically, if the radar optimally adapts its waveform based on adversarial target maneuvers (probes), how should the radar choose its waveform parameters (response) so that its utility function cannot be recovered by the adversary. This paper abstracts the radar’s cognition masking problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and embeds the algebraic Riccati equation into an economics-based utility maximization setup. Given an observed sequence of radar responses, the adversary tests for utility maximization behavior of the radar and estimates its utility function that rationalizes the radar’s responses. In turn, the radar deliberately chooses sub-optimal responses so that its utility function almost fails the utility maximization test, and hence, its cognitive ability is masked from the adversary. We illustrate the perf...

2006 9th International Conference on Information Fusion, 2006

This work presents a deterministic approach to the problem of modelling the human behaviour in a Command and Control radar system and it considers thefusion ofinformation between the operator and the system. The implementation and the results of a case study are presented where a human operator performs a tracking operation of multiple targets in a sea region. The mission performed by the operator is the surveillance of a coast area and the selection of a system action against possible threat targets, in order to check their identity. An analytical model of human memory has been investigated where the human decision maker is represented as a subsystem involved with two operational blocks, corresponding to the Situation Assessment process and the Response Selection process that he performs. The operator performance is evaluated by mean of his error probability in these two processes.

We have proposed a machine-learning based classification framework for cognitive radar for target state classification. Based on the estimated frequency of the received signal at the radar receiver, we have classified three rotational movements (yaw, pitch, and roll) of a maneuvering aircraft motion. Direct classification of the data sets for the different rotational movement was found non-separable. It is difficult to find a classier to construct linear boundary for the classification of this data sets. We intended to design an algorithm for this problem. The proposed algorithm is applied on separable, half separable and non-separable data sets. The success rate of the classifier was verified in terms of cross-validation, mean square error, type I and type II error. The algorithm has shown a success rate of approximately 87.28% and 99.15% for not-separable and separable data sets respectively. It also shows that the increment in the accuracy by 6.86% as compared with the conventional approach .

IEEE Signal Processing Magazine, 2000

In this paper we consider the problem of Bayesian sequential parameter estimation of extended targets for cognitive radar with multi-antenna arrays using adaptive waveforms. The target is modeled as a complex Gaussian random process. Using iterative waveform transmission, the cognitive radar estimates the target's characteristic parameters and updates its probabilistic model based on new measurements. The adaptive waveform is designed by minimizing the conditional entropy from the posterior density of the model parameters. We analyze the performance of the developed Bayesian sequential estimation algorithm and derive expressions for the signal-to-noise ratio gain, the asymptotical posterior Cramer Rao bound, and the mutual information gain. The analysis and numerical simulations demonstrate that the adaptive sequential Bayesian estimator yields accelerated convergence of the estimate towards its true value and a smaller estimation error compared with the conventional Bayesian estimator that uses fixed waveform transmission under Gaussian or non-Gaussian noise.