Waveform Design For Cognitive Radar Research Articles

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

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

SINR- and MI-Based Double-Robust Waveform Design.

Owing to cognitive radar breaking the open-loop receiving-transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.

Cognitive Radar Waveform Design and Prototype for Coexistence With Communications

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.

Time-dependent diffusion model for sea clutter dynamics

ABSTRACT Sea clutter models play a critical role in cognitive radar waveform design and target detection. In this paper, a new sea clutter modelling using Stochastic Differential Equations (SDEs) is proposed to improve clutter amplitude dynamic prediction. The Extended Vasicek model (EV), a nonstationary time-varying diffusion model, is leveraged as the framework. Model parameters are estimated using the observations in a short time interval in temporal dimension before the prediction time, providing an accurate and real-time prediction which is critical in cognitive radars deployed in marine environments. The method is evaluated in terms of mean-squared Error (MSE), mean absolute error (MAE), mean relative error (MRE), relative standard error (RSE), the complementary cumulative distribution function (CCDF) and the Kolmogorov-Smirnov statistical distance . The performance of the method is compared through experimental radar data with the state-of-the-art models such as generalized autoRegressive conditional heteroskedasticity (GARCH) model, Weibull, log-normal, K and log-logistic distributions. It is demonstrated that the proposed model outperforms other state-of-the-art methods in terms of all the above-mentioned metrics.

Adaptive OFDM Waveform Design for Cognitive Radar in Signal-Dependent Clutter

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.

Waveform design for cognitive radar in presence of jammer using Stackelberg game

With the development of modern electronic warfare, the jammer is becoming smarter than before and can be able to take actions according to the intercepted radar waveform. It has considerable significance for the radar to design a proper waveform to improve its performance when facing the smart jammer. Here, a novel cognitive radar waveform design method is proposed to improve the radar's performance. The relationship between the radar and the jammer is modelled as a two-person zero-sum Stackelberg game, where the cognitive radar is the leader and the jammer is the follower. Signal-to-interference and noise ratio (SINR) criterion is utilised as the utility function, where the radar tries to maximise the SINR while the jammer aims to minimise it. Lagrange multiplier method and particle swarm optimisation (PSO) algorithm is applied to solve the problem and the Stackelberg equilibrium (SE) is obtained. Numerical results show the effectiveness of the proposed method.

Waveform Design for Improved Detection of Extended Targets in Sea Clutter.

Adaptive waveform design for cognitive radar in the case of extended target detection under compound-Gaussian (CG) sea clutter is addressed. Based on the CG characteristics of sea clutter, the texture component is employed to characterize the clutter ensemble during each closed-loop feedback and its estimation can be used for the next transmitted waveform design. The resulting waveform design problem is formulated according to the following optimization criterion: maximization of the output signal-to-interference-plus-noise ratio (SINR) for sea clutter suppression, and imposing a further constraint on sidelobes level of the waveform autocorrelation outputs for decreasing the false alarm rate. Numerical results demonstrate the effectiveness of this approach.

Cognitive radar waveform design with the signal-to-clutter-plus-noise ratio and filtered integrated sidelobe level considerations

We deal with the unimodular waveform design for cognitive radar in signal-dependent interference (clutter). In addition to the signal-to-clutter-plus-noise ratio (SCNR), we also consider the filtered integrated sidelobe level (ISL) and optimize these two metrics simultaneously. It is assumed that cognitive radar can obtain environment information based on database and prior return. We relate the SCNR and ISL to the signal power spectrum density (PSD) and compare the optimal PSDs for optimizing SCNR and ISL solely. It is found that these two metrics are coupled in a conflicting way. So, the weighted sum of these two metrics is exploited as objective function to optimize the detection performance and pulse compression simultaneously. In order to solve the resulting problem efficiently, we utilize the gradient method and devise a way to employ fast Fourier transform in calculating the gradient of the objective function to accelerate the gradient method. Finally, numerical simulations are performed to demonstrate the superiority of the proposed method over state-of-the-art method in terms of the output SCNR, filtered ISL, and computational efficiency.

Adaptive Waveform Design for Cognitive Radar in Multiple Targets Situation.

In this paper, the problem of cognitive radar (CR) waveform optimization design for target detection and estimation in multiple extended targets situations is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended targets with unknown target impulse response (TIR). To address this problem, an improved algorithm is employed for target detection by maximizing the detection probability of the received echo on the promise of ensuring the TIR estimation precision. In this algorithm, an additional weight vector is introduced to achieve a trade-off among different targets. Both the estimate of TIR and transmit waveform can be updated at each step based on the previous step. Under the same constraint on waveform energy and bandwidth, the information theoretical approach is also considered. In addition, the relationship between the waveforms that are designed based on the two criteria is discussed. Unlike most existing works that only consider single target with temporally correlated characteristics, waveform design for multiple extended targets is considered in this method. Simulation results demonstrate that compared with linear frequency modulated (LFM) signal, waveforms designed based on maximum detection probability and maximum mutual information (MI) criteria can make radar echoes contain more multiple-target information and improve radar performance as a result.

Waveform Design for Cognitive Radar with Deterministic Extended Targets in the Presence of Clutter

Adjusting radar transmitted waveform to its environment is one of the most important roles in cognitive radar; having the capability of updating transmitted waveforms in different applications is a key point. It has been shown in many studies that if the waveform is designed according to the target and clutter characteristics, the detection performance will improve significantly. The uncertainty of the target radar signatures decreases via maximizing MI and the probability of extended target detection is increases via maximizing SNR. In this paper, a waveform design approach based on maximizing both SNR and MI and with regard to target and clutter shape is presented. The detection performance for proposed waveform is compared with previous proposed waveforms. The present paper compares different scenarios of target and clutter and using the probability of detection as a cost function to investigate the advantages and disadvantages of each waveform in different scenarios which are mainly discussed in this text. The desired waveform for cognitive radar is selected based on simultaneously making compromises between SNR and MI, which plays an important role in cognitive radar systems and based on the assumption addressed in the text, the best waveform transmitted into the environment.