Optimal sequential waveform design for cognitive radar

In the field of target detection and tracking, the resolvability of multiple closely located targets is a significant criterion for measuring the sensing ability of radar and other sensing systems. This paper considers the problem of adaptive radar waveform design to maximize the practical resolvability of closely located targets. Conventional ambiguity function (AF)-based radar resolution considers only the waveform, regardless of the influence of noise. However, noise grreatly influences the achievable resolution as well as radar detection and tracking performance. As a result of this deficiency in conventional radar resolution, Kullback-Leibler Divergence (KLD) is introduced in this paper in order to quantify the “distance” between two probability density functions (PDFs) of radar measurements, and thus to represent practical radar resolution, which includes not only the effect of the waveform but also the signal-to-noise ratio (SNR) and measurement model. Consequently, a new adaptive radar waveform design criterion is proposed, which aims to maximize the practical radar resolution. Simulation results show the effectiveness of the proposed adaptive waveform design method.

In this paper, the problem of adaptive waveform design for cognitive radar (CR) in signal-dependent interference, as well as additive channel noise is investigated. With constraints on waveform energy and bandwidth, a flexible waveform optimization method taking both detection and range resolution into account is proposed. Unlike existing optimal waveforms designed by a single design criterion, waveform designed by the proposed method can be updated according to the environment information fed back by receiver and radar performance demands simultaneously at each cycle of CR. Simulations are conducted to illustrate that CR with the proposed waveform performs better than traditional radar system with fixed waveform, and offers more practical and flexible.

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.

In this paper, the problem of sequential waveform design for target enumeration for cognitive multiple-input single-output (MISO) radar is investigated. In the proposed technique, the transmit spatial waveform is adaptively determined at each step based on observations in the previous steps. The waveform is determined to minimize an approximated lower bound on the average sample number (ASN) required to achieve given error rates. The algorithm is tested via simulations and shown to exhibit superior performance compared to orthogonal waveform transmission.

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.

The problems of multiple-input multiple-output (MIMO) radar adaptive waveform design in additive white Gaussian noise channels and multitarget recognition based on sequential likelihood ratio test are jointly addressed in this paper. Two information-theoretic waveform design strategies, namely, the optimal waveform for maximizing the mutual information (MI) between the extended target impulse response and the target echoes and the optimal waveform for maximizing the Kullback-Leibler (KL) divergence (or relative entropy), are applied in the multitarget recognition application. For multitarget case, two adaptive waveform design methods for all possible targets based on the current knowledge of each hypothesis are proposed. Method 1 is the probability weighted waveform method. Method 2 is the probability weighted target signature method. The optimal waveform is transmitted and adaptively changed such that a decision is made based on the likelihood ratio after several illuminations. Numerical results demonstrate that the best waveform is the KL divergence-based optimal waveform using Method 1 as it has the lowest average illumination number and the highest correct decision rate for target recognition. By optimally designing and adaptively changing the transmitted waveform, the average number of illuminations required for multitarget recognition can be much reduced.

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.

Radar performance is strongly dependent on the transmitted waveform and its parameters which must be adapted to the surrounding environment, radar mission, goal, and task. Waveform diversity is a relatively new paradigm involving a continuous variation and adaptation of the transmitted signal to dynamically optimize the radar performance and fulfill the more and more stressing requirements. In this context, cognitive radar waveform design in spectrally dense environments is a very challenging and topical problem. This paper deals with the synthesis of signals optimizing radar capabilities while satisfying spectral compatibility constraints. Specifically, the design of radar waveforms, sharing appealing features and ensuring spectral coexistence with other Radio Frequency (RF) systems, is introduced and discussed according to a rigorous framework.

We consider waveform design for radar space time adaptive processing (STAP), accounting for the waveform dependence of the clutter correlation matrix. It was shown previously in [1], that the joint problem of receiver weight vector optimization and radar waveform design is an intractable optimization problem, and constrained alternating minimization was proposed. In this paper, we propose projected gradient minimization. This minimization algorithm affords a numerically stable, lower computational complexity solution than the previously proposed alternating minimization. However, as a trade-off it results in larger iteration counts to achieve similar error variances of the STAP filter. Step size rules and the optimal step size for descent are derived analytically and validated via numerical simulations.

A simple and successful design for an adaptive radar phase-coded waveform is presented. The optimised phase-coded waveform is designed by the phase-only conjugate gradient method. The signal-to-interference-plus-noise ratio of the optimised phase-coded waveform is compared with an optimal waveform and good approximation is achieved.

This paper focuses on the radar waveform design in a spectrally crowded environment. A spectral constraint is forced on the radar waveform to ensure spectral compatibility with the overlayed licensed electromagnetic radiators. The waveform design problem is formulated by maximizing the output signal-to-interference-plus-noise ratio (SINR) under the energy, similarity and spectral constraints. Inspired by the feasibility and high-quality solution properties of feasible point pursuit successive convex approximation (FPP-SCA), we develop an efficient al-gorithm framework based on FPP-SCA to solve the waveform design problem. Numerical results demonstrate the effectiveness of the proposed algorithm.

The 18 papers in this issue focus on the following areas: waveform design; adaptive waveform design; surveillance and classification in radar; multiple-input multiple-output (MIMO) radar systems; and wireless communications.

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76–81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

Improving parameter estimation with an efficient sequential probabilistic collocation-based optimal design method

To solve the coexistence of ultra wideband (UWB) system with other existed narrowband systems operating within the UWB bandwidth, we propose an adaptive non-linear chirp UWB waveform based on the atan function in this paper. With the utilization of cognitive radio (CR) technology and the good time-frequency relationship of the chirp function, the flexible multiple narrowband interferences (NBIs) suppression can be achieved. Simulation results demonstrate the superior multiple NBIs suppression ability of the proposed scheme in direct sequence bipolar pulse amplitude modulation (DS-BPAM) UWB system, which is also compared with the linear chirp pulse based UWB system.