A flexible waveform optimization method for cognitive radar

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 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.

This paper addresses the problem of adaptive sequential waveform design for system parameter estimation. This problem arises in several applications such as radar, sonar, or tomography. In the proposed technique, the transmit/input signal waveform is optimally determined at each step, based on the measurements in the previous steps. The waveform is determined to minimize the Bayesian Cramer-Rao bound (BCRB) for estimation of the unknown system parameter at each step. The algorithm is tested for spatial transmit waveform design in multiple-input multiple-output radar target angle estimation at very low signal-to-noise ratio. The simulations show that the proposed adaptive waveform design achieves significantly higher rate of performance improvement as a function of the pulse index, compared to identical signal transmission.

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.

To improve the target recognition and classification performance of integrated radar and communications system in signal-dependent interference, we propose an innovative robust adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design taking into account the clutter. We set the uncertainty class for clutter and other prior information to solve the problem of system performance degradation caused by inaccurate information in actual environments. Then, we formulate the optimization function to maximize the normalized weighted sum of mutual information and data information rate subject to the constraint of the total transmitted energy. To solve the optimization problem, we propose a maximum marginal allocation algorithm that transforms the original problem into a multi-stage energy distribution problem, and performs optimal energy distribution in each stage according to the optimization criteria. Moreover, by considering a more practical scenario of multiple downlink communication users, a joint scheme for channel allocation and waveform design is presented to ensure the fairness of multiple communication users. Simulation results show the effectiveness and feasibility of the designed waveform and channel allocation method.

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.

In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF). In the waveform diversity community, especially within the context of spectrum sharing, MIMO and cognitive radars, several waveform optimization and design methodologies have been studied, see [1], [2] and references therein. In parallel to waveform design, several signal processing techniques have also been proposed which exploit some kind of prior knowledge and/or iterative algorithms to improve the performance of the more classical MF, such as the Adaptive Pulse Compression(APC) [3], MUSIC [4], CLEAN [5] and Sparse Signal Processing (SSP) [6], [7]. In this paper we present some results and examples to show how the combination of waveform design with SSP can lead to improved performance in radar compared to the more classical approach.

Simple pulsed radar is limited in range sensitivity by the average radiation power and, in range resolution by the pulse length. The design of any radar always involves a compromise between the two constraints. Waveform design aims to seek an appropriate harmony that best suits the relevant application. The pulse compression theory has been introduced in order to get a high range resolution as well as a good detection probability. One of the basic types of pulse compression is binary phase coding which encodes the transmitted pulse with information that is compressed (decoded) in the receiver of the radar. The study of the peak sidelobe level (PSL) binary sequences occurs as a classical problem of signal design for digital communication and, in equivalent guise, in analytic number theory. It has also become a notorious problem of combinatorial optimization. For years mathematicians, engineers, physicists and chemists have sought a systematic way to construct long binary sequences with low PSL. In this Chapter, we describe pulse compression technique in radar waveform design. In order to make the presentation self-contained, we start by providing a short summary of waveform design and an introduction to principle behind pulse compression by compiling the basic tools required for analyzing and comparing different radar signals. After that, we talk about binary sequence, its desired properties and general types of methods for finding and generating such waveforms. We keep on by an overview and introducing the existing methods and search routine done. We conclude the chapter with a brief survey of the results exhibited yet for low autocorrelation binary sequences. We mention a table of complete results presented and also introduce a histogram to unscramble them visually and predict the future.

This article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

In this paper, a low probability of intercepting radar waveform design method is proposed. The time-frequency image of the designed radar waveform is agile and random. Its time-frequency image is more complex than a chirp. This paper first determines the number of sub-pulses in a single pulse. Then find random points on the time-frequency plane of unit time and unit bandwidth. Finally, the randomly generated multi-component LFM signal is searched, and the waveform that meets the constraints, such as peak sidelobe level and main lobe width, is selected. Simulation results show that the peak sidelobe level of the waveform designed in this paper can reach as low as −23.44 dB, which is lower than the traditional chirp signal (-13.86 dB). The price is that the waveforms generated in this paper have a certain main lobe broadening compared to the traditional LFM. The simulation and analysis results show that the waveform designed in this paper is an effective low probability of intercepting radar waveform.

We consider the problem of joint transmit waveform optimization and receiver filter design in a signal-dependent clutter environment for a multistatic radar. We devise an optimization procedure that, by maximizing the Signal to Interference Noise Ratio (SINR) under certain constraints on the distortion of the transmitted signals, will iteratively optimize both the orthogonal codes and the receiver filter at the radar with centralized cognition abilities. The waveform design is based on the exploitation of initial radar codes characterized by desired auto and cross-correlation properties. Various performance analyzes demonstrate the effectiveness of the algorithm in providing a significant increase in SINR in a highly reverberating environment.

The use of information theory to design waveforms for the measurement of extended radar targets exhibiting resonance phenomena is investigated. The target impulse response is introduced to model target scattering behavior. Two radar waveform design problems with constraints on waveform energy and duration are then solved. In the first, a deterministic target impulse response is used to design waveform/receiver-filter pairs for the optimal detection of extended targets in additive noise. In the second, a random target impulse response is used to design waveforms that maximize the mutual information between a target ensemble and the received signal in additive Gaussian noise. The two solutions are contrasted to show the difference between the characteristics of waveforms for extended target detection and information extraction. The optimal target detection solution places as much energy as possible in the largest target scattering mode under the imposed constraints on waveform duration and energy. The optimal information extraction solution distributes the energy among the target scattering modes in order to maximize the mutual information between the target ensemble and the received radar waveform. >

A new scheme based on Kalman filtering to optimize the waveforms of an adaptive multi-antenna radar system for target impulse response (TIR) estimation is presented. This work aims to improve the performance of TIR estimation by making use of the temporal correlation between successive received signals, and minimize the mean square error (MSE) of TIR estimation. The waveform design approach is based upon constant learning from the target feature at the receiver. Under the multiple antennas scenario, a dynamic feedback loop control system is established to real-time monitor the change in the target features extracted form received signals. The transmitter adapts its transmitted waveform to suit the time-invariant environment. Finally, the simulation results show that, as compared with the waveform design method based on the MAP criterion, the proposed waveform design algorithm is able to improve the performance of TIR estimation for extended targets with multiple iterations, and has a relatively lower level of complexity.

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.

We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed method and analyze its computational complexity. Moreover, we offer an extended version of the method to cope with imperfect channel state information (CSI). Finally, we demonstrate its superior performance through simulations, in comparison to state-of-the-art radar-communication waveform designs.