Adaptive Waveform Design Research Articles

Optimal Adaptive Waveform Design Utilizing an End-to-End Learning-Based Pre-Equalization Neural Network in an UVLC System

Visible light communication (VLC) has emerged as a promising communication method in the special field of 6G, such as intersatellite communication (ISC) and underwater wireless optical communication (UWOC). Green light-emitting diode (LED)-based underwater visible light communication (VLC) is considered a potential candidate for UWOC of mobile autonomous underwater vehicle (AUV) and remotely operated underwater vehicle, particularly in coastal and harbor environments. However, due to bandwidth limitations and variable underwater channels, implementing high-speed underwater VLC remains a challenge. In this paper, a neural network-based auto equalization model (NNAEM) using end-to-end learning is proposed to achieve a high-speed underwater VLC link with a bandwidth-limited green LED. The entire NNAEM includes a neural network-based channel model, a pre-equalization neural network, and a post-equalization neural network. Based on the data-driven channel model, our auto equalization (AE) method can dynamically pre-equalize the discrete multitone (DMT) modulated signal in different bandwidth-limited cases and various nonlinearity cases. An underwater VLC link with a data rate of 3.451 Gbps and a transmission distance of 1.2 m is experimentally demonstrated by employing the proposed NNAEM under the hard decision-forward error correction (HD-FEC) threshold of 3.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The data rate improvement is up to 30.4% compared to other advanced digital signal processing (DSP) methods. The scenario adaptation and robustness of the NNAEM are also verified in the experiments, which demonstrate that the NNAEM can serve an important role in underwater high-speed VLC transmission.

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.

Waveform Design for Multi-Target Detection Based on Two-Stage Information Criterion.

Parameter estimation accuracy and average sample number (ASN) reduction are important to improving target detection performance in sequential hypothesis tests. Multiple-input multiple-output (MIMO) radar can balance between parameter estimation accuracy and ASN reduction through waveform diversity. In this study, we propose a waveform design method based on a two-stage information criterion to improve multi-target detection performance. In the first stage, the waveform is designed to estimate the target parameters based on the criterion of single-hypothesis mutual information (MI) maximization under the constraint of the signal-to-noise ratio (SNR). In the second stage, the objective function is designed based on the criterion of MI minimization and Kullback–Leibler divergence (KLD) maximization between multi-hypothesis posterior probabilities, and the waveform is chosen from the waveform library of the first-stage parameter estimation. Furthermore, an adaptive waveform design algorithm framework for multi-target detection is proposed. The simulation results reveal that the waveform design based on the two-stage information criterion can rapidly detect the target direction. In addition, the waveform design based on the criterion of dual-hypothesis MI minimization can improve the parameter estimation performance, whereas the design based on the criterion of dual-hypothesis KLD maximization can improve the target detection performance.

Adaptive Waveform Design for Communication-Enabled Automotive Radars

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.

Adaptive Waveform Design with Multipath Exploitation Radar in Heterogeneous Environments

The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from the adjacent cells. The proposed GLRT is also used at the end of the second transmission for the final decision. Extensive performance evaluation of the proposed detector and adaptive waveform for various multipath scenarios is presented. The performance analysis prove that the proposed method improves the Signal-to-Clutter Ratio (SCR) of the received signal, and the detection performance with multipath exploitation.

Adaptive Waveform Design for Automotive Joint Radar-Communication Systems

Unified waveform design for automotive joint radar-communications (JRC) leverages the scarce spectrum efficiently and has become a key topic for investigation of late. Designing such a waveform necessitates meeting the requirements of both systems, thereby making it a challenging task. The contribution of this paper is to formulate the JRC design problem into an optimization problem and propose an algorithm to maximize the signal-to-clutter-plus-noise-ratio (SCNR) of radar system and signal-to-noise-ratio (SNR) at communicating vehicle, simultaneously. Central to this are the exploitation of the communication link to acquire environment/ channel information and enhance radar tasks, flexibility to impart trade-off between the two systems during design as well the formulation of the optimization problem to include sidelobe constraints and yield solutions robust to Doppler shifts. The designed waveforms exhibit enhanced radar performance in terms of probability of detection and communication performance in terms of bit error rate (BER), while taking into account the trade-off between two systems. The numerical simulations corroborate the claim of optimized performance with environment/ channel information, ease of effecting trade-off and the use of design flexibility.

Adaptive Transmit Waveform Design Using Multitone Sinusoidal Frequency Modulation

This paper presents an adaptive waveform design method using Multi-Tone Sinusoidal Frequency Modulation (MTSFM). The MTSFM waveform's modulation function is represented as a finite Fourier series expansion. The Fourier coefficients are utilized as a discrete set of design parameters that may be modified to adapt the waveform's properties. The MTSFM's design parameters are adjusted to shape the spectrum, Auto-Correlation Function (ACF), and Ambiguity Function (AF) shapes of the waveform. The MTSFM waveform model naturally possesses the constant envelope and spectrally compact waveforms that make it well suited for transmission on practical radar/sonar transmitters which utilize high power amplifiers. The MTSFM has an exact mathematical definition for its time-series using Generalized Bessel Functions which allow for deriving closed-form analytical expressions for its spectrum, AF, and ACF. These expressions allow for establishing well-defined optimization problems that finely tune the MTSFM's properties. This adaptive waveform design model is demonstrated by optimizing MTSFM waveforms that initially possess a "thumbtack-like" AF shape. The resulting optimized designs possess substantially improved sidelobe levels over specified regions in the range-Doppler plane without increasing the Time-Bandwidth Product (TBP) that the initialized waveforms possessed. Simulations additionally demonstrate that the optimized thumbtack-like MTSFM waveforms are competitive with thumbtack-like phase-coded waveforms derived from design algorithms available in the published literature.

Robust Time Resource Management in Cognitive Radar Using Adaptive Waveform Design

The Cognitive Radar is almost a new research topic, in which most efforts have been done for its conceptual description and the adaptive waveform design feature of these radars. While other aspect of adaptive action for optimum performance of cognitive radars has been ignored. In this paper, a framework for adaptive time resource management in Cognitive Radars is proposed. The main purpose of this paper is proposing an algorithm for time resource management, with incorporation of adaptive waveform design capability of cognitive radars, to enhance the radar performance for an efficient time resource usage. After developing the equations for radar time resource management using adaptive waveform design for the first time, an implementable algorithm is proposed and its performance is simulated and analysed. The results show that using the proposed algorithm resulted in a more efficient time resource management compared to the existing ones.

Adaptive waveform design for interference mitigation in SAR

This work details an interference mitigation scheme for synthetic aperture radar (SAR) which adapts the transmitted waveform spectrum to restore the quality of the scene range-profile based on the estimated interference spectrum. By expressing the estimation of the range-profile in the presence of correlated noise as a system identification problem, a cost function for the optimal waveform is derived. In order to respond effectively to changes in the radio frequency (RF) environment, adaptive waveform systems should perform waveform design on a small timescale, ideally on a pulse-to-pulse basis. Motivated by the need for a computationally efficient waveform design scheme, an alternative estimator is derived, which yields a closed form solution, which is shown to perform similarly to the optimal case. A series of test SAR images demonstrate the efficacy of this technique and are compared to the standard linearly frequency modulated signal and stretch processing outcome.

Radar code design to optimise detection under whitened similarity constraint

An active sensing system, such as radar, extracts the useful features of the targets of interest, by transmitting a certain waveform to a particular area and analysing the returned signal. There are two factors that are critical to the system performance, namely the receive filter and the transmitted waveform. Transmitted signal is among the most interesting factor affecting the radar system performance in complex environments. The waveform design problem is an important stage in the performance of cognitive radars. Here, the matched illumination approach is studied which specially addresses the issue of adaptive waveform design to maximise the signal-to-disturbance ratio that finally leads to an increased probability of detection. Different constraints on the designed waveform have been introduced in the literature. However, the focus is on the similarity constraint that controls the ambiguity function and modulus variations of the devised code. In more details, it is shown that this kind of constraint should be written based on the estimated background covariance matrix leading to a proposed whitened similarity constraint. It is shown by simulation that the proposed whitened constraint outperforms the well-known similarity one in resultant ambiguity function properties and probability of detection, assuming the same environmental conditions.

Frequency-Based Optimal Radar Waveform Design for Classification Performance Maximization Using Multiclass Fisher Analysis

In this article, we propose new waveform design procedures and classification schemes to improve target classification performance nonadaptively in radar systems. The new designs and schemes are all inspired by 2-class and multiclass Fisher discriminant analysis. The proposed system does not require as much computational capability as adaptive waveform design systems while also overcoming: 1) angular uncertainty in classifying high fidelity targets and 2) drops in performance experienced by nonadaptive systems when classification is extended to more than two targets. The waveform design procedure is based on an optimization problem to find the waveform that maximizes the objective function inspired by Fisher analysis under constant energy constraint. We also derive two closed-form solutions for the optimization problems under certain conditions for the 2-class and multiclass cases. All the methods are tested using synthetic and real data to show the performance of the proposed methods against the average Mahalanobis distance (AMD) nonadaptive waveform design and classifier.

Multi-Target Robust Waveform Design Based on Harmonic Variance and Mutual Information

Cognitive radar is an intelligent radar system, and adaptive waveform design is one of the core problems in cognitive radar research. In the previous studies, it is assumed that the prior information of the target is known, and the definition of target spectrum variance has not changed. In this paper, we study on robust waveform design problem in multiple targets scene. We hope that the upper and lower bounds of the uncertainty range of robustness are more close to the actual situation, and establish a finite time random target signal model based on mutual information (MI). On the basis of the optimal transmitted waveform and robust waveform based on MI, we redefine the target spectrum variance as harmonic variance, and propose a novel robust waveform design method based on harmonic variance and MI. We compare its performance with robust waveform based on original variance. Simulation results show that, in the situation of multiple targets, compared to the original variance, the MI lifting rate of robust waveform based on harmonic variance relative to the optimal transmitted waveform in the uncertainty range has great improvement. In certain circumstances, robust waveform based on harmonic variance and MI is more suitable for more targets.

An Adaptive Multi-Target Radar Waveform Design Based on PWS Algorithm.

Due to the uncertainty of radar target prior information in actual scenes, waveform design based on radar target prior information cannot meet the requirements of detection performance and parameter estimation. Aiming at the problem of waveform design for detecting multi-target in the presence of clutter, a linear probability-weighted summation (PWS) algorithm based on multi-target impulse response is proposed and includes the radar waveform design based on mutual information (MI) and signal-to-interference ratio (SINR) criteria. In view of the traditional water-filling algorithm, the problem of multi-target is further investigated in a new way to improve the overall performance of the system. The method makes a lot of deductions by using Jensen’s inequality, to determine the algorithm objective function and energy constraint. The simulation results show that the proposed algorithm has better detection performance and more accurate target information.

Mutual-Information-Based OFDM Waveform Design for Integrated Radar-Communication System in Gaussian Mixture Clutter

Existing studies on joint radar and communication systems are mainly based on a common assumption: Gaussian environment. Nevertheless, this does not hold in many complicated scenarios. In this letter, mutual information (MI)-based waveform design method for the integrated orthogonal frequency division multiplexing radar-communication system in Gaussian mixture (GM) clutter is investigated. First, from the statistical perspective, the approximate differential entropy of received signal in GM clutter is derived. Subsequently, an adaptive waveform design method is proposed by maximizing the MI, subject to three metrics: communication channel capacity; subcarrier power ratio; and energy constraint. Simulation shows the tradeoff among the above metrics.

Information theoretic waveform design for OFDM radar‐communication coexistence in Gaussian mixture interference

In this study, mutual information (MI)-based waveform design methods for the coexistence between orthogonal frequency division multiplexing (OFDM) radar system and communication system corrupted by Gaussian mixture interference are investigated for the first time. Firstly, the received signal is formulated as a Bayesian linear model. Subsequently, from a statistical perspective, the approximate differential entropy of received signal is derived, followed by three different approximate MIs and corresponding theoretic upper bounds. After that, two adaptive waveform design methods are proposed by maximising MI and minimising radar transmitted power, respectively. The numerical results show that exploiting the reflected communication signals helps to obtain higher MI value and reduce radar transmitted power. Besides, by maximising the MIs, the optimised radar waveforms remarkably outperform the fixed ones, which benefits the estimation performance.

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.

Phase Diagram-Based Sensing with Adaptive Waveform Design and Recurrent States Quantification for the Instantaneous Frequency Law Tracking.

Monitoring highly dynamic environments is a difficult task when the changes within the systems require high speed monitoring systems. An active sensing system has to solve the problem of overlapped responses coming from different parts of the surveyed environment. Thus, the need of a new representation space which separates the overlapped responses, is mandatory. This paper describes two new concepts for high speed active sensing systems. On the emitter side, we propose a phase-space-based waveform design that presents a unique shape in the phase space, which can be easily converted into a real signal. We call it phase space lobe. The instantaneous frequency (IF) law of the emitted signal is found inside the time series. The main advantage of this new concept is its capability to generate several distinct signals, non-orthogonal in the time/frequency domain but orthogonal within the representation space, namely the phase diagram. On the receiver side, the IF law information is estimated in the phase diagram representation domain by quantifying the recurrent states of the system. This waveform design technique gives the possibility to develop the high speed sensing methods, adapted for monitoring complex dynamic phenomena In our paper, as an applicative context, we consider the problem of estimating the time of flight in an dynamic acoustic environment. In this context, we show through experimental trials that our approach provides three times more accurate estimation of time of flight than spectrogram based technique. This very good accuracy comes from the capability of our approach to generate separable IF law components as well as from the quantification in phase diagram, both of them being the key element of our approach for high speed sensing.

Multi-Target Detection and Adaptive Waveform Design for Cognitive MIMO Radar

Multi-target detection is the main function of radar. Cognitive radar, which can adaptively investigate the radar scene and determine the next action based on previous measurements, has better performance. In this paper, a multi-target detection method and an adaptive waveform design algorithm for cognitive MIMO radar are proposed. In this method, the multi-target detection is modeled as a multi-hypothesis testing. The multi-hypothesis testing is investigated according to sequentially received data. Along with the multi-target detection method, an adaptive waveform design algorithm based on information theory is proposed to improve the efficiency of the multi-hypothesis testing. We adopt semi-definite relaxation technique and semi-definite programming to tackle the nonconvex design problem. Numerical examples demonstrate that the proposed multi-target detection method has better performance than the classical target detection method, and the proposed adaptive waveform design algorithm can significantly improve the performance of the proposed multi-target detection method.

Adaptive Waveform Design for MIMO Radar-Communication Transceiver.

The system architecture for an adaptive multiple input multiple output (MIMO) radar-communication transceiver is proposed. A waveform design approach for communication data embedding into MIMO radar pulse using M-ary position phase shift keying (MPPSK) waveforms is introduced. A waveform optimization algorithm for the adaptive system is presented. The algorithm aims to improve the target detection performance by maximizing the relative entropy (RE) between the distributions under existence and absence of the target, and minimizing the mutual information (MI) between the current received signals and the estimated signals in the next time instant. The proposed system adapts its MPPSK modulated inter-pulse duration to suit the time-varying environment. With subsequent iterations of the algorithm, simulation results show an improvement in target impulse response (TIR) estimation and target detection probability. Meanwhile, the system is able to transmit data of several Mbps with low symbol error rates.

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.