Adaptive Waveform Research Articles

Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems: Opportunities and Challenges

This article addresses the signal processing challenges for the design of a radar-on-chip/in-package in the autonomous-driving era, taking into account recent integration trends and technology capabilities. Radar signal processing platform specifications are discussed, and the radar sensor is compared with other competing sensors, such as lidars, ultrasonics, and video cameras, that aim at detecting still or moving objects and measuring their motion parameters. This survey first focuses on signal processing techniques for a low-cost and power-efficient radar sensor, which operates in real time while ensuring the automotive coverage-range needs. The main signal processing techniques for velocity-range estimation, direction estimation, waveform design, and beamforming are analyzed with particular emphasis on the radar physical layer codesign. The future evolution of embedded computing platforms and advanced signal processing techniques are explored, such as multiple-input, multiple-output (MIMO) and cognitive radars, along with adaptive waveforms for solving interference and spectrum scarcity issues.

Adaptive traveltime inversion

We have developed a method to obtain a misfit function for robust waveform inversion. In this method, called adaptive traveltime inversion (ATI), a matching filter that matches predicted data to measured data is computed. If the velocity model is relatively accurate, the resulting matching filter is close to a Dirac delta function. Its traveltime shift, which characterizes the defocusing of the matching filter, is computed by minimization of the crosscorrelation between a penalty function such as [Formula: see text] and the matching filter. ATI is constructed by minimization of the least-squares errors of the calculated traveltime shift. Further analysis indicates that the resulting traveltime shift corresponds to a first-order moment, the mean value of the resulting matching filter distribution. We extend ATI to a more general misfit function formula by computing different order moment of the resulting matching filter distribution. Choosing the penalty function in adaptive waveform inversion (AWI) as [Formula: see text], the misfit function of AWI is the second-order moment, the variance of the resulting matching filter distribution with zero mean. Because our ATI method is based on a global comparison using deconvolution, such as AWI, it can resolve the “cycle skipping” issue. We evaluate our ATI misfit function and compare it with state-of-the-art options such as least-squares inversion (L2 norm), wave-equation traveltime inversion, and AWI using schematic examples before moving to more complex examples, such as the Marmousi model. For the Marmousi model, starting with a 1D [Formula: see text] model, with data without low frequencies (no energy below 3 Hz), a meaningful estimation of the P-wave velocity model is recovered. Our ATI misfit function (first-order moment) indicates comparable performance with the AWI misfit function (the second-order moment). We also include a real data example from the Gulf of Mexico to demonstrate the effectiveness of our method.

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.

Adaptive waveform inversion: Practice

Adaptive waveform inversion (AWI) reformulates the misfit function used to perform full-waveform inversion (FWI), so that it no longer contains local minima related to cycle skipping. It does this by finding a model that drives the ratio of the predicted and observed data sets to unity rather than driving the difference between these two data sets to zero as is the case for conventional FWI. We apply AWI to a 3D field data set acquired over a pervasive gas cloud in the North Sea, comparing its performance with that of conventional FWI in a variety of circumstances. When starting inversion from 3 Hz, and using a good starting model obtained from reflection tomography, FWI and AWI generate similar models although the FWI result contains edge artifacts that are not produced by AWI. However, when the starting frequency is increased to approximately 6 Hz, or when the starting model is less accurate, FWI fails to recover a good model whereas AWI continues to converge. When both of these conditions apply, FWI fails comprehensively, leading to a model that is significantly worse than the starting model, whereas the AWI result remains largely unaffected. We applied Kirchhoff depth migration to the fully-processed data using the FWI result obtained following reflection tomography, and using the AWI result obtained from a simple one-dimensional starting model. We use the resulting migrated volumes, together with measures of residual moveout throughout the volume, to show that the AWI result from a simple starting model is at least as good as the FWI result obtained following tomography. We conclude that AWI is robust in the presence of cycle skipping on this 3D field data set, and can proceed successfully from a less-accurate starting model, and from a higher starting frequency, in circumstances in which FWI fails completely.

Controlled tracking in urban terrain: Closing the loop

AbstractWe investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed‐loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one‐step‐ahead error covariance matrix is transmitted; the received signal goes through a matched‐filter, and curve fitting is used to extract range and range‐rate measurements that feed the LMIPDA‐VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line‐of‐sight.

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants.

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a cross-platform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (<50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform.

PDF-Euclidean Distance-Based Adaptive Waveform Selection for Maximizing Radar Practical Resolution

This paper focuses on the issue of adaptive waveform selection to optimize the radar resolvability of closely located targets, which is significant in radar detection and estimation. The well-known ambiguity function-based radar resolution only takes transmitted waveform into consideration, which cannot reflect the achievable resolution of a real radar system. In this paper, we consider radar practical resolution based on a geometric metric, Euclidean Distance between probability density functions (PDF-ED), which is defined as square difference between the probability density functions of radar measurements. The PDF-ED takes the PDF's envelope curve into account and specifies the essential difference between the two PDFs in terms of information geometry. Thus the radar practical resolution based on this conveniently characterizes the effect of waveform parameter, target state and measurement model, etc. and offers a statistical way to assess radar sensing capability for a given application. Accordingly, an adaptive waveform selection criterion aiming to maximize the practical resolution is proposed. The experimental simulations verify its effectiveness in decreasing the probability of error when distinguishing two targets.

Jammer-Nulling Transmit-Adaptive Radar Against Knowledge-Based Jammers in Electronic Warfare

The authors present transmit-adaptive jammer nulling waveforms in cognitive radar (CRr) for aircraft response recognition as a form of electronic support in the electronic warfare domain. New forms of knowledge-based noise jammers that function as a form of electronic attack to matched illumination waveforms are also introduced. These knowledge-based noise jamming techniques capitalize on prior knowledge of the target’s frequency response to obscure the true target response echo. The results from simulation show that real-time adaptive waveforms suppress the effects of electronic attack noise jammers and the newly introduced knowledge-based noise jammers. High bandwidth radar cross section (RCS) responses via electromagnetic (EM) simulations of true-to-physical-size aircraft are used for simulations to produce CRr classification performance against a comprehensive list of noise jammers.

Complexity Analysis of Time‐Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency

The multisource impact signal of rolling bearings often represents nonlinear and nonstationary characteristics, and quantitative description of the complexity of the signal with traditional spectrum analysis methods is difficult to be obtained. In this study, firstly, a novel concept of local frequency is defined to develop the limitation of traditional frequency. Then, an adaptive waveform decomposition method is proposed to extract the time‐frequency features of nonstationary signals with multicomponents. Finally, the normalized Lempel–Ziv complexity method is applied to quantitatively measure the time‐frequency features of vibration signals of rolling bearings. The results indicate that the time‐frequency features extracted by the proposed method have clear physical meanings and can accurately distinguish the different fault states of rolling bearings. Furthermore, the normalized Lempel–Ziv complexity method can quantitatively measure the nonlinearity of the multisource impact signal. So, it supplies an effective basis for fault diagnosis of rolling bearings.

Fault Feature Extraction of Reciprocating Compressor Based on Adaptive Waveform Decomposition and Lempel-Ziv Complexity

The multi-source impact signal of reciprocating compressor often represents nonlinear and non-stationary. For this reason, the fault features of the signal are difficult to quantitatively describe using conventional signal processing methods. In this paper, a novel adaptive waveform decomposition method was proposed to convert the strong non-stationary multi-component signal into stationary single-component signal. Subsequently, the signal was denoised with threshold-based mutual information to protect from being interfered by the noise. Finally, to measure the nonlinearity of reciprocating compressor signals in four states (normal valve sheets, gap valve sheets, fractured valve sheets, and bad spring), the normalized Lempel-Ziv complexity indexes were employed. The results reveal that the proposed method is capable of extracting the different faults states of reciprocating compressor accurately, which supplies a measure of fault diagnosis and maintenance strategy for the reciprocating compressor.

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.

Ground vehicle target signature identification with cognitive automotive radar using 24–25 and 76–77 GHz bands

The authors present cognitive automotive radar (CARr) as a significant capability to the sensing technology suite of autonomous driving vehicles. CARr is a closed-loop intelligent radar system utilising the automotive frequency bands of 24–25 and 76–77 GHz for the purposes of ground vehicle target signature identification. The authors consider specific cases of ground vehicle recognition and ground vehicle class identification in the presence of aspect angle uncertainty using forward-looking automotive radar. The transmit-adaptive waveforms are based from signal-to-noise ratio and mutual information optimisation metrics. In this study, two new adaptive waveform techniques with the flexibility to accommodate angular uncertainty probability distributions are introduced. The classification performance of these new waveforms is compared against the receive-adaptive wideband pulsed and other transmit-adaptive waveforms in the presence of angular uncertainties characterised by the uniform and truncated normal distributions. To ensure the validity of results, high-fidelity electromagnetic simulated radar cross-section signatures generated from scaled-to-true-physical-size ground vehicle computer-aided design models are utilised.

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.

Adaptive waveform selection for maneuvering target tracking in cognitive radar

Cognitive radar can adjust the transmitted waveform adaptively to the external dynamic environment for a better target tracking performance than the traditional radar transmitting the fixed waveform. There are two key problems in the tracking system of cognitive radar, which are how to build the transmitted waveform library and how to select the optimal waveform for target tracking in the waveform library. The problem of adaptive waveform selection for maneuvering target tracking is studied in this paper. The factors influencing the target tracking accuracy are analyzed, and the maneuvering target tracking framework in cognitive radar is proposed. Considering that the narrow pulses correspond to good delay but poor Doppler resolution capability and that the long constant-frequency pulses have the opposite behavior, we build a transmitted waveform library consisting of the waveforms with both types of properties using the fractional Fourier transform (FrFT) technique. And the criteria for waveform selection in the control theoretic and information theoretic approaches are discussed in detail. The corresponding solutions of waveform selection in the waveform library are given, respectively. The superior performance of the adaptive waveform over the fixed waveform is illustrated with simulation examples. The simulations demonstrate that the target tracking algorithm is also very important for waveform selection.

Adapitive waveform design for distributed OFDM MIMO radar system in multi-target scenario

In order to improve detection and estimation performance of distributed Orthogonal-Frequency-Division Multiplexing (OFDM) Multiple-Input Multiple-Output (MIMO) radar system in multi-target scene, we propose a novel approach of Adaptive Waveform Design (AWD) based on a constrained Multi-Objective Optimization (MOO). The sparse measurement model of this radar system is derived, and the method based on decomposed Dantzig selectors is applied for the sparse recovery according to the block structures of the sparse vector and the system matrix. An AWD approach is proposed, which optimizes two objective functions, namely minimizing the upper bound of the recovery error and maximizing the weakest-target return, by adjusting the complex weights of the emitting waveform amplitudes. Several numerical simulations are provided and their results show that the detection and estimation performance of the radar system is improved significantly when this MOO-based AWD approach is applied to the distributed OFDM MIMO radar system. Especially, we verify the effectiveness of our AWD approach when the available samples are reduced severally and the technique of compressed sensing is introduced.

Capacity Gains for Single- and Multi-User CAW in FBMC-OQAM Systems

Channel adaptive waveforms (CAWs) as an extension of channel adaptive modulation (CAM) have been shown to be a promising design concept for wireless communication systems in heterogeneous environments. In contrast to prior works, this letter investigates the performance gains for CAW based on channel capacity for cell-specific and user-specific system configurations for FilterBank MultiCarrier (FBMC) with offset quadrature amplitude modulation subcarrier modulation systems in consideration of required inter-user guard bands. The results show that the cell- and user-specific CAWs provide a low-complexity system design, which is capable of fighting the interference problem for systems without a cyclic prefix, thereby improving the cell capacity in urban and hilly environments up to 90%.

Superposition Coded-Orthogonal Frequency Division Multiplexing

Orthogonal frequency division multiplexing (OFDM) technique is the most used approach in various wireless communication technologies due to its implementation advantages. However, OFDM has also some significant drawbacks, such as the sensitivity against real-time impairments and the poor peak-to-average power ratio (PAPR) performance. These drawbacks decrease the usage potential of OFDM in future networks. Recent OFDM alternatives, which have been proposed in 5G waveform research activities, can provide limited improvements and these mostly include modified filtering operations when compared to the OFDM. In this paper, as a novel and flexible approach, superposition coding is adopted to OFDM, and superposition coded-OFDM (SC-OFDM) is proposed. By exploiting superposition coding as a new transmission dimension, an adaptive waveform solution that solves the PAPR problem without creating any inefficiency is provided. At the same time, throughput or error performance, and sensitivity against real-time impairments can also be improved. Features for each problem are proposed within the SC-OFDM approach. Performances of each SC-OFDM features are measured with extensive computer simulations and real-time experiments. Real-time experiments are realized by using software defined radio nodes and these are compared with the computer simulation results. To the best of authors’ knowledge, such a comprehensive waveform design is proposed for the first time in the literature. As observed with the results, SC-OFDM can meet the requirements of future communication technologies.

Cognitive Tracking Waveform Design Based on Multiple Model Interaction and Measurement Information Fusion

To enhance maneuvering target tracking in modern battlefield, cognitive radar could adjust its waveforms and information processing manner. In this paper, a novel adaptive waveform design method based on multiple model interaction and measurement information fusion is developed. First, some latest measurements and virtual ones are collected to exploit more robust information. Second, the unknown target state is formulated via the multi-model idea, and the tracking framework is highlighted by the matrix-weighted multi-model fusion (MMF) in lieu of the probability-weighted way. Finally, the MMF output covariance matrix is selected as the ellipse metric, and ellipse parameters can be obtained by using the eigenvalue decomposition. Given these parameters, fractional Fourier transform is used to rotate the measurement error-ellipse to make them orthogonal, and further obtain the desirable rotating orientations for the cognitive transmitting waveform. Simulations show that compared with several algorithms, e.g., MIMM and IMM, our algorithm could further improve tracking performance and robustness.

An Adaptive OFDM Detection Strategy for Range and Doppler Spread Targets in Non-Gaussian Clutter

Orthogonal frequency division multiplexing (OFDM) radar can obtain higher target detection performance than traditional single carrier frequency radar due to simultaneously multiple frequency measurements. Meanwhile, the detection performance can be further improved by utilizing both the range and Doppler distribution information for extended targets. In this paper, an OFDM detection strategy for range and Doppler spread targets in non-Gaussian clutter is proposed, which comprises a new generalized likelihood ratio test (GLRT) detector and an adaptive waveform design method. The deduced GLRT detector is an asymptotically optimal detector and has a constant false alarm rate property. Comparisons show that the GLRT detector outperforms other existed detectors, which use the range-only spread model or the point-like target model. Furthermore, by optimizing the transmitted weights corresponding to different subcarriers of the OFDM signal, an approximate 2-dB output signal-to-clutter ratio improvement is achieved, which benefits the target detection with a cognitive manner.