Yuanwei Jin

Detection by Time Reversal: Single Antenna

This paper studies the binary hypothesis test of detecting the presence or absence of a target in a highly cluttered environment by using time reversal. In time reversal, the backscatter of a signal transmitted into a scattering environment is recorded, delayed, energy normalized, and retransmitted through the medium. We consider two versions of the test-target channel frequency response assumed known or unknown-and, for each version, contrast two approaches: conventional detection (where no time reversal occurs) and time reversal detection. This leads to four alternative formulations for which we derive the optimal detector and the generalized likelihood ratio test, when the target channel frequency response is known or unknown, respectively. We derive analytical expressions for the error probabilities and the threshold for all detectors, with the exception of the time reversal generalized likelihood ratio test. Experiments with real-world electromagnetic data for two channels (free space with a target immersed in 20 scatterers and a duct channel) confirm the analytical results and show that time reversal detection provides significant gains over conventional detection. This gain is explained by the empirical distribution or type of the target channel frequency response-richer scattering channels induce types with heavier tails and larger time reversal detection gains

Time-Reversal Detection Using Antenna Arrays

The paper studies detection of a target buried in a rich scattering medium by time reversal. We use a multi-static configuration with receive and transmit arrays of antennas. In time reversal, the backscattered field is recorded, time reversed, and retransmitted (mathematically or physically) into the same scattering medium. We derive two array detectors: the time-reversal channel matched filter when the target channel response is known; and the time-reversal generalized-likelihood ratio test (TR-GLRT) when the target channel response is unknown. The noise added in the initial probing step to the time-reversal signal makes the analysis of the TR-GLRT detector non trivial. The paper derives closed form expressions for the signal-to-noise ratio gain provided by this detector over the corresponding conventional clutter subtraction energy detector in the two extreme conditions of weak and strong (electronic additive) noise and shows that time reversal provides, under weak noise, the optimal waveform shape to probe the environment. We analyze the impact of the array configuration on the detection performance. Finally, experiments with electromagnetic data collected in a multipath scattering laboratory environment confirm our analytical results. Under the realistic conditions tested, time reversal provides detection gains over conventional detection that range from 2 to 4.7 dB.

Time Reversal in Multiple-Input Multiple-Output Radar

Time reversal explores the rich scattering in a multipath environment to achieve high target detectability. Multiple-input multiple-output (MIMO) radar is an emerging active sensing technology that uses diverse waveforms transmitted from widely spaced antennas to achieve increased target sensitivity when compared to standard phased arrays. In this paper, we combine MIMO radar with time reversal to automatically match waveforms to a scattering channel and further improve the performance of radar detection. We establish a radar target model in multipath rich environments and develop likelihood ratio tests for the proposed time-reversal MIMO radar (TR-MIMO). Numerical simulations demonstrate improved target detectability compared with the commonly used statistical MIMO strategy.

Time Reversal Imaging by Adaptive Interference Canceling

We develop the time reversal adaptive interference canceler (TRAIC) time reversal beamformer (TRBF), a new algorithm to detect and locate targets in rich scattering environments. It utilizes time reversal in two stages: (1) Anti-focusing: TRAIC time reverses and then reshapes the clutter backscatter to mitigate the clutter response; (2) Focusing: TRBF time reverses the residual backscatter to focus the radar image on the target. Laboratory experiments with electromagnetic radar data in a highly cluttered environment confirm the superiority of TRAIC-TRBF over conventional direct subtraction (DS) beamform imaging.

A CFAR adaptive subspace detector for second-order Gaussian signals

We study the problem of detecting subspace signals described by the Second-Order Gaussian (SOG) model in the presence of noise whose covariance structure and level are both unknown. Such a detection problem is often called Gauss-Gauss problem in that both the signal and the noise are assumed to have Gaussian distributions. We propose adaptive detectors for the SOG model signals based on a single observation and multiple observations. With a single observation, the detector can be derived in a manner similar to that of the generalized likelihood ratio test (GLRT), but the unknown covariance structure is replaced by sample covariance matrix based on training data. The proposed detectors are constant false alarm rate (CFAR) detectors. As a comparison, we also derive adaptive detectors for the First-Order Gaussian (FOG) model based on multiple observations under the same noise condition as for the SOG model. With a single observation, the seemingly ad hoc CFAR detector for the SOG model is a true GLRT in that it has the same form as the GLRT CFAR detector for the FOG model. We give an approximate closed form of the probability of detection and false alarm in this case. Furthermore, we study the proposed CFAR detectors and compute the performance curves.

Detection of distributed sources using sensor arrays

In this paper, we consider the problem of detecting a random spatially distributed signal source by an array of sensors. We start with an approximate likelihood ratio (LR) detector and analyze its performance. Using the generalized likelihood ratio (GLR) approach, we then derive detectors under several assumptions on the available statistics. The performance of these detectors is evaluated, and the effect of the angular spread of the source is investigated. The detection performance behaves differently under different scenarios. We notice that the degrees of freedom (DOF) of the distributions of the detection statistics depend on both the signal angular spread and the number of data snapshots. Specifically, at a high SNR level and with small degrees of freedom, an increase of angular spread improves the detection performance. However, with large degrees of freedom, the increase of angular spread reduces detection performance. We provide a detailed discussion of the behavior of detection performance under various conditions. A comparison between the GLR detectors and conventional beamformer detectors is made by computer simulations. The results indicate that the GLR detectors perform better as the angular spread becomes large than that of the conventional beamformer detectors.

Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection

AbstractA multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of...

A joint design of transmit waveforms for radar and communications systems in coexistence

In this paper, we propose a dynamic spectrum allocation approach for the coexistence of a radar system with a communications system whose operating frequency ranges overlap. We develop a combined mutual information criterion for the joint waveform and power spectrum design to optimize the performance of the radar and communications systems. The performance is evaluated in terms of the minimum separation distance between the coexisting systems under the constraint that a predefined operational SINR must be achieved for both systems. Significant performance gains close to 3.5 dB for the communication system and 1 dB for the radar system are observed in terms of the minimum separation distance compared to the scenario when the waveform with equal power allocation across the frequency bandwidth is used.

Multiple Antenna Time Reversal Transmission in Ultra-Wideband Communications

In this paper we study the multiple antenna time reversal downlink transmission in an ultra-wideband (UWB) communication system which consists of access points and users. The access point has multiple antennas and the user has a single antenna. We design the UWB beamformer that focuses on the intended user while minimizing its interference on unintended users and eavesdropping access points. We show that the designed UWB beamformer is equivalent to the time reversal focusing and nulling schemes and yields better performance than the conventional delay line wideband beamformer. We verify our results using experimentally measured electromagnetic data in an indoor environment.

Time Reversal Based Broadband Synthesis Method for Arbitrarily Structured Beam-Steering Arrays

We propose a novel broadband beam pattern synthesis method to synthesize arbitrarily structured beam-steering arrays by utilizing time reversal technique. Unlike conventional frequency-domain array synthesis methods, this method achieves array excitation by taking the Fourier Transform of the time reversed version of the transient signals received by antenna elements. Using this method, designers can determine the array excitation over a wide frequency range at once by a single run of time reversal experiment, instead of performing time-consuming multi-objective optimizations of beam pattern cost-functions at each frequency. Furthermore, it does not require explicit measurements of element mutual coupling and platform scattering effects since the time reversal signals have implicitly taken into account those effects. The proposed method is theoretically analyzed with the antenna reciprocity and numerically validated with three dipole arrays of different configurations. The results show that this new method is capable of realizing accurate beam steering over a wide frequency band if the desired steering angle is located in the feasible angular scope of beam scan. The beam patterns with accurate beam steering are successfully achieved over a bandwidth of more than 1.5 GHz for three different dipole arrays, i.e., a typical linear dipole array, a grounded circular dipole array, and a dome-shaped dipole array.