Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network

We demonstrate the classification of common motions of held objects using the harmonic micro-Doppler signatures scattered from harmonic radio-frequency tags. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged handheld objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We developed narrow-band harmonic tags at 2.4/4.8 GHz that support frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-label classifier was developed and obtained a real time classification accuracy of 94.24%, with a response time of 2 seconds per sample with a data processing latency of less than 0.5 seconds.

Design of a Single-Element Dynamic Antenna for Secure Wireless Applications

We introduce a new technique for secure wireless applications using a single dynamic antenna. The dynamic antenna supports a constantly changing current distribution that generates a radiation pattern that is static in a desired direction and dynamic elsewhere, thereby imparting additional modulation on the signal and obscuring information transmitted or received outside of the secure spatial region. Dynamic currents are supported by a single feed that is switched between separate ports on a single antenna, generating two different radiation patterns. We introduce the theoretical concept by exploring an ideal complex dynamic radiation pattern that remains static in a narrow desired direction and is dynamic elsewhere. The impact on the transmission of information is analyzed, showing that the secure region narrows as the modulation order increases, and design constraints on the spatial width of the secure region as a function of modulation format are determined. We design and analyze a 2.3 GHz two-state dynamic dipole antenna and experimentally demonstrate secure wireless transmission. We show the ability to steer the secure region experimentally, and to maintain high throughput in the secure region while obscuring the information elsewhere. Our approach introduces a novel single-element technique for secure wireless applications that can be used independently from the rest of the wireless system, essentially operating as a $``$black box$"$ for an additional layer of security.

Online Expectation-Maximization Based Frequency and Phase Consensus in Distributed Phased Arrays

Distributed phased arrays are comprised of separate, smaller antenna systems that coordinate with each other to support coherent beamforming towards a destination. However, due to the frequency drift and phase jitter of the oscillators, as well as the frequency and phase estimation errors induced at the nodes, there exists decoherence that degrades the beamforming process. A decentralized frequency and phase consensus (DFPC) algorithm was proposed in prior work for undirected networks in which the nodes locally share their frequencies and phases with their neighbors to reach synchronization. Kalman filtering (KF) was also integrated with DFPC (KF-DFPC) to lower the total residual phase error upon convergence. Since these DFPC-based algorithms rely on the average consensus protocol, they do not converge for directed networks. In this paper, we propose a push-sum based frequency and phase consensus (PsFPC) algorithm for the directed networks. The residual phase error of PsFPC is theoretically derived as well. Kalman filtering is also integrated with PsFPC and the resulting KF-PsFPC algorithm shows a significant reduction in the residual phase error upon convergence. KF assumes that the model parameters, i.e., the measurement noise and innovation noise covariance matrices, are known. Since they may not be known in practice, we develop an online expectation maximization (EM) based algorithm that iteratively computes the maximum likelihood (ML) estimate of the unknown matrices in an online manner. EM is integrated with KF-PsFPC to propose the EM-KF-PsFPC algorithm. Simulation results are included where the performance of the PsFPC-based algorithms is analyzed for different distributed phased arrays and is compared to other algorithms.

Wireless Picosecond Time Synchronization for Distributed Antenna Arrays

Distributed antenna arrays have been proposed for many applications ranging from space-based observatories to automated vehicles. Achieving good performance in distributed antenna systems requires stringent synchronization at the wavelength and information level to ensure that the transmitted signals arrive coherently at the target, or that scattered and received signals can be appropriately processed via distributed algorithms. In this paper we address the challenge of high precision time synchronization to align the operations of elements in a distributed antenna array and to overcome time-varying bias between platforms due to oscillator drift. We use a spectrally sparse two-tone waveform, which obtains approximately optimal time estimation accuracy, in a two-way time transfer process. We also describe a technique for determining the true time delay using the ambiguous two-tone matched filter output, and we compare the time synchronization precision of the two-tone waveform with the more common linear frequency modulation (LFM) waveform. We experimentally demonstrate wireless time synchronization using a single pulse 40$\,$MHz two-tone waveform over a 90$\,$cm 5.8$\,$GHz wireless link in a laboratory setting, obtaining a timing precision of 2.26$\,$ps.

A Message Passing Based Average Consensus Algorithm for Decentralized Frequency and Phase Synchronization in Distributed Phased Arrays

We consider the problem of decentralized frequency and phase synchronization in distributed phased arrays via local broadcast of the node electrical states. Frequency and phase synchronization between nodes in a distributed array is necessary to support beamforming, but due to the operational dynamics of the local oscillators of the nodes, the frequencies and phases of their output signals undergo the random drift and jitter in between the update intervals. Furthermore, frequency and phase estimation errors contribute to the total phase errors, leading to a residual phase error in the array that degrades coherent operation. Recently, a classical decentralized frequency and phase synchronization algorithm based on consensus averaging was proposed with which the standard deviation of the residual phase errors upon convergence was reduced to $10^{-4}$ degrees for internode update intervals of $0.1$ ms, however this was obtained for arrays with at least $400$ nodes and a high connectivity ratio of $0.9$. In this paper, we propose a message passing based average consensus (MPAC) algorithm to improve the synchronization of the electrical states of the nodes in distributed arrays. Simulation results show that the proposed MPAC algorithm significantly reduces the residual phase errors to about $10^{-11}$ degrees, requiring only $20$ moderately connected nodes in an array. Furthermore, MPAC converges faster than the DFPC-based algorithms, particularly for the larger arrays with a moderate connectivity.

Frequency and Phase Synchronization in Distributed Antenna Arrays Based on Consensus Averaging and Kalman Filtering

A decentralized approach for joint frequency and phase synchronization in distributed antenna arrays is presented. The nodes in the array share their frequencies and phases with their neighboring nodes to align these parameters across the array. Our signal model includes the frequency drifts and phase jitters of the local oscillators as well as the frequency and phase estimation errors at the nodes and models them using practical statistics. A decentralized frequency and phase consensus (DFPC) algorithm is proposed which uses an average consensus method in which each node in the array iteratively updates its frequency and phase by computing an average of the frequencies and phases of their neighboring nodes. Simulation results show that upon convergence the DFPC algorithm can align the frequencies and phases of all the nodes up to a residual phase error that is governed by the oscillators and the estimation errors. To reduce this residual phase error and thus improve the synchronization between the nodes, a Kalman filter based decentralized frequency and phase consensus (KF-DFPC) algorithm is presented. The total residual phase error at the convergence of the KF-DFPC and DFPC algorithms is derived theoretically. The synchronization performances of these algorithms are compared to each other in light of this theoretical residual phase error by varying the duration of the signals, connectivity of the nodes, the number of nodes in the array, and signal to noise ratio of the transmitted signals. Simulation results demonstrate that the proposed KF-DFPC algorithm converges in fewer iterations than the DFPC algorithm. Furthermore, for shorter intervals between local information broadcasts, the KF-DFPC algorithm significantly outperforms the DFPC algorithm in reducing the residual total phase error, irrespective of the signal to noise ratio of the received signals.

Distortion Mitigation in Millimeter-Wave Interferometric Radar Angular Velocity Estimation Using Signal Response Decomposition

A new method of distortion mitigation for multitarget interferometric angular velocity estimation in millimeter-wave radar is presented. In general, when multiple targets are present, the response of a correlation interferometer is corrupted by intermodulation distortion, making it difficult to estimate individual target angular velocities. We present a distortion mitigation method that works by decomposing the responses at each antenna element into the responses from the individual targets. Data association is performed to match individual target responses at each antenna such that cross-correlation is performed only between associated targets. Thus, the intermodulation distortion (cross-terms) from correlating unlike targets are eliminated, and the result is a frequency response whose individual frequencies are proportional to the angular velocities of the targets. We demonstrate the approach with a custom 40 GHz interferometric radar, a high-accuracy motion capture system which provides ground-truth position measurements, and two robotic platforms. The multitarget experiments consist of three scenarios, designed to represent easy, medium, and difficult cases for the distortion mitigation technique. We show that the reduction in distortion yields angular velocity estimation errors in the three cases of less than $0.008$ rad/s, $0.020$ rad/s, and $0.033$ rad/s for the easy, medium, and hard cases, respectively.

A C-Band Fully Polarimetric Automotive Synthetic Aperture Radar

Due to the rapid increase in 76 GHz automotive spectrum use in recent years, wireless interference is becoming a legitimate area of concern. However, the recent rise in interest of automated vehicles (AVs) has also spurred new growth and adoption of low frequency vehicle-to-everything (V2X) communications in and around the 5.8 GHz unlicensed bands, opening the possibility for communications spectrum reuse in the form of joint radar-communications (JRC). In this work, we present a low frequency 5.9 GHz side-looking polarimetric synthetic aperture radar (SAR) for automotive use, utilizing a ranging waveform in a common low frequency V2X communications band. A synthetic aperture technique is employed to address the angular resolution concerns commonly associated with radars at lower frequencies. Three side-looking fully polarimetric SAR images in various urban scenes are presented and discussed to highlight the unique opportunities for landmark inference afforded through measurement of co- and cross-polarized scattering.