skip to main content
Explore Scholarly Publications and Datasets in the NSF-PAR

Title: Direction Finding and Likelihood Ratio Detection for Oceanographic HF Radars
Abstract Previous work with simulations of oceanographic high-frequency (HF) radars has identified possible improvements when using maximum likelihood estimation (MLE) for direction of arrival; however, methods for determining the number of emitters (here defined as spatially distinct patches of the ocean surface) have not realized these improvements. Here we describe and evaluate the use of the likelihood ratio (LR) for emitter detection, demonstrating its application to oceanographic HF radar data. The combined detection–estimation methods MLE-LR are compared with multiple signal classification method (MUSIC) and MUSIC parameters for SeaSonde HF radars, along with a method developed for 8-channel systems known as MUSIC-Highest. Results show that the use of MLE-LR produces similar accuracy, in terms of the RMS difference and correlation coefficients squared, as previous methods. We demonstrate that improved accuracy can be obtained for both methods, at the cost of fewer velocity observations and decreased spatial coverage. For SeaSondes, accuracy improvements are obtained with less commonly used parameter sets. The MLE-LR is shown to be able to resolve simultaneous closely spaced emitters, which has the potential to improve observations obtained by HF radars operating in complex current environments. Significance Statement We identify and test a method based on the likelihood ratio more » (LR) for determining the number of signal sources in observations subject to direction finding with maximum likelihood estimation (MLE). Direction-finding methods are used in broad-ranging applications that include radar, sonar, and wireless communication. Previous work suggests accuracy improvements when using MLE, but suitable methods for determining the number of simultaneous signal sources are not well known. Our work shows that the LR, when combined with MLE, performs at least as well as alternative methods when applied to oceanographic high-frequency (HF) radars. In some situations, MLE and LR obtain superior resolution, where resolution is defined as the ability to distinguish closely spaced signal sources. « less
Authors:
; ;
Award ID(s):
1658475 1831937
Publication Date:
NSF-PAR ID:
10325099
Journal Name:
Journal of Atmospheric and Oceanic Technology
Volume:
39
Issue:
2
Page Range or eLocation-ID:
223 to 235
ISSN:
0739-0572
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , Journal of Atmospheric and Oceanic Technology)
    Abstract

    While land-based high-frequency (HF) radars are the only instruments capable of resolving both the temporal and spatial variability of surface currents in the coastal ocean, recent high-resolution views suggest that the coastal ocean is more complex than presently deployed radar systems are able to reveal. This work uses a hybrid system, having elements of both phased arrays and direction finding radars, to improve the azimuthal resolution of HF radars. Data from two radars deployed along the U.S. East Coast and configured as 8-antenna grid arrays were used to evaluate potential direction finding and signal, or emitter, detection methods. Direction finding methods such as maximum likelihood estimation generally performed better than the well-known multiple signal classification (MUSIC) method given identical emitter detection methods. However, accurately estimating the number of emitters present in HF radar observations is a challenge. As MUSIC’s direction-of-arrival (DOA) function permits simple empirical tests that dramatically aid the detection process, MUSIC was found to be the superior method in this study. The 8-antenna arrays were able to provide more accurate estimates of MUSIC’s noise subspace than typical 3-antenna systems, eliminating the need for a series of empirical parameters to control MUSIC’s performance. Code developed for this researchmore »has been made available in an online repository.

    « less
  2. ; ; ( , 2020 IEEE Asia-Pacific Microwave Conference (APMC))
    While Doppler radar measurement of respiration has shown promise for various healthcare applications, simultaneous sensing of respiration for multiple subjects in the radar field of view remains a significant challenge as reflections from the subjects are received as an interference pattern. Prior research has demonstrated the basic feasibility of using phase comparison with a 24-GHz Monopulse radar for isolation of one subject when another subject was in view, by estimating each subject's angular location with 88% accuracy. The integration of the high-resolution Multiple Signal Classification (MUSIC) algorithm with a phase-comparison technique is proposed to achieve robust accuracy for practical multi-subject respiration monitoring. Experimental results for this work demonstrate that the MUSIC pseudo-spectrum can separate two subjects 1.5 meters apart from each other at a distance of 3 meters from the sensor, using the same antenna array elements, spacing, and experimental scenarios previously reported for phase comparison Monopulse alone. Experimental results demonstrate that the MUSIC algorithm outperforms the phase-comparison technique with an azimuth angular position estimation accuracy over 95%. Higher accuracy indicates the system has improved robustness concerning noise and interference.
  3. ( , History of Geo- and Space Sciences)
    Abstract. Part I of this history describes the motivations for developing radars in the high frequency (HF) band to study plasma density irregularities in the F region of the auroral zone and polar cap ionospheres. French and Swedish scientists were the first to use HF frequencies to study the Doppler velocities of HF radar backscatter from F-region plasma density irregularities over northern Sweden. These observations encouraged the author of this paper to pursue similar measurements over northeastern Alaska, and this eventually led to the construction of a large HF-phased-array radar at Goose Bay, Labrador, Canada. This radar utilized frequencies from 8–20 MHz and could be electronically steered over 16 beam directions, covering a 52∘ azimuth sector. Subsequently, similar radars were constructed at Schefferville, Quebec, and Halley Station, Antarctica. Observations with these radars showed that F-region backscatter often exhibited Doppler velocities that were significantly above and below the ion-acoustic velocity. This distinguished HF Doppler measurements from prior measurements of E-region irregularities that were obtained with radars operating at very high frequency (VHF) and ultra-high frequency (UHF). Results obtained with these early HF radars are also presented. They include comparisons of Doppler velocities observed with HF radars and incoherent scatter radars, comparisons of plasmamore »convection patterns observed simultaneously in conjugate hemispheres, and the response of these patterns to changes in the interplanetary magnetic field, transient velocity enhancements in the dayside cusp, preferred frequencies for geomagnetic pulsations, and observations of medium-scale atmospheric gravity waves with HF radars.« less
  4. ; ; ; ; ; ( , The International Journal of Robotics Research)

    In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOAmore »estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2environment with occlusions.

    « less
  5. ; ; ( , IISE Transactions)
    The actual failure times of individual components are usually unavailable in many applications. Instead, only aggregate failure-time data are collected by actual users, due to technical and/or economic reasons. When dealing with such data for reliability estimation, practitioners often face the challenges of selecting the underlying failure-time distributions and the corresponding statistical inference methods. So far, only the exponential, normal, gamma and inverse Gaussian distributions have been used in analyzing aggregate failure-time data, due to these distributions having closed-form expressions for such data. However, the limited choices of probability distributions cannot satisfy extensive needs in a variety of engineering applications. PHase-type (PH) distributions are robust and flexible in modeling failure-time data, as they can mimic a large collection of probability distributions of non-negative random variables arbitrarily closely by adjusting the model structures. In this article, PH distributions are utilized, for the first time, in reliability estimation based on aggregate failure-time data. A Maximum Likelihood Estimation (MLE) method and a Bayesian alternative are developed. For the MLE method, an Expectation-Maximization algorithm is developed for parameter estimation, and the corresponding Fisher information is used to construct the confidence intervals for the quantities of interest. For the Bayesian method, a procedure for performingmore »point and interval estimation is also introduced. Numerical examples show that the proposed PH-based reliability estimation methods are quite flexible and alleviate the burden of selecting a probability distribution when the underlying failure-time distribution is general or even unknown.« less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email