Yuriy Goykhman

A Wireless Soil Moisture Smart Sensor Web Using Physics-Based Optimal Control: Concept and Initial Demonstrations

This paper introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective of the technology, supported by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors. A potential application for this technology is the validation of products from the Soil Moisture Active/Passive (SMAP) mission. Spatially, the total variability in soil-moisture fields comes from variability in processes on various scales. Temporally, variability is caused by external forcings, landscape heterogeneity, and antecedent conditions. Installing a dense in-situ network to sample the field continuously in time for all ranges of variability is impractical. However, a sparser but smarter network with an optimized measurement schedule can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic physics-based hydrologic and sensor modeling system. The overall design of the smart sensor web-including the control architecture, physics-based hydrologic and sensor models, and actuation and communication hardware-is presented in this paper. We also present results illustrating sensor scheduling and estimation strategies as well as initial numerical and field demonstrations of the sensor web concept. It is shown that the coordinated operation of sensors through the control policy results in substantial savings in resource usage.

Measurement Scheduling for Soil Moisture Sensing: From Physical Models to Optimal Control

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.

Smart antenna technology for structural health monitoring applications

A smart antenna has been developed for structural health monitoring. The antenna is based on Monarchs GEN 2 selfstructuring antenna (SSA) technology and provides polarization and beam-diversity for improving signal-to-noise ratio (SNR). The antenna works with University of Michigans Narada platform, where a microcontroller monitors the RSSI and selects the best beam to maintain reliable RF link. Antenna has two wide beams for each polarization and the beams are selected by applying appropriate DC voltages to the RF switches on the antenna aperture. Paper presents the GEN C antenna, which is a smaller version of the GEN 2B with comparable performance features.

A Soil Moisture Smart Sensor Web using Data Assimilation and Optimal Control: Formulation and First Laboratory Demonstration

We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. One potential application is the Soil Moisture Active/Passive (SMAP) mission. The science measurements considered are the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in-situ sensors. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic data assimilation system subject to energy and accuracy constraints. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware are presented in this paper. We also present results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture.

Retrieval of Parameters for Three-Layer Media with Nonsmooth Interfaces for Subsurface Remote Sensing

A solution to the inverse problem for a three-layer medium with nonsmooth boundaries, representing a large class of natural subsurface structures, is developed in this paper using simulated radar data. The retrieval of the layered medium parameters is accomplished as a sequential nonlinear optimization starting from the top layer and progressively characterizing the layers below. The optimization process is achieved by an iterative technique built around the solution of the forward scattering problem. The forward scattering process is formulated by using the extended boundary condition method (EBCM) and constructing reflection and transmission matrices for each interface. These matrices are then combined into the generalized scattering matrix for the entire system, from which radar scattering coefficients are then computed. To be efficiently utilized in the inverse problem, the forward scattering model is simulated over a wide range of unknowns to obtain a complete set of subspace-based equivalent closed-form models that relate radar backscattering coefficients to the sought-for parameters including dielectric constants of each layer and separation of the layers. The inversion algorithm is implemented as a modified conjugate-gradient-based nonlinear optimization. It is shown that this technique results in accurate retrieval of surface and subsurface parameters, even in the presence of noise.

Frequency tunable antenna for LTE (4G) handsets operating in the 2.3–2.7GHz global roaming band

A frequency tunable antenna for 4G global roaming devices operating in the 2.3-2.7GHz band is presented. Both the design and manufacturing methods are described and measured data are provided. Antenna is a half-patch with a reconfigurable aperture realized by a collection of shorting pins that are controlled by DC signals. The design follows the teachings of the patented self-structuring antenna technology and can be operated either in open or closed loop fashion. The frequency tunable feature of the antenna also makes it immune to detuning when used in a closed loop control system. Though the design is compatible with a multitude of manufacturing and embedding methods, the particular prototype was built by wire-bonding bare-die SPST switches onto the antenna board.

Multi-core CPU and GPU accelerated FMM-FFT solver for antenna co-site interference analysis on large platforms

A surface method of moment (MoM)-based computational electromagnetics solver utilizing the fast multipole method combined with the fast Fourier transform (FMM-FFT) is applied to the analysis of antenna co-site interference problems on realistically large and complex platforms. The code is hardware-accelerated by parallelizing and vectorizing the computational bottlenecks to take advantage of the abundance of multi-core CPUs and graphics processing units (GPUs). Antenna coupling data are validated using measured data, and scaling results are presented.

Electromagnetic scattering models of layered random rough surfaces and their role in addressing some of the grand challenges of climate research

Several layered rough surface and random media scattering models recently developed are discussed, including analytical methods based on small perturbation method and the stabilized extended boundary condition method. It is also shown how some of these models are being used to retrieve subsurface properties from synthetic and experimental data. This is an overview paper on the above topics, with many of them described in more detail in other papers whose citations are provided here.

Recent theoretical and experimental advances in electromagnetic sensing of subsurface profiles

We present an overview of recent developments in subsurface image formation using iterative profile inversion and focusing using concepts of tomography and SAR signal processing. Electromagnetic forward and inverse scattering techniques are presented, along with the subsequent focusing techniques. Experimental and simulation results are presented.

Radar retrieval of subsurface parameters for layered media with nonsmooth interfaces

The solution to the inverse problem for a three-layer medium representing a large class of natural subsurface structures is developed in this paper using radar data. The retrieval of the layered medium parameters is accomplished as a sequential nonlinear optimization starting from the top layer and progressively characterizing the layers below. The optimization process is accomplished by an efficient iterative technique built around the solution of the forward scattering problem. The forward scattering process is formulated by using the Extended Boundary Condition Method (EBCM) and constructing reflection and transmission matrices for each interface. These matrices are then combined into the generalized scattering matrix for the entire system, from which radar scattering coefficients are then computed. To be efficiently utilized in the inverse problem, the forward scattering model is simulated over a wide range of unknowns to obtain a complete set of subspace-based equivalent closed form models that relate radar cross section coefficients to the sought-for parameters including dielectric constants of each layer and separation of the layers. The inversion algorithm is implemented as a modified conjugate-gradient-based nonlinear optimization. It is assumed that multifrequency radar measurements are available from tower-mounted or airborne platforms, for example at typical radar frequencies of L-band and P-band (UHF). It is shown that this technique results in accurate retrieval of surface and subsurface parameters, even in the presence of noise.