OPEN-SOURCING OF A SOOP SIMULATOR WITH BISTATIC VEGETATION SCATTERING MODEL Orhan Eroglu, Dylan Boyd, Mehmet Kurum Department of Electrical and Computer Engineering Mississippi State University Mississippi State, MS, 39762, USA
[email protected] ABSTRACT 1. INTRODUCTION Conventional microwave remote sensing has been performed with mono-static active radars for decades. However, SoOp (Signal of Opportunity) has been gaining a great interest among researchers in recent years because it removes costs for a transmitter antenna by reception of existing direct and/or reflected signals. Although SoOp has produced encouraging results for the remote sensing of ocean surface roughness and wind vectors, the concept is still emerging and requires exhaustive analysis in order to be applied on land observations such as retrieval of biomass, soil moisture, surface topography, and snow depth. Bistatic analytical models and simulators can fulfill the need for analysis. They create environments that enable computation, validation, and examination of methods for future missions, which are difficult to perform in the real world experiments. Being motivated by this phenomenon, we have developed a generalized coherent forward model of bistatic scattering from vegetation cover for SoOp applications with the name SCoBi-Veg (SoOp Coherent Bistatic Scattering Model for Vegetated Terrains), which is currently under review by IEEE Transactions on Geoscience and Remote Sensing [1][2]. We have also developed a simulator that employs SCoBi-Veg model, for the sake of creating a medium for a community of researchers, scientists, and users with little-orno electromagnetic background to study new methods with varying configurations, to analyze such methods, to determine the optimal cases for specific missions, to generate, visualize, and analyze test data. In fact, SCoBi is a framework that implements only the simulator for vegetated terrains (SCoBi-Veg) for now. The simulator is being open-sourced in the Matlab/Octave development environment. It takes many inputs for vegetation, antennas, ground, and preferences. It generates received field and power, reflectivity, and/or NBRCS (normalized bistatic radar crosssection) for direct, coherent (specular), and incoherent (diffuse) contributions. This paper describes the ongoing open sourcing and the capabilities of the SCoBi simulator. Index Terms— bistatic, signal of opportunity, model, simulator, open source, vegetation
SoOp approach utilizes the existing navigation and communication satellites instead of investing in the development of new transmitter satellites. It has been a novel approach to Earth sciences in global scales in the recent decades. In contrast to conventional mono-static active radars, SoOp systems have a bistatic configuration. This approach can be exploited for different geophysical parameter retrieval tasks through selection of an operating frequency with electrical length relative to the object being measured [3]. Those are quite mature for ocean missions such as wind estimation. For example, CYGNSS (Cyclone Global Navigation Satellite System) is a space mission of NASA’s that measures ocean wind vectors via this methodology [4]. However, SoOp land applications still require considerable effort to be validated for usage with confidence. In a developing area of research such as SoOp, simulators are essential for a detailed, comprehensive analysis of a given problem. Additionally, simulators are vital for the difficult task of decomposing a system into discrete components as well as producing data products through routine, repeatable algorithms that work on every variable of interest for a particular purpose. In other words, a sufficiently accurate and flexible simulator can allow researchers and engineers to perform well-rounded premission analysis in advance of the launch of a real system. Under this motivation, we have developed a coherent SoOp forward model that works for vegetation under a bistatic configuration between a pair of transmitter and receiver antennas that operate in the microwave spectrum (from P to S-band). It takes into account the antenna characteristics such as orientation, polarization mismatch/crosstalk, virtual or homogenous vegetation, and ground surface characteristics. There exist few simulators that aim to cope with bistatic SoOp configuration and these simulators can be grouped into two main classes with respect to the kind of bistatic model that they employ: Simulators with either incoherent or coherent bistatic scattering models. While the former group uses models that are based on radiative transfer theory and only provides amplitude information, the latter group works
with models that are based on analytical wave theory and gives both amplitude and phase information. One example of the first category is Bi-MIMICS (Bistatic- Michigan Microwave Canopy Scattering) for forest biomass estimation with linear polarization at X, C, and L-bands [5]. The others in the same category use the Tor Vergata model for biomass monitoring with circular or linear polarization at various frequency range [6][7]. SAVERS (The Soil And VEgetation Reflection Simulator), which is also based on Tor Vergata, for GNSS (Global Navigation Satellite System) reflections from bare and vegetated soil [8]. There are also few simulators with coherent bistatic models. MPSIM (MultiPath SIMulator) is a GPS (Global Positioning System) multipath simulator in this category that was developed in Matlab/Octave environment. It produces signal-to-noise ratio, carrier phase, and code pseudorange outputs at GPS L1 and L2 frequencies based on a forward model for nearsurface reflectometry [9]. WAVPY (Waveform simulation in Python) differs in that it is not only a simulator, but also an open-source GNSS-R (GNSS-Reflectometry) software library for separate simulation purposes [10]. In other words, it provides many classes that can be individually employed for different tasks or combined together. In addition, there is a model with the name COBISMO (COherent BIstatic Scattering MOdel) and it is focused on forest canopy scattering coefficient analysis with linear polarization at Pband [11]. Although it was utilized for simulations, there is no known public simulator that employs COBISMO. Despite the fact that the SCoBi-Veg model is in the same class with COBISMO and has similar features, SCoBi-Veg has a much larger set of capabilities. This is because of SCoBi-Veg’s flexibility to account for a myriad of affects and system variables such as full polarimetric analysis with combinations of linear and circular polarizations, antenna orientation, beamwidth, side-lobe effects, cross polarization coupling, beam divergence, and interferometric effects. The intent of open-sourcing SCoBi simulator is (1) to turn it into a professional, generic, extendible framework that can be utilized by researchers, scientists, and end-users in several SoOp fields, and (2) to take the initial step for potential academic collaborations. The next parts of the paper are organized as follows: Section 2 describes the overall design and significant modules of the simulator, Section 3 explains the current usage and mentions the potential extensions that the simulator framework enables, and Section 4 concludes the study. 2. DEVELOPMENT OF THE SCOBI-VEG The SCoBi simulator has been developed in the Matlab environment and will be compatible with Octave. The components of the main framework and simulation engine are all implemented with functional programming (FP) principles that Matlab mainly supports; however, object oriented programming (OOP) design and implementation principles are also employed within the simulator as needed
for data encapsulation, manipulation, and code organization purposes. For instance, calculating the antenna voltage pattern is simply handled by a function implementation while the antenna parameters that should be taken as inputs and maintained as it is initialized throughout the simulation are handled under a class with singleton pattern features. The main actions from beginning to the end of a full SCoBi-Veg simulation that includes reception of all inputs and the creation of all output products in the preferences is shown in an activity diagram, which is a significant UML (Unified Modelling Language) diagram to describe dynamic aspects of a software program, in Fig. 1. It is worth noting here that Monte Carlo simulations are performed for vegetation scattering over the generated realizations. 2.1. Design The SCoBi simulator is a software program that combines both FP and OOP implementations; however, the design is described with the help of a component model diagram in Fig. 2 as if it is a pure OOP project for ease of explanation. The simulator is currently implemented to get input parameters from xml files and this process will be handled via a GUI (Graphical user interface) component in the future. It can currently simulate scattering through two main vegetation types: homogenous and virtual vegetation. Homogenous vegetation requires average vegetation parameter values such as density, dimension, and dielectric constant as inputs and calculates the propagation and scattering through a homogenous canopy that is generated from these input values. Virtual vegetation is more realistic since it enables the generation of every individual plant within vegetated fields, and the framework allows for the use of plugin-like, custom plant generator functions out of the simulator. 2.2. Insight into Modules For clarification, the term “module” is used in this paper to describe distinct set of functions, classes, and/or packages that handle a specific kind of task in the simulator. The Simulation Engine starts and manages the flow of the simulation until final products are generated. It initially calls Input Handler to read input files, determines the simulation type regarding the vegetation type, then uses the other modules as needed in the sequence. Geometry Module is responsible for configuring the bistatic geometry that consists of the transmitter and the receiver. Instrument Module provides calculations that depend on the antenna characteristics. Monte-Carlo Module generates realizations of vegetation covers on the ground for Monte-Carlo simulations and computes propagation and scattering effects through the canopy. It also realizes the antenna pattern and rotations. Products Module is responsible for calculating the resulting products of the system that are field and power outputs from direct, specular, and diffuse terms. It also manages storing the products to the output directories.
Fig. 2: Component model of the SCoBi-Veg simulator
3. USAGE OF THE SCOBI-VEG The term “usage” in fact refers to two different phenomena in this part: First, it describes the way of running the simulator, second, it discusses the potential benefits of SCoBi simulator and future work in the SCoBi system. To run the simulator, there should be separate xml input files for simulation, satellite (transmitter), receiver, vegetation, and ground, where the directories for these files, semantics of each parameter in each file. Sample input files will be provided in the simulator user manuals in the open-source distribution. Starting the simulation is simply by calling the starter function which will also be described in the manuals. The SCoBi simulator calculates phase and amplitude information under a large number of input parameters to realistically model bistatic configuration, antenna characteristics and orientation, and scene statistics, which are described earlier. To illustrate, received power results for bare soil and virtually generated corn crop fields from five different stages are depicted in Fig. 3, where specular term obviously dominates. These results were obtained from a bistatic GNSS-R configuration, where the main parameter values were as follows: (1) transmitter is a GPS satellite operating with right hand circular polarization (RHCP) at L1band with 40° observation angle with EIRP (Equivalent Isotropically Radiated Power) of 27 dB, (2) receiver is a ground-based antenna with LHCP, zero-gain (actual gain adds an offset to the received power), a generalized Gaussian pattern with half-power beamwidth of 30°, and cross polarization level of 25 dB at 20 m-altitude, and (3) ground conditions are represented by 0.10 cm3/cm3 volumetric soil moisture (VSM) and 1.0 cm root mean square (rms) height roughness. As seen in Fig. 3, the simulator distribution will have plotting features for at least main products that are bare soil output, coherent and incoherent terms of the received power output. Furthermore reflectivity and effective Fig. 1: Activity diagram of the SCoBi-Veg simulator
provides multiple analysis techniques such as decomposition of received power into contributing terms, and acquisition of reflectivity and/or normalized bistatic radar cross section. It can be directly run by an end-user only for the purpose of generating the products, or it can be expanded by a researcher by implementing additional functions to handle particular objectives such as studying on a new vegetation campaign. The future work after preliminary distribution of this simulator will be SCoBi-MLS, where multi-layer soil studies will be conducted. Moreover, it will play a framework role for potential extensions like ScoBi-Snow. In other words, it will be advancing with predefined plans, and it can be expanded by the community for specific purposes. 5. REFERENCES
Fig. 3: An example output plot of SCoBi-Veg simulator.
normalized bistatic radar cross section (NBRCS) can be plotted per altering inputs to the system such as observation angles, VSM, or surface rms height. The SCoBi-Veg simulator can be applied for different platforms under different frequencies ranging from P to Sbands, varying altitudes, and different polarizations. It also allows to perform simulations on selected vegetation covers or bare soil. From this perspective, SCoBi is a comprehensive framework for microwave SoOp analysis through validation for space missions. Although the current study presents implementation of SCoBi-Veg model, in fact, the framework is designed as SCoBi system, and very distinct extensions such as SCoBi-Snow, SCoBi-Forest, SCoBi-MixedVeg, SCoBi-Topography, SCoBi-MLS (multilayer soil) can be made in the future. 4. CONCLUSION SoOp generally proposes a new inexpensive way of remote sensing of objects. Although the ocean observations via SoOp have been quite mature, land applications still require more effort to maturate. This effort may include a range of approaches from theoretical studies to experimental analysis. Simulations can play an intermediate role in these studies. To create a sufficiently accurate simulator, a correct bistatic model should be developed first, which is often an output of exhaustive theoretical derivations. A reliable simulator can allow for comprehensive analysis before experimental observations. The introduced SCoBi simulator under open-sourcing process runs a SoOp bistatic vegetation scattering model, which is the product of a long-term theoretical work. This simulator is sensitive to large number of altering parameters. Thanks to this sensitivity, it contributes to SoOp studies by satisfying the need for a comprehensive coherent bistatic vegetation system. It can be employed as a testbed that
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