ADVANCED OPTIMIZATION TECHNIQUES FOR UAS COMMAND AND CONTROL (C2) COMMUNICATIONS TERRESTRIAL INFRASTRUCTURE Dr. Erton Boci UAS Terrestrial Infrastructure

Agenda Background of C2 UAS feasibility study Radio Site Layout Design Objective Coverage Prediction Model Selection Availability of Radio Station Sites along the Pipeline Optimization of site selection using Linear Programming Optimization Results Conclusion and future work

Background of C2 UAS feasibility study In early 2015, Harris Corporation. and the University of Alaska Fairbanks (UAF) conducted a feasibility study to assess the use of C-Band terrestrial based C2 communications as enabler of low altitude unmanned aircraft system (UAS) operations along the 800 mile length of the Trans- Alaska Pipeline System (TAPS) The Technical Assessment of Infrastructure Requirements consisted of a detailed study to select an optimum solution of terrestrial radio stations that would meet the required C2 link coverage for the Alyeska Pipeline use case

Radio Site Layout Design Objective For the terrestrial infrastructure selection process, we followed a well-established Service Volume (SV) design approach SV engineering effort is the selection of radio sites and their configuration to provide the RF coverage required in offering the service(s) in predefined 3-dimensional airspaces The site selection process includes Selection and adaptation of an RF coverage model Model-based solution architecting for optimal radio station siting Complex geospatial/RF design model development and design sharing Flight campaign for model verification / risk reduction For this feasibility study, we focused on the first two steps of our overall site selection process

Coverage Prediction Model Selection Generally, coverage prediction models can be grouped in the following three major categories Empirical Statistical Deterministic Viewshed (Radio Line-Of-Sight) Field-Integral (CRC-Predict, Air-predict models) Ray-Tracing Deterministic prediction models are selected for further evaluation because they achieve a high level of accuracy at the expense of longer simulation time

Coverage Prediction Model Selection (2) Viewshed coverage model was selected for this study because: It is simplest to set up and implement It provides excellent first order coverage estimation for most situations (cases A and D) Case Possible coverage combinations Coverage Prediction Accuracy Simple Setup Complex RF Setup Very Complex RF Setup LOS ? RF Link Closed ? Viewshed CRC-Predict Ray-Tracing A1 Yes Excellent Good B2 No Very poor Fair C3 D1 1 - These cases are coverage conditions most likely encountered along the pipeline 2 - Even without radio LOS, RF link closes because of diffraction 3 - Even with radio LOS, RF link does not close, possibly because of multipath

Sample Coverage Analysis (100ft AGL) Terrain Viewshed CRC-Predict Case A Case B Case C Case D

Pipeline Terrain Environment Available terrain data for analysis USGS high resolution terrain (75m) STRM high resolution terrain (90m) – [Selected] Constraints Minimum prediction altitude 100 ft AGL Prediction models considered Viewshed model – [Selected] CRC Predict model Ray Tracing Model

Availability of Radio Station Sites along the Pipeline Pump Stations (12 sites) Assume installation of 75ft tower structures at each of the pump station locations Existing VHF radio sites (24 sites) Assume co-location with existing VHF radio system infrastructure SBSS ADS-B (8 sites) Assume co-location with SBSS ADS-B sites FCC listed sites (141 sites) Assume sites/structures identified in the FAA database are available for site installation

Example PS04 to PS05 Viewshed Analysis Combined viewshed analysis using eight sites along PS-04 to PS-05 section of pipeline No predicted low altitude visibility for many sections of the pipeline primarily due to terrain and lack of infrastructure Google Earth views and viewshed analysis show agreement

Radio Site Optimization Approach An initial set of 57 sites was identified as the most suitable for providing best coverage along pipeline Give priority to sites with the closest proximity to the pipeline and with some infrastructure, especially an existing tower. Coverage Problem Optimization Statement Minimize the number of sites while maintaining the same coverage provided by the initial set of selected sites for the flight altitudes of interest Coverage Problem Solution Leverage Linear Programming techniques in search of the optimum problem solution

Radio Site Linear Programming Formulation 𝑗=1 𝑛 𝑐 𝑗 𝑥 𝑗 Objective function: Minimize subjet to where 𝑛 - number of the initial set of selected radio sites 𝑚 - number of 3D points in space for coverage evaluation 𝑥 𝑗 - the 𝑗𝑡ℎ site 0 if the site is not required and 1 if the site is required 𝑐 𝑗 - the cost associated with the deployment of 𝑗𝑡ℎ site 𝑙𝑜𝑠 𝑖𝑗 - the predicted line-of-site (LOS) from 𝑗𝑡ℎ site to the desired 3D 𝑖𝑡ℎ point in space with allowed values of 0 (no LOS) is predicted and 1 if LOS is predicted 𝑏 𝑖 - required LOS coverage at 𝑖𝑡ℎ 3D point in space with allowed values of 0 if no LOS is required, 1 if single site LOS coverage is required, … and k if k- redundant LOS coverage is required 𝑗=1 𝑛 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 𝑚 𝑥 𝑗 ∈ 0,1 𝑗=1,…,𝑛

Radio Site Optimization Example Assume our goal is to provide non-redundant LOS coverage to ten 3D points in space [ 𝑝 1 , 𝑝 2 , …, 𝑝 10 ] Three radio sites [ 𝑥 1 , 𝑥 2 , 𝑥 3 ] selected as the initial set Cost to acquire each of these sites is the same [ 𝑐 1 = 𝑐 2 = 𝑐 3 ] Coverage requirements (3D points) LOS Analysis 3D Lat Lon Altitude (ft AGL) x1 x2 x3 p1 70.15672 -155.51921 200 1 p2 70.14214 -155.54836 500 p3 70.11991 -155.56645 1000 p4 70.10591 -155.56191 1500 p5 70.08723 -155.55106 2000 p6 70.08220 -155.54922 2500 p7 70.08221 -155.54998 p8 70.07924 -155.54721 p9 70.05276 -155.53389 p10 70.01891 -155.51523 Linear Programming Formulation 𝑗=1 3 𝑥 𝑗 Minimize subject to 𝑗=1 3 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 10 𝑥 𝑗 ∈ 0,1 𝑗=1,2,3

Linear Programing “LP Solve” software LP Solve is a linear programing solver written in C and available on both Linux and Windows It solves linear programming, mixed-integer programming (MIP), and semi-continuous and special ordered sets problems LP Formulation using LPSolve software LPSolve Solution Based on the LPSolve solution, only two sites, 𝑥 1 𝑎𝑛𝑑 𝑥 3 , are needed to achieve the RLOS coverage provided by the initial set of three sites 𝑥 1 , 𝑥 2 , 𝑥 3 .

Pipeline LP Optimization Parameters Total of 18,295 sample points were used along the pipeline with an average sample distance of 185m Out of ~300 sites along the pipeline, we selected 57 best candidate sites based on the proximity of the site to the pipeline and removal of sites in clustered areas The altitude floor of the SV along the pipeline was selected to be 100ft AGL with the ceiling set to 3000ft AGL. Coverage predictions were generated per site per 100ft of altitude increase Linear Programming Formulation per altitude 𝑗=1 57 𝑥 𝑗 Minimize 𝑗=1 57 𝑙𝑜𝑠 𝑖𝑗 𝑥 𝑗 ≥ 𝑏 𝑖 𝑖=1,…, 18925 subject to 𝑥 𝑗 ∈ 0,1 𝑗=1,…,57

Data Storage and Processing Viewshed predictions for each pipeline geo sample location were stored in MongoDB database MongoDB is a flexible and scalable NoSQL document-oriented database MongoDB Map-Reduce was used to query and generate the LP Solve source file Sample record stored in MongoDB Pipeline sample point location Radio site information Predicted complex viewshed value representing the height the point should be raised or lowered to make it just visible from the radio station location

Pipeline Radio Site Optimization Results Optimized number of sites given the 57 sites as the initial set

Sensitivity Analysis Percentage C2 System Coverage Provided by each Site over a Range of Altitudes of Operational Interest

Conclusion We described the use of linear programming optimization techniques, as the bases of our technical assessment and infrastructure requirements derivation for the C-Band terrestrial based C2 communications The site optimization results were used to evaluate various optimal and near-optimal infrastructure solutions Future enhancements Account for coverage scenarios that require redundant coverage so to improve the overall performance of the operational system Account for site acquisition cost and formulate the LP problem so to minimize the overall cost of the proposed solution Use RF predictions instead of viewshed predictions