MATA-RL: Continuous Reaction Wheel Attitude Control Using the MATA Simulation Software and Reinforcement Learning

As earth observation satellites, Diwata microsatellites need to have a high degree of target pointing accuracy. Additionally, being in low orbit, they could experience strong external disturbances. Current methods for attitude control have proven to be effective. However, they are prone to changes in control and mass parameters. In this paper, we explore using Deep Reinforcement Learning (RL) for attitude control. This paper also leverages on Diwata’s simulator, MATA: Mission, Attitude, and Telemetry Analysis (MATA) software, in training the RL agent. We implemented two RL algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). We then simulated different scenarios and compared the performance of these algorithms to that of Diwata’s current attitude controller, the Proportional-Integral-Derivative (PID) control. Our results show that reinforcement learning can outperform traditional controllers in terms of settling time, overshoot, and stability. The results of this research will help solve problems in conventional attitude controllers and enable satellite engineers to design a better Attitude Determination and Control System (ADCS)

Diwata-2: Earth Observation Microsatellite with a Compact Bus System, ElectronicallyTunable Multi-spectral Imager, and Amateur Radio Communications Capability

The microsatellite Diwata-2 was launched into the 600-km Sun-Synchronous Orbit (SSO) last October 29, 2018. It has a low-power, low-complexity, compact bus structure, capable of Earth observation and remote sensing mission through a 5-meter resolution Near-Infrared (NIR) High Precision Telescope (HPT) and a 125-meter resolution Space-borne Multispectral Imager (SMI) with two Liquid Crystal Tunable Filters (LCTF). The LCTF operates as an electronic-based band reconfiguration filter allowing for more than 600-channels of wavelength variation. As a secondary mission, Diwata-2 has full-duplex FM voice communications capability via a non-board module utilizing the amateur radio band at a 5W power requirement from mobile ground users. The structure has a 500-mm cubic external dimension, with JAXA’s Payload Attached Fairing (PAF) rocket interface and deployment mechanism. Deployable solar array panels (DSAP) were also introduced to increase the power generation capabilities of the microsatellite. The importance of detailed structural-mechanical models for finite-element analysis allowed for accurate structural simulation results. The observed accuracy is within 5-Hz for the first two modes compared to the actual vibration test results. Lastly, strict management of in-flight procedures allowed for consistent satellite performance, while modification of satellite maneuver based on imaging observation results improved target pointing accuracy to within 5-km

MATA-Cloud: A Cloud Detection and Dynamic Attitude Correction Evaluation Software

With the increasing demand for high-resolution images from earth observation satellites, there is a need to optimize the usability of the images being downloaded in the ground stations. Most captured satellite images are not usable for certain applications due to high cloud cover percentage. To address this problem, this research demonstrates a cloud detection and dynamic attitude correction evaluation software. This software explores two key experiments. First is evaluating different image processing and machine learning-based approaches to detect cloud cover. The cloud detection algorithms were evaluated based on their accuracy, latency, and memory consumption. The second is exploring dynamic attitude correction to minimize the effect of cloud cover on captured images. Results show that our software can help test algorithms that increase the usability of captured images

Growing the Local Space Workforce Through Synergistic Collaborations of the Philippine Space Agency, Universities, and Private Industry

For decades, space technology and applications development have been in the forefront of human advancement. To maximize the gains from these achievements, numerous countries have established space agencies to manage the growing space economy. However, for emerging space countries, the establishment of a space agency and a complementary space ecosystem proves to be a more complex and challenging task. In this paper, we present a review of lessons learned in building up the local upstream space workforce in the Philippines through various projects spearheaded by the government, mostly through the Philippine Space Agency (PhilSA). For the projects in collaboration with universities, this paper discusses the importance of providing training programs, scholarship opportunities, research and development activities, and promotion of current Space Science and Technology capabilities to create a young pool of knowledgeable personnel. On the other hand, collaborations with the local industry provide a support to ongoing satellite development activities in PhilSA. Established companies specializing in space-adjacent activities such as those in the manufacturing, electronics, and software development have immense potential in transitioning to actual space development activities. The paper highlights the lessons learned from PhilSA\u27s ongoing collaborations with these companies, and how such engagements translate to a more skilled space workforce. This paper summarizes the challenges faced, milestones achieved, and how the lessons learned are applied to the current activities in PhilSA and form strategic plans. These lessons learned can be helpful to other emerging space nations looking to ramp up capacity building and establish a thriving space ecosystem

Geometric filter algorithms for device-free localization using received-signal strength in wireless sensor networks

Device-free localization (DFL) is a method of determining the location of a target without requiring the target to wear a device or tag. This capability to track a device-free target is useful in applications where the target may be uncooperative and unwilling to be located and monitored. In radio frequency-based DFL systems that use received-signal strength (RSS) measurements, the changes induced by the target’s presence or motion on the RSS of the network’s links are used to infer his location. A number of RSS-based DFL algorithms have been recently proposed that can locate and track a target accurately, albeit with high computational requirements. This thesis presents new DFL algorithms that have lower computational costs while able to track a single device-free target with high accuracy. In this thesis, a new single target RSS-based DFL algorithm, referred to as the “Geometric Filter” (GF) algorithm is proposed. The GF algorithm uses simple geometric objects to represent radio links, probable target locations, and locational filters. The intersection points of line segments representing the target-affected links are used as probable locations of the device-free target. A locational filter is used to remove outlier links and points. Information about the target’s prior location and induced RSS changes are used to further refine the target location estimates. In order to perform accurate tracking in multipath-rich environments, the GF algorithm was extended further to utilize channel diversity. The “Multi-Channel Geometric Filter” (MCGF) fuses measurements of the RSS changes of each link across different frequency channels, and uses link-specific thresholds to detect the target-affected links. The measurements are then processed by a modified GF algorithm that uses estimates of the overall fade levels of intersecting links as weights to generate the target location estimates. The GF and MCGF algorithms have been evaluated using single-target tracking experiments in both indoor and outdoor environments. In these experiments, the new algorithms have been shown to outperform existing DFL algorithms in both tracking accuracy and execution time.DOCTOR OF PHILOSOPHY (EEE

Maximum likelihood estimation of ground truth for air quality monitoring using vehicular sensor networks

Various works on vehicular sensor networks (VSNs) for air quality monitoring use solid-state gas sensors due to its low cost and compact form factor. However, solid-state gas sensors have poor selectivity and are sensitive to ambient temperature and relative humidity. In addition, the sensitivity and accuracy of solid-state gas sensors degrade over time due to aging effects. Frequent recalibration of these sensors are required to maintain the accuracy of their measurements. In large VSNs, it is impractical to manually calibrate each node. Thus, calibration must be performed automatically and in-field. Assuming that the gas concentration is homogenous within an area, co-located VSN nodes can either: (1) copy measurements from a highly accurate fixed station in their immediate vicinity, or, in the absence of a fixed station, (2) collaboratively estimate the ground truth. In this work, we use maximum likelihood estimation for determining the ground truth gas concentration in an area by fusing information from co-located sensors in a VSN. Through simulations, we show that the absolute errors of the proposed method has lower mean and standard deviation as compared with existing work

A Tilt, Soil Moisture, and Pore Water Pressure Sensor System for Slope Monitoring Applications

This paper describes the design, implementation and characterization of a sensor network intended for monitoring of slope deformation and potential failures. The sensor network system consists of a tilt and moisture sensor column, a pore water pressure sensor column and a personal computer for data storage and processing. The tilt sensor column consists of several pipe segments containing tri-axial accelerometers and signal processing electronics. Each segment is joined together by flexible joints to allow for the column to deform and subsequently track underground movement. Capacitive-type sensors for soil moisture measurement are also included in the sensor column, which are used to measure the soil moisture at different depths. The measurements at each segment are transferred via a Controller Area Network (CAN) bus, where the CAN master node is located at the top of the column above ground. The CAN master node transmits the collected data from the slave nodes via a wireless connection to a personal computer that performs data storage, processing and display via a Python-based graphical user interface (GUI). The entire system was deployed and characterized on a small-scale slope model. Slope failure was induced via water seepage and the system was demonstrated to ably measure the inclination and soil moisture content throughout the landslide event

ESEP: An Experimental Onboard Computing System for Localized Capacity Building and Future Philippine Microsatellites

The Experimental Science & Engineering Payload (ESEP) platform is a bus and payload system designed to host and perform the functions of the primary components of a satellite bus system. This engineering platform is intended to act as an experimental redundant bus & payload system to the primary Diwata-2 system. This will allow testing of experimental bus and payload modules which will then be the base platform for future satellite developments in the Philippines for small satellites. The bus segment consists of the satellite command & data handling, satellite attitude control & management, and the communications subsystem. Consequently, the mission segment performs both scientific mission instrument control and data management. The platform follows the FLOWN Fly Old with New concept, designed to conform with the existing Diwata-2 modules and act as a redundant bus system for the planned Diwata-3 satellite. After establishing heritage, the flexibility of ESEP allows it to be integrated with future small satellite architectures, addressing the constantly evolving developments in satellite technology