Publications of the Jet Propulsion Laboratory 1989

This bibliography describes and indexes by primary author the externally distributed technical reporting, released during 1989, that resulted from scientific and engineering work performed, or managed, by JPL. Three classes of publications are included: JPL publications in which the information is complete for a specific accomplishment; articles from the quarterly Telecommunications and Data Acquisition (TDA) Progress Report; and articles published in the open literature

Near earth objects space observatory

In this Master Thesis we begin with an introduction about Near Earth Objects (NEOs). We start with the different kind of existing NEOs, and then we will focus more on which ones can represent the biggest hazard for Earth. Thus many studies suggest irrelevant number of meteorites hit the earth each year, but actually is very hard to know number of exact hit to Earth, but for introducing some meteors are caused by pea-sized of rock, for good estimated number of meteorites per year is necessary to carefully monitoring the meteorites per day in one area and finally extrapolate this data for all area of Earth, or find meteorites fall in to the dry regions and estimate for all area of Earth some valor. However, is so hard to find exact value because of different size ranges and all procedures have errors, but the estimate value of the mass of material that falls on Earth each year rang from 37000-78000 tons [23]. Most of this mass would come from dust particles. A study done in 1996 calculated that for objects in the 10 grams to 1 kilograms size range 2900-7300 kilograms per year hit Earth, furthermore, between 36 and 166 meteorites larger than 10 grams fall to Earth per million square kilometers per year. Thus that translates to 18000 to 84000 meteorites bigger than 10 grams falls to Earth. Nowadays different space agencies of several countries have their programs to detect hazardous NEOs, but in case of many of this agencies they need extra help from amateurs astronomers. Furthermore, all of this programs represent different disadvantages such as high cost of operation, no centralized data base and work with people that are amateurs and no depending to any agencies. New systems will be proposed to detect on time, the hazardous NEOs. These new systems are an answer for the actual issues to detect NEOs on time, and issues of the main official agencies to resolve their problems with this kind of the space objects. The system where is proposed here is a system based on the constellation of the satellites in the Low Earth Orbit (LEO), equipped with a Newtonian Telescope on board. Furthermore, this system had a ground stations and centralized database, thus that all information about NEOs compiled by satellites can be used for the space agencies to detect on time hazardous NEOs. The Satellites use low cost components and they are respectable to the environment, the function of the satellites will be determined during this thesis, although the LEO present some conditions, like drag, and depending the mass of the satellites, the orbit can be free after several orbits, when the satellites burn because of contact with drag. For design and simulation of the system we use required some specific tools like Solidworks for the 3D design and Moon2.0 for the orbital simulation, and finally we propose an alternative system to put our satellites in the orbit, with a system called QuickFast

Integration of Spiking Neural Networks for Understanding Interval Timing

The ability to perceive the passage of time in the seconds-to-minutes range is a vital and ubiquitous characteristic of life. This ability allows organisms to make behavioral changes based on the temporal contingencies between stimuli and the potential rewards they predict. While the psychophysical manifestations of time perception have been well-characterized, many aspects of its underlying biology are still poorly understood. A major contributor to this is limitations of current in vivo techniques that do not allow for proper assessment of the di signaling over micro-, meso- and macroscopic spatial scales. Alternatively, the integration of biologically inspired artificial neural networks (ANNs) based on the dynamics and cyto-architecture of brain regions associated with time perception can help mitigate these limitations and, in conjunction, provide a powerful tool for progressing research in the field. To this end, this chapter aims to: (1) provide insight into the biological complexity of interval timing, (2) outline limitations in our ability to accurately assess these neural mechanisms in vivo, and (3) demonstrate potential application of ANNs for better understanding the biological underpinnings of temporal processing

Spiking Neural Networks for Detecting Satellite-Based Internet-of-Things Signals

With the rapid growth of IoT networks, ubiquitous coverage is becoming increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to provide coverage to regions where terrestrial systems cannot. However, LEO constellations for uplink communications are severely limited by the high density of user devices, which causes a high level of co-channel interference. This research presents a novel framework that utilizes spiking neural networks (SNNs) to detect IoT signals in the presence of uplink interference. The key advantage of SNNs is the extremely low power consumption relative to traditional deep learning (DL) networks. The performance of the spiking-based neural network detectors is compared against state-of-the-art DL networks and the conventional matched filter detector. Results indicate that both DL and SNN-based receivers surpass the matched filter detector in interference-heavy scenarios, owing to their capacity to effectively distinguish target signals amidst co-channel interference. Moreover, our work highlights the ultra-low power consumption of SNNs compared to other DL methods for signal detection. The strong detection performance and low power consumption of SNNs make them particularly suitable for onboard signal detection in IoT LEO satellites, especially in high interference conditions

USING COMMERCIAL 5G AND LEO TECHNOLOGIES TO ENHANCE NAVY-ARMY SENSOR-TO-SHOOTER NETWORKS

The emerging commercial technologies of 5G and low Earth orbit (LEO) satellite communications have the capability to provide links that send large amounts of data with low latency. As the DOD continues to explore how to best leverage these technologies, it is important to develop potential use cases within the military. This thesis describes a sensor-to-shooter operational scenario and the network transport links currently in use to move data from a Navy sensor to an Army shooter. The current sensor-to-shooter network transport links are then compared to the emerging commercial alternatives of 5G and LEO satellite communications in the categories of throughput, latency and range. This analysis demonstrates the comparative advantages and disadvantages of both 5G and LEO technologies over current links.Captain, United States ArmyLieutenant, United States NavyApproved for public release. Distribution is unlimited

Multispectral imaging of Mars from a lander

Multispectral imaging of Mars from lande

Interference Management and System Optimization with GNSS and non-GNSS Signals for Enhanced Navigation

In the last few decades, Global Navigation Satellite System (GNSS) has become an indispensable element in our society. Currently, GNSS is used in a wide variety of sectors and situations, some of them offering critical services, such as transportation, telecommunications, and finances. For this reason, and combined with the relative ease an attack on the GNSS wireless signals can be performed nowadays with an Software Defined Radio (SDR) transmitter, GNSS has become more and more a target of wireless attacks of diverse nature and motivations. Nowadays, anyone can buy an interference device (also known as a jammer device) for a few euros. These devices are legal to be bought in many countries, especially online. But at the same time, they are illegal to be used. These devices can interfere with signals in specific frequency bands, used for services such as GNSS. An outage in the GNSS service at a specific location area (which can be even a few km2) could end up in disastrous consequences, such as an economical loss or even putting lives at risk, since many critical services rely on GNSS for their correct functioning. Fundamentally, this thesis focuses on developing new methods and algorithms for interference management in GNSS. The main focus is on interference detection and classification, but discussions are also made about interference localization and mitigation. The detection and classification algorithms analyzed in this thesis are chosen from the point of view of the aviation domain, in which additional constraints (e.g., antenna placement, number of antennas, vibrations due to movement, etc.) need to be taken into account. The selected detection and classification methods are applied at the pre-correlation level, based on the raw received signal. They apply specific signal transforms in the digital domain (e.g., time-frequency transformations) to the received signal. With such algorithms, interferences can be detected at a level as low as 0 dB Jamming-to-Signal Ratio (JSR). The interference classification combines transformed signals with previously trained signals Convolutional Neural Network (CNN) and/or Support Vector Machine (SVM) to determine the type of interference signal among the studied ones. The accuracy of such a classification methodology is above 90%. Knowing which signal causes interference we can better optimize which mitigation and localization algorithm we should use to obtain the best mitigation results. Furthermore, this thesis also studies alternative positioning methods, starting from the premise that GNSS may not always be available and/or we are certain that we can not rely on it due to some reason such as high or unmitigated interferences. Therefore, if one needs to get a Position Velocity and Time (PVT) solution, one would have to rely on alternative signals that could offer positioning features, such as the cellular network signals (i.e. 4G, 5G, and further releases) and/or satellite positioning based on Low Earth Orbit (LEO) satellites. Those systems use presumably different frequency bands, which makes it more unlikely that they will be jammed at the same time as the GNSS signal. In this sense, positioning based on LEO satellites is studied in this thesis from the point of view of feasibility and expected performance