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Towfic, Zaid

Publications and source records attributed to Towfic, Zaid.

15 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Comparison of Type 2 versus Type 3 Carrier Tracking Loops under High Dynamic Signal Conditions

Virtually all deep-space missions today and in the past employed Type 2 tracking loops in their spacecraft receivers. This was acceptable since the uplink from a single ground antenna makes use of very accurate trajectory information where the radiated frequency is Doppler compensated. However, in the desire to conserve ground resources, the technique of Multiple Uplinks per Antenna (MUPA) is being considered when using a single ground antenna to uplink to a constellation of spacecraft within its beam. This single frequency technique requires the use of non-standard signal acquisition techniques since the constellation may realize different orbits and trajectories around a planetary body leading to varying Doppler offsets and dynamics at each member spacecraft. In such a case, a Type 2 tracking loop may not suffice. An earlier study discussed the various methods for acquiring signals in spacecraft receivers with uncompensated Doppler at the uplink station. In the earlier study, three methods of achieving acquisition and lock were discussed for signals with large Doppler frequency offsets as well as high dynamics; 1) a step-and-sweep signal search algorithm, 2) an FFT signal search algorithm, and, 3) on-board tuning of the signal using uploaded trajectory information or lookup tables. On-board tuning appeared a feasible solution given the trend towards utilizing software-defined radios (SDRs). In the absence of on-board tuning, a search algorithm (e.g., step-and-sweep or FFT) could be utilized in the SDR. One such consideration is in the design of the tracking loop filter, where a Type 3 loop may be more amenable to handling large uncompensated Doppler effects. This report presents an analysis of comparing performance of Type 2 versus Type 3 carrier tracking loops when acquiring and tracking signals with large Doppler dynamics.

Abraham, Douglas↗
NASA NTRS2021

A High Dynamic-Range Photon-Counting Receiver for Deep Space Optical Communication

The Deep Space Optical Communication (DSOC) project will demonstrate free-space optical communication at almost 3 AU, or 3 orders of magnitude further than any previous attempt. DSOC will utilize the 5m Palomar Hale Telescope to receive the downlink signal, which will couple the downlink light onto an optical table and into a superconducting nanowire single photon detector (SNSPD). The output of the SNSPD is digitized by the Ground Laser Receiver Signal Processing Assembly (GSPA) using a high throughput streaming time to digital converter (TDC). The GSPA is a scalable FPGA-based receiver which demodulates and decodes the DSOC downlink signal through novel signal processing algorithms implemented on Xilinx UltraScale+ FPGAs, as well as Python-based software monitor and control routines. Exploiting the unique TDC-based architecture, the GSPA supports over four orders of magnitude of downlink data rates across multiple orders of magnitude of signal and background powers. In this paper we present an overview of the hardware, firmware and software architectures to implement this system, as well as performance analysis for links ranging from near-Earth to 2.8 AU.

Srinivasan, Meera↗
NASA NTRS2020

Opportunistic Arraying

This paper discusses recent activities at JPL that are focused on extending the Opportunistic Multiple Spacecraft Per Antenna (OMSPA) concept to include arraying multiple antennas. Specifically, we explore the ability to process multiple open loop recordings associated with multiple antennas and perform the appropriate alignment and combining. We focus on using the symbol stream combining technique and provide examples of performance measurements on actual spacecraft signals for MarCO A and B as well as the Mars Express.

Johnstone, Andrew↗
NASA NTRS2020

MSL Telecom Automated Anomaly Detection

Deformation in the form of natural hazards like earthquakes, volcanic eruptions and landslides, to ecosystem disturbances, to changes in the cryosphere (measurements of polar ice caps, ice sheets and sea ice).

Bell, David↗
NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗
NASA NTRS2020

Opportunistic Arraying

This paper discusses recent activities at JPL that are focused on extending the Opportunistic Multiple Spacecraft Per Antenna (OMSPA) concept to include arraying multiple antennas. Specifically, we explore the ability to process multiple open loop recordings associated with multiple antennas and perform the appropriate alignment and combining. We focus on using the symbol stream combining technique and provide examples of performance measurements on actual spacecraft signals for MarCO A and B as well as the Mars Express.

Okino, Clayton↗
NASA NTRS2019

InSight/MarCO Opportunistic Multiple Spacecraft Per Antenna (OMSPA) demonstration

Opportunistic Multiple Spacecraft Per Antenna(OMSPA),maybe particularly suited to smallsats. In the concept for this technique, smallsats within the scheduled ground antenna beam of some other spacecraft, make opportunistic use of that spacecraft’s beam by transmitting “open-loop” to a recorder associated with the antenna. These transmissions get captured on the recorder and can be later retrieved, demodulated, and decoded so that the smallsats can recover their data – all without them having to schedule the antenna itself and compete with larger missions for antenna time. Widespread use of such a technique could lead to more efficient use of receiver antenna resources and result in a dramatic increase in downlink throughput. An opportunity to demonstrate the technique occurred in May 2018, when the Mars CubeSat One (MarCO) mission, consisting of two nanospacecraft (MarCO-A & B) launched alongside InSight, a NASA Mars lander mission.

Conner, Charles D.↗