Figure 1: Diagram of cis-lunar space, which includes the real GPS sidelobe data collected on an HEO space vehicle. (All figures provided by the authors)

As part of NASA’s increased interest in returning to the moon, the ability to acquire accurate, onboard navigation solutions will be indispensable for autonomous operations in cis-lunar space (see Figure 1). Artemis I recently made its weeks-long journey to the Moon, and spacecraft carrying components of the Lunar Gateway and Human Landing System are planned to follow suit. During launch and within the GNSS space service volume, space vehicles can depend on the robust navigation signals transmitted by GNSS constellations (GPS, GLONASS, BeiDou, and Galileo). However, beyond this region, NASA’s Deep Space Network (DSN) serves as the system to track and guide lunar spacecraft through the dark regions of cis-lunar space. Increasingly, development of a lunar navigation satellite system (LNSS) that relies on a low size, weight and power (SWaP) “smallSat” constellation is being discussed for various possible orbits such as low lunar orbit (LLO), near rectilinear halo orbit (NRHO) and elliptical frozen orbit (ELFO).

Figure 2: DPE 3D (left) and 2D (right) spatial correlogram shown on a 3D north-east grid.

We have implemented direct positioning estimation (or collective detection) techniques to make the most of the limited and weak GPS signals (see Figure 2) that have been employed in other GNSS-degraded environments such as urban canyons. The algorithm used in conventional GNSS positioning employs a two-step method. In the first step, the receiver acquires signals to get a coarse estimate of the received signal’s phase offset. In the second step, the receiver tracks the signals using a delay lock loop coupled with a phase or frequency lock loop. The second step enables the receiver to get fine measurements, ultimately used to obtain a navigation solution. In the scenario addressed in our work, where a vehicle is navigating beyond the GPS satellite constellation, the signals are weak and sparse, and a conventional GPS receiver may not be able to acquire or maintain a lock on a satellite’s sidelobe signals to form a position solution. For a well-parameterized region of interest (that is, having a priori knowledge of the vehicle orbital state through dynamic filtering), and if the user’s clock error is known within a microsecond, a direct positioning estimator (DPE) can be used to improve acquisition sensitivity and obtain better position solutions. DPE works by incorporating code/carrier tracking loops and navigation solutions into a single step. It uses a priori information about the GPS satellites, user location, and clocks to directly estimate a position solution from the received signal. The delay-Doppler correlograms are first computed individually for the satellites and are then mapped onto a grid of possible candidate locations to produce a multi-dimensional spatial correlogram. By combining all signals using a cost function to determine the spatial location with the most correlation between satellites, the user position can be determined. As mentioned, signals received beyond the constellation will be sparse and weak, which makes DPE a desirable positioning method.

BACKGROUND

The proposed techniques draw from several studies exploring the use of weak signals and provide a groundwork for developing robust direct positioning methods for navigating beyond the constellation. NASA has supported and conducted several of the studies in developing further research into the use of signals in this space.

A study done by Kar-Ming Cheung and his colleagues at the Jet Propulsion Laboratory propagates the orbits of satellites in GPS, Galileo, and GLONASS constellations, and simulates the “weak GPS” real-time positioning and timing performances at lunar distance. The authors simulated an NRHO lunar vehicle based on the assumption that the lunar vehicle is in view of a GNSS satellite as long as it falls within the 40-degree beamwidth of the satellite’s antenna. The authors also simulate the 3D positioning performance as a function of the satellites’ ephemeris and pseudorange errors. Preliminary results showed that the lunar vehicle can see five to 13 satellites and achieve a 3D positioning error (one-sigma) of 200 to 300 meters based on reasonable ephemeris and pseudorange error assumptions. The authors also considered using relative positioning to mitigate the GNSS satellites’ ephemeris biases. Our work differs from this study in several key ways, including using real data collected beyond the GNSS constellations and investigating the method of direct positioning estimation for sparse signals.

Luke Winternitz and colleagues at the Goddard Space Flight Center described and predicted the performance of a conceptual autonomous GPS-based navigation system for NASA’s planned Lunar Gateway. The system was based on the flight-proven Magnetospheric Multiscale (MMS) GPS navigation system augmented with an Earth-pointed high-gain antenna, and optionally, an atomic clock. The authors used high-fidelity simulations calibrated against MMS flight data, making use of GPS transmitter patterns from the GPS Antenna Characterization Experiment project to predict the system’s performance in the Gateway NRHO. The results indicated that GPS can provide an autonomous, real-time navigation capability with comparable, or superior, performance to a ground-based DSN approach using eight hours of tracking data per day.

In direct positioning or collective detection research, Penina Axelrad and her colleagues at the University of Colorado at Boulder and the Charles Stark Draper Laboratory explored the use of GPS for autonomous orbit determination in geostationary orbit (GEO). They developed a novel approach for directly detecting and estimating the position of a GEO satellite using a very short duration GPS observation period that had been presented and demonstrated using a hardware simulator, radio-frequency sampling receiver, and MATLAB processing.

Ultimately, these studies and more have directed our research in exploring novel methods for navigating beyond the constellation space.

DATA COLLECTION

The data we used was collected as part of the U.S. Air Force Academy-sponsored Falcon Gold experiment and the data was also post-processed by analysts from the Aerospace Corporation. A few of the key notions behind the design of the experiment was to place an emphasis on off-the-shelf hardware components. The antenna used on board the spacecraft was a 2-inch patch antenna and the power source was a group of 30 NiMH batteries. To save power, the spacecraft collected 40-millisecond snapshots of data and only took data every five minutes. The GPS L1 frequency was down-converted to a 308.88 kHz intermediate frequency and was sampled at a low rate of 2 MHz (below the Nyquist rate) and the samples were only 1- bit wide. Again, the processing was designed to minimize power requirements.

METHODS AND SIMULATIONS

To test our techniques, we used real data collected from the Falcon Gold experiment on a launch vehicle upper stage (we’ll call it the Falcon Gold satellite) which collected data above the constellation on a HEO orbit. The data collected was sparse, and the signals were weak. However, the correlation process has shown that the collected data contained satellite pseudorandom noise codes (PRNs). Through preliminary investigation, we find that the acquired Doppler frequency offset matches the predicted orbit of the satellite when propagated forward from an initial state. The predicted orbit of the satellite was derived from the orbital parameters estimated using a batch least-squares fit of range-rate measurements using Aerospace Corporation’s TRACE orbit-determination software. The propagation method uses a Dormand-Prince eighth-order integration method with a 70-degree, first-order spherical harmonic gravity model and accounting for the gravitation of the Moon and Sun. The specifics of this investigation are detailed below.

Figure 3: GPS constellation “birdcage” (grey tracks), with regions of visibility near the GPS antenna boresight in blue and green for the given line-of-sight from the Falcon Gold satellite along its orbit (orange).

The positions of the GPS satellites are calculated using broadcast messages (combined into so-called BRDC files) and International GNSS Service (IGS) precise orbit data products (SP3 files). GPS satellites broadcast signals containing their orbit details and timing information with respect to an atomic clock. Legacy GPS signals broadcast messages contain 15 ephemeris parameters, with new parameters provided every two hours. The IGS supports a global network of more than 500 ground stations, whose data is used to precisely determine the orbit (position and velocity in an Earth-based coordinate system) and clock corrections for each GNSS satellite. These satellite positions, along with the one calculated for the Falcon Gold satellite, allowed for the simulation of visibility conditions. In other words, by determining points along the Falcon Gold satellite trajectory, we determine whether the vehicle will be within the 50° beamwidth of a GPS satellite that is not blocked by Earth.

Figure 3 shows a plot rendering of the visibility conditions of the Falcon Gold satellite at a location along its orbit to the GPS satellite tracks. Figure 4 depicts three of the 12 segments where signals were detected and compares the predicted visibility to the satellites that were actually detected. A GPS satellite is predicted to be visible to the Falcon Gold satellite if the direct line-of-sight (DLOS) is not occluded by Earth and if the DLOS is within 25° of the GPS antenna boresight (see Figure 5).

Figure 4: Predicted visibility of direct line-of-sight to each GPS satellite where a blue line indicates the PRN is predicted to be visible but undetected. A green line is predicted to be visible and was detected, and a red line indicates that the satellite is predicted to not be visible, but was still detected.

Figure 5: Depiction of the regions of a GPS orbit where the Falcon Gold satellite could potentially detect GPS signals based on visibility.

As a preliminary step to evaluate the Falcon Gold data, we analyzed the Doppler shifts that were detected at 12 locations along the Falcon Gold trajectory above the constellation. By comparing the Doppler frequency shifts detected to the ones predicted by calculating the rate of change of the range between the GPS satellites and modeled Falcon Gold satellite, we calculated the range rate root-mean-square error (RMSE). Through this analysis, we were able to verify the locations on the predicted trajectory that closely matched the detected Doppler shifts.

These results are used to direct our investigations to regions of the dataset to parameterize our orbit track in a way to effectively search our delay and Doppler correlograms to populate our spatial correlograms within the DPE. Figure 6 shows the time history of the difference of predicted range rates on the trajectory and the detected range rates on the trajectory. That is, a constant detected range rate value is subtracted from a changing range rate for the duration of the trajectory and not just at the location on the trajectory at the detect time (dashed vertical line). From this we can see that the TRACE method gives range rates near the detected ranges at the approximate detection time for the 12 different segments.

Figure 6: Plots depicting the 12 segments of detection and the corresponding time history of differences of range-rate values for each GPS PRN detected. The time history is of the range-rate difference between the predicted range rate from the TRACE-estimated trajectory and the constant detected range rate at the detection time (vertical line).

Excluding Segment 12, which was below the MEO constellation altitude, Segment 6 has more detected range rates than that of the other segments. On closer inspection of this segment, and using IGS precise orbit data products, it appears that the minimum RMSE of the range rates from the detected PRNs is off from the reported detection time by several seconds (see Figure 7). Investigating regions along the Falcon Gold TRACE-estimated trajectory and assuming a mismatch in time tagging results in a location (in Earth-centered Earth-fixed coordinates) with a lower RMSE for the predicted range rates compared to detected range rates.

Figure 7: Range-rate difference between the predicted range rate from the TRACE-estimated trajectory and the constant detected range rate at the detection time (left). A portion of the trajectory around Segment 6 with the TRACE-estimated location at the time of detection (red) and the location with the minimum RMSE of range rate (black).

To determine the search space for the DPE, we first determine the location along the original TRACE-estimated trajectory with the minimum RMSE of range rates for each segment. Then we propagate the state (position and velocity) at the minimum location to the Segment 6 time stamp. If the time segment has more than three observed range rates (Segment 6 and Segment 12), we perform a least squares velocity estimate using the range-rate measurements, using the locations along the trajectory and selecting the location with the smallest RMSE. Then, for Segment 12, the position and velocity obtained from least squares is propagated backwards in time to the Segment 6 timestamp. All of these points along the trajectory as well as the original point from the TRACE estimated trajectory are used in a way similar to the method of using a sigma point filter. Specifically, the mean and covariance of the position and velocity values are used to sample a Gaussian distribution. This distribution will serve as the first iteration of the candidate locations for DPE. There were a total of three iteration steps and at each iteration the range of clock bias values over which to search was refined from a spacing of 1,000 meters, 100 meters, and 10 meters. Also on the third iteration, the sampled Gaussian distribution was resampled with 1,000 times the covariance matrix values in the directions perpendicular to the direction to Earth. This was done to gain better insight into the GPS satellites that were contributing to the DPE solution.

RESULTS

Figure 8 shows the correlation peaks for each of the signals reported to be detected using a 15-millisecond non-coherent integration time within the DPE acquisition. Satellite PRNs 4, 16 and 19 are clearly detected. Satellite PRN 29 is less obviously detected, but the maximum correlation value is associated with the reported detected frequency. However, this is the peak detected frequency only if the Doppler search band is narrowly selected around the reported detected frequency. Similarly, while the peak code delay shows a clear acquisition peak for PRNs 4, 16 and 19, for PRN 29 the peak value for code delay is more ambiguous with many peaks of similar magnitude of correlation power. Figure 8 depicts the regions around the max peak correlation chip delay.

Figure 8: Acquisition peak in frequency (left) and time (right) for PRN 4, 16, 19 and 29. The correlograms are centered on the frequency predicted from the range rate calculated along the trajectory.

For the first iteration of DPE, the peak coordinated acquisition values for PRN 16 and PRN 4 are chosen for the solution space. From the corresponding spatial correlogram, the chosen candidate solution is roughly 44 kilometers away from the original position estimated using TRACE.
For the second iteration of DPE, the clock bias is refined to search over a 100-meter spacing. The peak values for PRN 16 and PRN 19 are chosen for the solution space and the chosen candidate solution is roughly 38 kilometers away from the original position estimated using TRACE.
For the final iteration, Figures 9 and 10 depict the solutions with the 10-meter clock bias spacing and the approach of spreading the search space over the dimension perpendicular to the direction of Earth. Again, this was done to illustrate how the peak correlations appear to be drawing close to a single intersection location. However, the results fall short of the type of results shown in the spatial correlogram previously depicted in Figure 2 when many satellite signals were detected.

Figure 9: Acquisition peaks plotted in the time domain with the candidate location chosen at the location of the vertical black line for the detected PRNs for the third iteration of the DPE method.

Figure 10: Spatial correlogram with the candidate location chosen at the location of the black circle for the detected PRNs for the third iteration of DPE method. The original TRACE-estimated position is indicated by a red circle. The two positions are approximately 28 kilometers apart.

A similar iterative method was followed using not just the four detected PRNs, but any satellite that was predicted to be visible with the relaxed criteria allowing for visibility based on receiving signals from the first and second sidelobes of the antenna. This is predicted using a larger 40° away from the GPS antenna boresight criterion. The final spatial correlogram (Figure 11) shows similar results to the intersections shown in Figure 10. However, there is potentially another PRN shown with a peak contribution near the original intersection point. These results are somewhat inconclusive and will need to be investigated further.

Figure 11: Spatial correlogram with the candidate location chosen at the location of the black circle for the detected PRNs for the third iteration of DPE method using additional satellites. The original TRACE-estimated position is indicated by a red circle. The two positions are approximately 24 kilometers apart.

CONCLUSIONS AND FUTURE WORK

Our research investigated the DPE approach of positioning beyond the GNSS constellations using real data. We will further investigate ways to parameterize our estimated orbit for use within a DPE algorithm in conjunction with other orbit determination techniques (such as filtering) as our results were promising but inconclusive. Some additional methods that may aid in this research include investigating the use of precise SP3 orbit files over the navigation message currently used (BRDC) within our DPE approach. Also, some additional work will need to be completed in determining the possibility of time tagging issues that could result in discrepancies and formulating additional methods related to visibility prediction that could aid in partitioning the search space. Additionally, we plan to investigate other segments where few signals were detected, but where more satellites are predicted to be visible (a better test of DPE). Finally, using full 40-millisecond data segments rather than the 15 milliseconds used to date may provide the additional signal strength needed to give more conclusive results.

ACKNOWLEDGMENTS

This article is based on the paper “Direct Positioning Estimation Beyond the Constellation Using Falcon Gold Data Collected on Highly Elliptical Orbit” presented at ION ITM 2023, the 2023 International Technical Meeting of the Institute of Navigation, Long Beach, California, January 23–26, 2023.

KIRSTEN STRANDJORD is an assistant professor in the Aerospace Engineering Department at the University of Minnesota. She received her Ph.D. in aerospace engineering sciences from the University of Colorado Boulder.

FAITH CORNISH is a graduate student in the Aerospace Engineering Department at the University of Minnesota.

<p>The post Far Out: Positioning above the GPS constellation first appeared on GPS World.</p>

Hexagon | NovAtel’s GAJT-710ML installed on a U.S. Army vehicle. Photo: U.S. Army Futures Command

We asked Dean Kemp, Ph.D., director of Marketing, Aerospace and Defense for Hexagon’s Autonomy & Positioning division, a few questions.

How do jamming and spoofing threats change?

Jamming and spoofing methods change as new interference-causing technologies become available. As such, it’s vital for us to continuously evaluate potential sources of threats and provide the highest possible level of resiliency to interference in our solutions.

Have new threats emerged in the past six weeks in connection with Russia’s invasion of Ukraine?

Evidence is emerging that electronic-warfare systems capable of high-power jamming and spoofing across wide areas are being used within Ukraine. Fortunately, there have been no known impacts on allied forces. However, knowing that the technology is in place and in use highlights the importance of assured positioning, navigation and timing (APNT) and our contribution to building resiliency in allied forces’ equipment against the potentially destabilizing effects of jamming and spoofing.

How do you define APNT?

We use APNT to describe measurements that are always accurate, available and reliable. Our anti-jamming, anti-spoofing and other resilience-building capabilities provide trusted and available PNT information at the level of accuracy requested.

When did you introduce GPS Anti-Jam Technology (GAJT)? How do you define it?

GAJT was introduced in 2011 and is our leading APNT solution. GAJT units are utilized worldwide across land, sea and air, with rapid deployment supported by commercial off-the-shelf solutions and short lead times. GAJT provides jamming protection of satellite-based navigation and precise timing receivers from intentional jamming and unintentional interference whatever your application. Product variants provide features to best support anti-jamming capabilities for the warfighter, national infrastructure, low-SWaP platforms and other mission-critical applications.

What are the key differences between the GAJT-710ML, the GAJT-710MS and the GAJT-410MS?

The GAJT-710 is designed for land vehicles (ML variant) and marine vessel platforms (MS variant) with up to six simultaneous nulls to protect against jamming signals and interference. The next generation of GAJT-710 includes jammer direction-finding and a silent mode to reduce its thermal signature. The GAJT-410 maintains the high levels of interference-rejection performance in the 710 but in a lower size, weight and power (SWaP) design, with three simultaneous nulls, for both land and marine variants. It also utilizes a single RF cable to provide clean power, data and protected GPS signal. The GAJT-410 enables APNT while also reducing the need for platform modifications or armor penetration.

The GAJT-AE extends jamming and interference protection to unmanned and autonomous applications. Using an external CRPA antenna, the GAJT-AE offers flexibility of integration into space-constrained platforms.

Is the GAJT-AE-N Anti-Jam Antenna receiver-agnostic?

We designed our GAJT product line to be receiver-agnostic and compatible with legacy and modern GNSS receivers. This flexibility results in GAJT being ideal for civil and military applications, including SAASM and M-code systems.

How does your GNSS Resilience and Integrity Technology (GRIT, launched in 2020 November) relate to your GAJT antennas?

GRIT is a firmware suite for our OEM7 receivers that expands their situational awareness and interference mitigation tools. GRIT includes our Interference Toolkit (ITK) along with spoofing detection to identify when your GNSS signal may be under threat. It also empowers the user to develop interference location algorithms through time-tagged snapshots of data samples to characterize the RF environment around your operations. GRIT, alongside GAJT, forms the foundation of our APNT strategy in providing accurate and always-available PNT.

Do you have any recent contracts with the U.S. Department of Defense or the militaries of other NATO countries to supply GAJT antennas?

Our GAJT product portfolio has been sold in large quantities to military and civil organizations for many years, successfully proving itself in the field. In 2020, we achieved a milestone of more than several thousand units shipped worldwide, making it one of Hexagon | NovAtel’s more successful years.

<p>The post Keeping up with jamming, spoofing threats first appeared on GPS World.</p>

Photo: Microchip Technology

Microchip Technology Inc. is offering the new SA65 chip-scale atomic clock (CSAC), providing precise timing accuracy and stability in extreme environments. Designed for military and industrial systems, the Microchip’s SA65 CSAC features ultra-high precision and low power consumption

Advanced military platforms, ocean-bottom survey systems and remote-sensing applications all require precise timing. CSACs ensure stable and accurate timing even when GNSS time signals are unavailable, thereby helping industrial and military system designers to meet timing requirements.

Microchip’s SA65 CSAC is an embedded timing solution with improved environmental ruggedness, delivering higher performance than the previous SA.45s CSAC, including double the frequency stability over a wider temperature range and faster warm-up from cold temperatures. The SA65 has an operating temperature range of –40 to 80 °C and a storage temperature range of –55 to 105 °C. The warm-up time of two minutes at –40 °C is 33% faster than that of the SA.45s.

These performance improvements benefit designers of highly portable solutions for military applications such as assured positioning, navigation and timing (A-PNT) and C5ISR (command, control, communications, computers, cyber, intelligence, surveillance and reconnaissance). It meets precise frequency requirements of a low size, weight and power (SWaP) atomic clock. Improvements such as fast warm-up to frequency after cold start, temperature stability over a wide operating range, and frequency accuracy and stability enabling extended operation while GNSS is denied help to ensure mission success in conflict environments.

The SA65 CSAC provides precise timing for portable and battery-powered applications requiring continuous operation and holdover in GNSS-denied environments. The SA65 is form-, fit- and function-compatible with the SA.45s, which minimizes risk and redesign costs for the system developer while improving performance and environmental insensitivity.

<p>The post Microchip offers new chip-scale atomic clock for defense first appeared on GPS World.</p>

John Fisher. (Photo: Orolia)

“For fixed-pattern antennas, it’s fairly simple: RF + DC to power the antenna. Most vendors are compatible. The challenge is more for controlled radiation pattern antennas (CRPA). Power requirements vary greatly, and performance can be improved with a two-way data exchange between the CRPA and receiver, but there is no industry standard yet for this interface. An example: tilt angles from the receiver’s IMU can greatly aid beam pointing.”
John Fischer
Orolia

Ellen Hall

“Antenna selection is exceptionally critical for our military and high-precision users. The platform and environment are the primary drivers of these antenna requirements. In general, SWaP (size, weight and power) is at the forefront of all criteria. As operational plans are developed, requirements for a single or multi-element array,  element gain, and noise figure must be considered.”
Ellen Hall
Spirent Federal Systems

Members of the EAB

Tony Agresta
Nearmap

Miguel Amor
Hexagon Positioning Intelligence

Thibault Bonnevie
SBG Systems

Alison Brown
NAVSYS Corporation

Ismael Colomina
GeoNumerics

Clem Driscoll
C.J. Driscoll & Associates

John Fischer
Orolia

Ellen Hall
Spirent Federal Systems

Jules McNeff
Overlook Systems Technologies, Inc.

Terry Moore
University of Nottingham

Bradford W. Parkinson
Stanford Center for Position, Navigation and Time

Jean-Marie Sleewaegen
Septentrio

Michael Swiek
GPS Alliance

Julian Thomas
Racelogic Ltd.

Greg Turetzky
Consultant

<p>The post Editorial Advisory Board PNT Q&A: Matching receivers and antennas first appeared on GPS World.</p>

The GAJT-410ML is the next evolution of NovAtel’s battle-proven anti-jam technology. It maintains the high levels of interference rejection performance as in the larger GAJT-710ML system, but in a lower size, weight and power (SWaP) design.

Photo: NovAtel

Working alongside the GAJT-410ML, the Power Injector Data Converter (PIDCTM) provides access to the jammer status and direction-finding (DF) information. It also provides clean power and data over the same cable that delivers the protected GPS signal back to the receiver, which reduces the need for costly platform modifications. The PIDC can be supplied in either an enclosure or board and is available to license for installation into third-party equipment.

NovAtel Defence Segment Manager Dean Kemp noted, “Building on the success of our existing anti-jam portfolio, the GAJT-410ML is the first system to address the needs of smaller land-based platforms and add situational awareness capability to already high levels of mitigation performance.”

“This product offers more choices for system integrators and end users to protect against GPS denied or constrained situations and delivers on our commitment to provide assured positioning anywhere,” Kemp added.

Learn more about the GAJT-410ML anti-jam antenna or talk with NovAtel’s team of specialists at these upcoming trade shows:

• The Special Operations Forces Industry Conference (SOFIC) – May 20 – 23, 2019, Tampa, FL USA
• CANSEC – May 29 – 30, 2019, Ottawa, ON Canada
• Joint Navigation Conference (JNC) – July 8 – 11, 2019, Long Beach, CA USA
• International Defence Industry Exhibition MSPO (Canadian Pavilion) – September 3 – 6, 2019, Kielce, Poland
• Defence & Security Equipment International (DSEI) – September 10 – 13, 2019, London, UK

<p>The post NovAtel reduces size of anti-jam GAJT first appeared on GPS World.</p>

In many respects, the story of innovation in electronics has been about miniaturization: designers pack more features, functionality and performance into electronics that are smaller, lighter and more power-efficient. However, this has traditionally been applied only to a limited extent to atomic clocks, which electronic devices employ to maintain correct time if their GPS signal is lost.

Atomic clocks have significant limitations in terms of scalability and portability, so until recently the best designers could use were ovenized crystal oscillators (OCXOs), which were smaller, lighter and consumed less power than atomic clocks.

However, they were also less accurate and precise. Now, micro-atomic clocks enable addressing an entirely new range of use cases. A miniature atomic clock (MAC) is not the same clock made smaller; it’s a different clock.

Timing Quality Measurements. A clock is accurate if its time agrees with a standard such as cesium reference or GPS. A clock is precise if its interval between ticks — its frequency of oscillation — is the same as a reference clock’s interval, even if the reference clock is inaccurate.

A stern measure of precision is syntonicity, which is a measure of consistency in the occurrence of ticks within the environment. Radar requires syntonicity. To obtain a clear image of a scanned object, the receiver of the signal bounced off the object needs to know the exact instant the associated pulse was sent from the transmitter.

It’s All About SWaP. One challenge of any timing miniaturization is whether the clock’s size, weight and power (SWaP) meet the needs of a given application. For example, a cesium chip-scale atomic clock (CSAC) is the smallest sized atomic clock in the current market; see the table below. By contrast, the rubidium MAChas the lowest power consumption after the CSAC (that is, 40 times more than CSAC). Before the introduction of the MAC, the standard rubidium clock was the clock with the lowest power consumption and with similar performance.

Performance metrics of clock technologies.

Benefits of small SWaP values are easily seen. Devices that required an external power source can now operate on batteries, without a heat sink. A person or a drone can now carry devices that were stationary or required a truck.

Improvements in SWaP only matters if application requirements for accuracy and precision are also met. What happens if an application’s GPS access is lost? All clocks tend to drift once they no longer reference an external time source. This is known as aging. A key factor that affects aging is temperature. While operating in extreme environments (such as, deserts, high altitudes or under sea), the rate of timing error increases due to temperature variation; the amount of temperature-related error is called tempco.

The availability of clocks with tight specifications signifies that designers can now employ accurate and precise timing in many ways and places. However, one must specify, analyze and select the clock carefully to meet the requirements of the application. For example, replacing the OCXO with a standard rubidium clock is typically not an option because the standard rubidium clock does not fit in to the OCXO form factor. Designers may consider replacing an OCXO with a CSAC or MAC if greater portabiity and better timing accuracy and precision are the key requirements.

The choice often comes to one between the CSAC’s lower power consumption and weight versus the MAC’s superior aging performance in the event of GPS loss. The difference between the two clocks lies in how gas atoms trapped into resonance by a microwave synthesizer are excited and then interrogated, a concept known as coherent population trapping.

Applications suitable for rubidium atomic clocks (MAC) include the following.

Cellular Base Stations. Rubidium atomic clocks can meet the tight timing requirements for 4G-/LTE-base stations up to 24 hours (even longer for 3G and 4G). Moreover, rubidium’s superior aging ensures longer holdover, meaning the network can remain operational for longer even if the sync reference is lost. The MAC’s lower power consumption compared to a standard rubidium clock also contributes to a lower power and heat density overall, potentially reducing the need for external cooling while increasing the electronic reliability and reducing its size. Low tempco is also critical, considering the environments in which these stations often operate.

Radar Base Stations. Radars require highly precise synchronization between transmitter and receiver signals. MACs are increasingly replace OCXO in these applications, which also benefit from the technology’s lower power.

Applications suitable for CSACs include these.

IED Jammers. Low-power consumption is critical in dismounted intelligent electronic devices (IED) jammers, which must be small, light and battery-powered. Yet they must be precise enough to tightly synchronize and allow pre-defined time slots in the signals (known as look windows) to allow friendly communications through.

Dismounted Military Radios. Portability and precise synchronization are critical, especially given the higher bandwidth waveforms required to handle encoded video and other data-rich signals.

Tactical Unmanned Aerial Vehicles (UAVs). In addition to relying on GPS (or clock holdover) for navigation, unmanned aircraft drones also require precise timing for their encoded data-rich and video communications. They also present challenges in terms of the size, weight and power consumption of payloads.

Undersea Seismic Sensing. Differences in time measurements of acoustic pulses across sensor nodes are used to map subterranean formations such as oil deposits. In the absence of GPS under water, precise synchronization and very good aging performance are critical to harvesting reliable data during the duration of a survey deep under the ocean.

More innovation lies ahead! Low-powered SWaP-friendly atomic clocks are revolutionizing the world without compromising clock performance, enabling many mission-critical applications.

RAMKI RAMAKRISHNAN is director of product line management and business development, Clocks Business Unit, Microsemi Corporation.

<p>The post PNT Roundup: Scaling down GPS-reliant devices first appeared on GPS World.</p>

The Mayflower NavAssure 125a GPS receiver.

Mayflower was awarded a Phase III SGUE (Small GPS User Equipment) contract with the U.S. Air Force Research Laboratory sponsored by the Space and Missile Systems Center/GPS Directorate (SMC/GPSD).

Under the contract, the company will develop a small SWaP (Size, Weight, and Power) security certifiable Common GPS Module (CGM).

Mayflowers’ small SWaP GPS receiver technology will allow the Department of Defense (DoD) and its agencies to benefit from increased competition, enhanced capability and reduction in overall program costs to DoD program managers and prime contractors in upgrading their navigation systems to the modernized M-code receiver.

Mayflower’s SGUE program is aimed at the development of advanced GPS receiver technology to support future military GPS requirements.  The goal of the program is to develop a NAVWAR (Navigation Warfare) compatible CGM form factor that will support SWaP-constrained military users.

The SGUE CGM development effort will expand Mayflower’s military GPS receiver product line to include modernized NavAssure-M product offerings so that current customers will have a form-fit-function upgrade path from SAASM to MGUE.

“Mayflower is a leader in small SWaP and miniaturized military GPS receiver and anti-jam products,” said Triveni Upadhyay, Mayflower founder and CEO. “I am confident in the quality and innovation expertise of our GPS engineering team to successfully develop the SGUE CGM. The development of small SWaP MGUE form factors, enabled by SGUE CGM, will have a significant impact in the M-Code market, providing secure modernized GPS signals to the warfighters and lowering total ownership costs on many military programs.”

“The Air Force is very pleased to see innovative GPS technology developed under its SBIR Program to find commercialization opportunity in the MGUE market. Mayflower has performed well and we are confident of the SGUE program success,” said Dana Howell, Air Force Research Laboratory (AFRL) program manager.

“The AFRL/GPSD objective in the SGUE Program is to advance MGUE technology and make it affordable to the warfighter,” said Eddy Emile, chief of the Advanced Technology and International Branch, GPS Directorate. ”

The SGUE Program fits the need and will lower the cost to the user by increased competition enabled by the SGUE Program.”

According to Mayflower, the NavAssure-M MGUE receiver form factors, focused toward small SWaP GPS receiver applications, will be backward compatible to SAASM, therefore, lowering the platform integration cost and total life-cycle cost.

<p>The post Mayflower awarded defense MGUE contract for GPS receiver modernization first appeared on GPS World.</p>