The ago-old Interrogation/Response method for air surveillance was aptly summarized in an important 1996 GPSWorld article by Garth van Sickle: Response from an unidentified IFF transponder is useful only to the interrogator that triggered it. That author, who served as Arabian Gulf Battle Force operations officer during Desert Storm, described transponders flooding the air with signals. Hundreds of interrogations per minute in that crowded environment produced a glut of r-f energy – but still no adequate friendly air assessment.
The first step toward solving that problem is a no-brainer: Allocate a brief transmit duration to every participant, each separate from all others. Replace the Interrogation/Response approach with spontaneous transmissions. Immediately, then, one user’s information is no longer everyone else’s interference; quite the opposite: each participant can receive every other participant’s transmissions. In the limit (with no interrogations at all), literally hundreds of participants could be accommodated. Garble nonexistent. Bingo.
Sometimes there a catch to an improvement that dramatic. Fortunately that isn’t true of this one. A successful demo was performed at Logan Airport – using existing transponders with accepted data formatting (extended squitter), in the early 1990s, by Lincoln Labs. I then (first in January 1998) made two presentations, one for military operation (publication #60- click here) and another one for commercial aviation (publication #61-click here), advocating adoption of that method with one important change. Transmitting GPS pseudoranges rather than coordinates would enable an enormous increase in performance. Reasons include cancellation of major errors – which happens when two users subtract scalar measurements from the same satellite, but not coordinates formed from different sets of satellites. That, however, only begins to describe the benefit of using measurements (publication #66); continue below:
With each participant receiving every other participant’s transmissions, each has the ability to track all others. That is easily done because
(1) every extended squitter message includes unique source identification, and (2) multiple trackers maintained in tandem have been feasible for years; hundredsof tracks would not tax today’s computing capability at all. Tracks can be formed by ad hoc stitching together coordinate differences, but accuracy will not be impressive. A Kalman tracker fed by those coordinate differences would not only contain the uncancelled errors just noted, but nonuniform sensitivities, unequal accuracies, and cross-axis correlations among the coordinate pseudomeasurement errors would not be taken into account. Furthermore, the dynamics (velocity and acceleration) – as derivatives – would degrade even more – and dynamic accuracy is absolutely crucial for ability to anticipate near-future position (e.g., for collision avoidance).
The sheer weight of all the considerations just noted should be more than enough to motivate the industry towards preparing to exploit this capability. But, wait – there’s more. Much more, in fact. For how many years have we been talking about consolidating various systems, so that we wouldn’t need so many different ones? Well, here’s a chance to provide both 2-dimensional (runway incursion) and 3-dimensional (in-air) collision avoidance with the same system. The performance benefits alone are substantial but that plan would also overcome a fundamental limitation for each –
* Ground: ASDE won’t be available at smaller airports
* In-air: TCAS doesn’t provide adequate bearing information; conflict resolution is performed with climb/dive.
The latter item doesn’t make passengers happy, especially since that absence of timely and accurate azimuth information prompts some unnecessary “just-in-case” maneuvers.
No criticism is aimed here toward the designers of TCAS; they made use of what was available to them, pre-GPS. Today we have not just GPS but differential GPS. Double differencing, which revolutionized surveying two decades ago, could do the same for this 2-D and 3-D tracking. The only difference would be absence of any requirement for a stationary reference. All positions and velocities are relative – exactly what the doctor ordered for this application.
OK, I promised – not just more but MUCH more. Now consider what happens when there aren’t enough satellites instantaneously available to provide a full position fix meeting all demands (geometry, integrity validation): Partial data that cannot provide instantaneous position to be transmitted is wasted (no place to go). But ancient mariners used partial information centuries ago. If we’re willing to do that ourselves, I’ve shown a rigorously derived but easily used means to validate each separate measurement according to individual circumstances. A specific satellite might give an acceptable measurement to one user but a multipath-degraded measurement to another. At each instant of time, any user could choose to reject some data without being forced to reject it all. My methods are applicable for any frequency from any constellation (GPS, GLONASS, GALILEO, COMPASS, QZSS, … ).
While we’re at it, once we open our minds to sharing and comparing scalar observations, we can go beyond satellite data and include whatever our sensors provide. Since for a half-century we’ve known how to account for all the nonuniform sensitivities, unequal accuracies, and cross-axis correlations previously mentioned, all incoming data common to multiple participants (TOA, DME, etc.) would be welcome.
So we can derive accurate cross-range as well as along-range relative dynamics as well as position, with altitude significantly improved to boot. Many scenarios (those with appreciable crossing geometry) will allow conflict resolution in a horizontal plane via deceleration – well ahead of time rather than requiring a sudden maneuver. GPS and Mode-S require no breakthrough in inventions, and track algorithms already in public domain carry no proprietary claims. Obviously, all this aircraft-to-aircraft tracking (with participants in air or on the ground) can be accomplished without data transmitted from any ground station. All these benefits can be had just by using Mode-S squitter messages with the right content.
There’s still more. Suppose one participant uses a different datum than the others. Admittedly that’s unlikely but, for prevention of a calamity, we need to err on the side of caution; “unlikely” isn’t good enough. With each participant operating in his own world-view, comparing scalar measurements would be safe in any coordinate reference. Comparing vectors with an unknown mismatch in the reference frame, though, would be a prescription for disaster. Finally, in Chapter 9 of GNSS Aided Navigation & Tracking I extend the approach to enable sharing observations of nonparticipants.
In the About panel of this site I pledged to substantiate a claim of dramatic improvements afforded by methods to be presented. This operation is submitted as one example satisfying that claim. Many would agree (and many have agreed) that the combined reasons given for the above plan is compelling. Despite that, there is no commitment by the industry to pursue it. ADSB is moving inexorably in a direction that was set years ago. That’s a reality – but it isn’t the only reality. The world has its own model; it doesn’t depend on how we characterize it. It’s up to us to pattern our plans in conformance to the real world, not the other way around. Given the stakes I feel compelled to advocate moving forward with a pilot program of modest size – call it “Post-Nextgen” – having the robustness to recover from severe adversity. Let’s get prepared.
In 2013 a phone presentation was arranged, for me to talk for an hour with a couple dozen engineers at Raytheon. The original plan was to scrutinize the many facets and ramifications of timing in avionics. The scope expanded about halfway through, to include topics of interest to any participant. I was gratified when others raised issues that have been of major concern to me for years (in some cases, even decades). Receiving a reminder from another professional, that I’m not alone in these concerns, prompts me to reiterate at least some aspects of the ongoing struggle — but this time citing a recent report of flight test verification.
The breadth of the struggle is breathtaking. The About panel of this site offers short summaries, all confirmed by authoritative sources cited therein, describing the impact on each of four areas (satnav + air safety + DoD + workforce preparation). Shortcomings in all four areas are made more severe by continuation of outdated methods, as unnecessary as they are fundamental, Not everyone wants to hear this but it’s self-evident: conformance to custom — using decades-old design concepts (e.g., TCAS) plus procedures (e.g., position reports) and conventions (e.g., interface standards — guarantees outmoded legacy systems. Again, while my writings on this site and elsewhere — advocating a different direction — go back decades, I’m clearly not alone (e.g., recall those authoritative sources just noted). Changing more minds, a few at a time, can eventually lead to correction of shortcomings in operation.
We’re not pondering minor improvements, but dramatic ones. To realize them, don’t communicate with massaged data; put raw data on the interface. Communicate in terms of measurements, not coordinates — that’s how DGPS became stunningly successful. Even while using all the best available protection against interference, (including anti-spoof capability), follow through and maximize your design for robustness; expect occurrences of poor GDOP &/or less than a full set of SVs instantaneously visible. Often that occurrence doesn’t really constitute loss of satnav; when it’s accompanied by history of 1-sec changes in carrier phase, those high-accuracy measurements prevent buildup of position error. With 1-sec carrier phase changes coming in, the dynamics don’t veer toward any one consistent direction; only location veers during position data deficiencies (poor GDOP &/or incomplete fixes) and, even then, only within limits allowed by that continued accurate dynamic updating. Integrity checks also continue throughout.
So then, take into account the crucial importance of precise dynamic information when a full position fix isn’t instantaneously available. Take what’s there and stop discarding it. Redefine requirements to enable what ancient mariners did suboptimally for many centuries — and we’ve done optimally for over a half-century. Covariances combined with monitored residuals can indicate quality in real time. Aircraft separation means maintaining a stipulated relative distance between them, irrespective of their absolute positions and errors in their absolute positions. None of this is either mysterious or proprietary, and none of this imposes demands for huge budgets or scientific breakthroughs — not even corrections from ground stations.
A compelling case arises from cumulative weight of all these considerations. Parts of the industry have begun to address it. Ohio University has done flight testing (mentioned in the opening paragraph here) that validates the concepts just summarized. Other investigations are likely to result from recent testing of ADSB. No claim is intended that all questions have been answered, but — clearly — enough has been raised to warrant a dialogue with those making decisions affecting the long term.
In February of this year the navigation community lost a major contributor to navigation — John Bortz. To many his name is best known in connection with “the Bortz equation” which easily deserves a note here to highlight its significance in development of strapdown inertial nav. Before his work in the early 1970s, strapdown was widely considered as something with possible promise “maybe, if only it could ever come out of the lab-&-theory realm” and into operation. Technological capabilities we take for granted today were far less advanced then; among the many state-of-the-art limitations of that time, processing speed is a glaringly obvious example. To make a long story short, John Bortz made it all happen anyway. Applying the previously mentioned equation (outgrowth of an early investigation of Draper Lab’s Dr. J.H. Laning) was only part of his achievement. Working with 1960s hardware and those old computers, he made a historic mark in the annals of strapdown. Still, importance of that accomplishment should not obscure his other credentials. For example, he also made significant contributions to radio navigation — and he spent the lst two decades of his life as a deacon.
A comment challenged my video . I’m glad it included an acknowledgment that some points might have been missed. To be frank that happened a bunch; bear with me while I explain. First, there’s the accuracy issue; doppler &/or deltarange info provided from many receivers is far less accurate than carrier phase (sometimes due to cutting corners in implementation — recall that carrier phase, as the integral of doppler, will be smoother if processing is done carefully). Next, preference for 20-msec intervals will backfire badly. If phase noise at L-band gives a respectable 7mm = 0.7cm, doppler velocity error [(current phase) – (previous phase)] / 1 sec is (1.414) (0.7) = 1 cm/sec RMS for a 1-sec sequential differencing interval. Now use 20 msec: FIFTY times as much doppler error! Alternatively if division is implicit instead of overt, degradation is more complicated: sequential phase differences are highly correlated (with a correlation coefficient of -1/2, to be precise). That’s because the difference (current phase) – (previous phase) and the difference (next phase) – (current phase) both contain the common value of current phase. In a modern estimation algorithm, observations with sequentially correlated errors are far more difficult to process optimally. That topic is a very deep one; Section 5.6 and Addendum 5.B of my 2007 book address it thoroughly. I’m not expecting everyone to go through all that but, to offer fortification for its credibility, let me cite a few items:
* agreement from other designers who abandoned efforts to use short intervals
* table near the bottom of a page on this site.
* phase residual plots from Chapter 8 of my 2007 book.
The latter two, it is recalled, came from flight test for an extended duration (until flight recorder was full), under severe test aircraft (DC-3) vibration.
For doppler updating from sources other than satnav, my point is stronger still. Doppler from radar (which lacks the advantage of passive operation) won’t get velocity error much below a meter/sec — and even that is an improvement over unaided inertial nav (we won’t see INS velocity specs expressed in cm/sec within our lifetime).
Additional advantages of what the video offers include (1) no requirement for a mask angle (2) GNSS interoperability, and (3) robustness. A brief explanation:
(1) Virtually the whole world discards all measurements from low-elevation satellites because of propagation errors. But ionospheric and tropospheric effects change very little over a second; 1-sec phase differences are great for velocity information. Furthermore they offer a major geometry advantage while occurrence of multipath would stick out like a sore thumb, easily edited out.
(2) 1-sec differences from various constellations are much easier to mix than the phases themselves.
(3) For receivers exploiting FFT capability even short fragments of data, not sufficiently continuous for conventional mechanizations (track loops), are made available for discrete updates.
The whole “big picture” is a major improvement is robust operation
The challenger isn’t the only one who missed these points; much of our industry, in fact, is missing the boat in crucial areas. Again I understand skepticism, but consider the “conventional wisdom” regarding ADSB: Velocity errors expressed in meters per second — you can hear speculative values as high as ten. GRADE SCHOOL ARITHMETIC shows how scary that is; collision avoidance extrapolates ahead. Consider the vast error volume resulting from doing that 90 seconds ahead of closest approach time with several meters per second of velocity error. So — rely on see-and-avoid? There are beaucoup videos that show how futile that is (and many more videos that show how often near misses occur — in addition there are about a thousand runway incursions each year). That justifies the effort for dramatic reduction of errors in tracking dynamics — to cm/sec relative velocity accuracy.
It’s perfectly logical for people to question my claims if they seem too good to be true. All I ask is follow through, with visits to URLs cited here.
James L Farrell launches his new Kindle book – GPS Made Simple
James L Farrell, author of GNSS Aided Navigation & Tracking & Integrated Aircraft Navigation has just launched his new book on the Amazon platform Kindle. The book is now available for Android tablets, iPads and of course Kindle Fire. The book sells for $3.99 and available in the Kindle Store.
A LinkedIn discussion centered on the Future Airborne Capability Environment (FACE) standard contained an important observation concerning certification. Granted — requirements for validation, with acceptance by governing agencies, definitely are essential for safety. What follows here is advocacy for a proposed way to realize the common avionics benefits offered by FACE while retaining (and in fact, improving) the process of certification. Reasoning is based on three major items:
* CHANGE. In many respects this has necessitated improved standards.
* HISTORY. Spectacular failures in what we have now are widely documented.
* COST. The status quo is (and, for a long time, has been) unaffordable.
In regard to the first item: the pace of change in so many areas (hardware, software, operating systems, data communication, etc., etc., etc.) — and the effects on procurement cycles — are well known. How can certification remain unchanged when nothing else does? That argument would be undercut if the process had a rock solid track record — but that theme would not be supported by the second item — history:
Myriad shortcomings of existing operational systems are so pervasive that no one is considered a “loose cannon” for openly discussing them. Any of my horror stories — too strange and too numerous to be revisited here — would be trumped anyway by a document from the government itself. GAO-08-467SP, in 2008, described outlandish cost overruns, schedule delays, and deficient technical performance in the defense industry. That 3-way combination speaks for itself. Now a significant addition: the certification process has not been at all immune to serious flaws. The first-ever certified GPS receiver is now well known to have failed spectacularly in multiple facets of integrity testing by another manufacturer. It is readily acknowledged that correction of those early problems is quite credible, but one issue is inescapable: Historical proof of flightworthiness improperly bestowed — with proprietary rights accepted for algorithms and tests –- happened, and that was not widely known until much later.
There is still more, including integrity failure probability limits missed by orders-of-magnitude in certified GPS receivers, severe limitations of GO/NO-GO testing, and failed attempts to gain approval to set requirements for correcting those plus other deficiencies. For brevity here, those issues are covered by citing the fifth page from another related reference.
The final item is, after years of fruitless talk about cost reduction, being acknowledged — we can’t do what we’ve been doing any more. With dollars being the ultimate driver of so many decisions, we might finally see the necessary break from ingrained habits. FACE already addresses the issues and the requisite justifications. To make it all happen, two essential ingredients are
* raw-data-across-the-board, and
* nonproprietary software, with standardization under government control.
Flight-validated algorithms already in existence can be converted (e.g., from proof-of-concept to in-flight real-time form) according to government specification, by small groups more interested in engineering than in dollars (yes, that does exist). The payoff in cost savings can be huge.
Significant momentum is evolving toward a role for Open System Architecture (OSA) applied to radar. My observations in connection with that, voiced in a short LinkedIn discussion, seem worth repeating here.
One step could add major impact to this development: Rather than position (or relative position) outputs, provide measured range, azimuth, elevation (doppler could optionally be added if applicable) on the output interface. That simple step would vastly improve effectiveness of track file maintenance. Before attempting to describe all reasons for improved performance, two obvious benefits can be identified first:
* ability to use partial information (e.g., range-only or, for passive operation, angle-only)
* proper weighting of data for updating track state estimates.
The first item is self-evident. The second arises from common-sense attachment of greater value to the most accurate information. An explanation:
One-sigma error ellipsoids for individual radar fixes are not spherical (not a beachball shape but more like a flattened beachball), even at close range. At longer distances the shape progresses from a frisbee to a pancake to a DVD. Kalman filtering has enabled us to capitalize on that feature for over a half-century. Without exploiting it, we effectively treat separate radar-derived “coordinates” by intersecting volumes in space that are common to overlapping spheres. Resulting uncertainty volume is enormously larger than it should be.
The feature just noted shows up dramatically when mixing data among multiple platforms. Consider cooperative engagement whereby participants, all tracking each other via network-transmitted GPS observations, share radar observations from an unknown non-participant. Share measurements or coordinates? No contest; multiple lines crossing from different directions can offer best (i.e., along-range) accuracies applicable in 3-D.
That fact (i.e., combining data from different sensors and different platforms further dramatizes available improvements) doesn’t diminish the basic issue; even with a time history of data from only one radar, a designer with direct measurements available — instead of, not in addition to, coordinates — can provide incomparably superior performance.
“Send Measurements not Coordinates” (1999; #66 from the “Published Articles” panel, opening with eight rock-solid reasons) was aimed at GPS rather than radar. Many of the principles are the same when mixing data with information from other platforms — and from other sensors such as GPS. There is no reason, in fact, why data from satellite navigation and radar can’t be combined in the same estimation algorithm. That practice hasn’t evolved but the historical separation of operations (e.g., navigation and surveillance), arising from old equipment limitations, should no longer be a constraint. Moreover, with focus shifted from tracking to navigation, integration with additional (e.g., inertial) data offers still more reasons for using direct measurements. Rather than loose integration, superior benefits are widely known to result as the sophistication progresses forward (tight. ultratight, and deep integration).
Further elaborations on “casting off our old habits” appear from different perspectives in various blogs, one-pagers, and a few manuscripts available at this site. If your library has a copy of GNSS Aided Navigation & Tracking pages 203-4 show a way to implement the cooperative sharing of radar data obtained from a non-participant, among participants tracking each other via the mutual surveillance and tracking approach defined earlier in that same chapter.
Because so many operational systems (in fact, a vast majority) use reports in the form of coordinates, reiteration is warranted. The central issue is the content, not the amount, of data. Rather than coordinates, provide accurately time-stamped direct measurements with links connected to whichever platform observed the data (e.g., for satnav — pseudoranges; for radar — range, azimuth, elevation). Those links are automatically attached when Mode-S extended squiter (e.g., chosen for ADSB) is the means for conveying the data. For message content, strictly disallow “massaging the data beyond the light of day” (e.g., by unknown processes, with uncertain timing, … ) which invariably results in enormous loss of performance in common occurrence today.
Free-inertial navigation uses accelerometers and gyros alone, unaided. For that purpose pioneers of yesteryear developed a variety of techniques, ranging from a 2-sample approach (NASA TND-5384, 1969) by Jordan to his and various others’ higher-order algorithms to reduce errors from noncommutativity of finite rotations in the presence of coning (and/or pseudoconing). The methods showed considerable insight and produced successful operation. Since it’s always good to have “another tool in the toolbox” I’ll mention here an alternative. What I describe here isn’t being used but, with today’s processing capabilities, could finally become practical. The explanation will require some background information; I’ll try to be brief.
A very old investigation (“Performance of Strapdown Inertial Attitude Reference Systems,” AIAA Journal of Spacecraft and Rockets, Sept 1966, pp 1340-1347) used the usual small-angle representation for attitude error expressed in the vehicle frame. With that frame rotating at a rate omega the derivative of that vector therefore contains a cross product of itself crossed with omega. One contributor to that product is a lag effect from omega premultiplied by a diagonal matrix consisting of delays (e.g., transport lags equated to reciprocals of gyro bandwidths). Mismatch among those diagonal elements produces drift components with nonzero average, e.g., the x-component of the cross product is easily seen to be
aaaaaaaaaaa (difference between y and z lags) times (omega_y) times (omega_z)
Even with zero-average (e.g., oscillatory) angular rates, that product has nonzero average due to rectification. I then characterized the lags as delays from computation rather than from the gyros, with the lag differences now proportional to nonuniformities among RMS angular rate components along vehicle axes, and average products proportional to cross-correlation coefficients of the angular rate components. That was easy; I had a simple model enabling me to calculate the error due to finite gyro sampling rates producing finite rotation increments that don’t commute.
A theoretical model is only that until it is validated. I had to come up with a validation method with mid-1960s computational limitations. Solution came from a basic realization: performance doesn’t degrade from what’s happening but from belief in occurrences that aren’t happening. The first-ever report of coning (Goodman and Robinson, ASME Trans, June 1958) came from a gimballed platform that was believed to be stable while it was actually coning. If the true coning motion they described had been known and taken into account, then their high drift rates never would have occurred. The reason they weren’t taken into account then was narrow gimbal servo bandwidth; the gyros responded to the coning frequency but the platform servos didn’t. Now consider strapdown with the inverse problem: pseudoconing — a vehicle believed to experience coning when it isn’t. That will fall victim to the same departure of perception from reality. If you gave the same Goodman and Robinson coning motion to their strapdown gyro triad and sampled them every nanosecond, the effect from noncommutativity wouldn’t be noticeable.
Armed with that insight I then chose rotational dynamics with a closed form solution. Although rotations about fixed vehicle axes produced no coning, the pseudoconing was severe, with the apparent (reported-from-gyros) rotation axis changing radically within fractions of a millisecond; too fast for the 10 kHz data rate used in that computation. The cross product formulation was then validated by making extensive sets of runs, always comparing two time histories:
* a closed form solution for a true direction cosine matrix corresponding to a vehicle experiencing a sinusoidal omega
* an apparent direction cosine matrix, obtained by brute-force but meticulous formation from processing gyro outputs at finite rates with quantization, time lags, and a wide variety of error sources.
That “bull-by-the-horns” computation allowed extended runs (up to a million attitude iterations) to be made for a wide range of angular rate frequencies, axis directions, and combinations of gyro input errors (steady, random, motion-sensitive, etc.). Deviation of apparent attitude from closed-form truth was consistently in close conformance to the analytical model, for a host of error sources. I have to admit that this “bull-by-the-horns” approach gave me an advantage of finding out answers before I understood the reasons for them. The cross-product analytical model didn’t come from my vision; it came after much head-scratching with answers computed from dozens of runs. A breakthrough came from the sensitivity, completely unanticipated, to angular acceleration about gyro output axes — clear in retrospect but not initially. After these experiences it occurred to me: if cross-axis covariances were known, the dominant contributor to errors — including noncommutativity — could be counteracted. I noted that on page 1342 of that old AIAA paper.
Finally I can describe the alternative means of compensating the dominant computational error. Description begins with the reason why it would be useful. Earlier I mentioned that many authors developed very good algorithms to reduce errors from noncommutativity of finite rotations in the presence of coning and/or pseudoconing. All that history, plus more detailed presentation of everything discussed here, can be found in Chapters 3 and 4 of my 1976 book plus Addendum 7.A of my 2007 book. A supreme irony upstages much of the work from those brilliant authors: without accounting for gyro frequency response characteristics, the intended benefit can be lost — or the “compensation” can even become counterproductive (Mark and Tazartes, AIAA Journal of Guidance, Control, & Dynamics, Jul-Aug 2006, pp 641-647). As if those burdens weren’t enough, the adjustment’s complexity — as shown in that paper — can be extensive. So : that motivates usage of a simpler procedure.
By now I’ve put so much explanation into preparing its description that not much more is needed to define the method. Today’s signal-processing boards enable the requisite covariances to be repetitively computed. Then just form the vector cross product already described and subtract the result from the gyro increments ahead of attitude updating. So much for coning and pseudoconing — but I’m not quite finished yet. The paper just cited leads to another consideration: even if we successfully removed all of the error theoretically arising from inexact computation, significant improvement in free-inertial performance would require more. Operation in the presence of vibrations would necessitate reduction of other motion-sensitive errors. Gyro degradations from rotations, for example, would have to be compensated — and that includes a multitude of components. For that topic you can begin with the discussion of gyro mounting misalignment following that up with the tables in Chapter 4 of my 1976 book and Addendum 4.B of my 2007 book.
Now that a few years have passed since the LORAN-C budget was killed, it might be a good time to revisit that decision. Unlike other decisions, this one might conceivably be undone; there hasn’t been the widespread demolition of resources (e.g., towers, transmitters) followed by restoration of sites. Something else, though, did occur: recent success achieved by cooperative effort between the Coast Guard and UrsaNav Inc.
For brevity here it suffices to make a few surface-scratching notes. The vast majority of us in the navigation community recognized the potential benefit of LORAN (and an extended form eLORAN) as a crucial backup — at extremely low cost — to be used when GPS is unavailable. Many of us, furthermore, anxiously pressed for sanity (e.g., my “2-cents worth” written, to no avail, in 2009).
What’s different now, conceivably, is a combined effect of multiple factors:
* The USCG/UrsaNav success surpassed goals that had been stated earlier.
* Awareness of GPS vulnerability (therefore need for backup) has increased.
* Delay in follow-through (site restoration) offers the chance for a remedy.
An utterance appearing in Coordinates Magazine’s March 2012 cover story was reached from a different context, but its importance prompted me to cite it in the April 2012 cover story — and to repeat it here: “Do we really need to wait for a catastrophe before taking action against GNSS vulnerabilities?”
Once again I’m adding my voice to the chorus of those speaking out before it’s too late.