This sounds like it couldn't possibly work (surely all the little errors compound?) but apparently it's how Apollo navigated
https://wehackthemoon.com/tech/inertial-measurement-unit-mec...
This sounds like it couldn't possibly work (surely all the little errors compound?) but apparently it's how Apollo navigated
https://wehackthemoon.com/tech/inertial-measurement-unit-mec...
Sidewinders are another example. Both developed at China Lake.
GPS can be jammed (see Russia-UKraine war), so inertial systems are still very important for rockets, for example some HIMARS rockets start with GPS and then rely only on inertial while getting close to target.
This is how the Russians have been throwing double digit percentages of launches off course.
F.ex. the 90s Tomahawk used terrain contour matching to orient itself
For more details see https://apps.dtic.mil/sti/tr/pdf/ADA315439.pdf (US translation of a mid-90s Chinese survey of the guidance space, but it covers the material and is publicly available)
Afaik, most modern systems use infrared target matching for final course correction. (Initially developed to allow anti-shipping missiles to autonomously prioritize targets, but now advanced enough to use in land scenarios as well)
It wouldn't make much sense to me, as most ATACMS warheads are area based, not point target based, so they wouldn't expect to aim at a single target. Also these systems are relatively cheap compared to things that DO have such guidance
I think anything developed before ~2005 that wasn't explicitly anti-ship doesn't have terminal guidance. Cruise missiles maybe/not.
Things after (e.g. SBD2 / StormBreaker) started to, because components were finally cheap and mature enough during the development cycle.
I remember taping two together back to back and integrating acceleration across them. That's when I learned Kalman filters. It was accurate enough so I could throw it across my desk and measure the desk length :)
https://en.wikipedia.org/wiki/Kalman_filter#History
https://github.com/chrislgarry/Apollo-11/blob/master/Luminar...
I think if I kept messing with it, it'd get a lot better, but I sorta lost interest. This was more of a fun weekend toy.
I think all phones have them, and they might be reachable through chrome/safari. And it is kinda fun to play with, but you'll probably hit sampling rate errors pretty quick. you gotta guess the shape of the curve between datapoints.
Besides inertial navigation, they had a transponder that would echo back a continuous pseudorandom bit stream, and the delay gave a precise measurement of distance.
"Optical navigation subsystem sightings of celestial bodies and landmarks on the Moon and Earth are used by the computer subsystem to determine the spacecraft's position and velocity and to establish proper alignment of the stable platform."
And Wikipedia (https://en.m.wikipedia.org/wiki/Apollo_PGNCS):
"The CM optical unit had a precision sextant (SXT) fixed to the IMU frame that could measure angles between stars and Earth or Moon landmarks or the horizon. It had two lines of sight, 28× magnification and a 1.8° field of view. The optical unit also included a low-magnification wide field of view (60°) scanning telescope (SCT) for star sightings. The optical unit could be used to determine CM position and orientation in space."
Or you can add an external correcting factor, such the Trident's astronav system that takes star-shots to recalibrate the INS.
But it's based on the same idea, only getting position as a derivative of velocity. (And some borderline-magic statistics applied.)
And that's before taking into account the absurdity of how low power the broadcast signal is.
There's a lot of math that phrase is hiding. It's not a magic black box. It just often seems that way.
Your point stands, though... the way it does work is pretty much indistinguishable from magic, from a 1970s perspective. Those guys were wizards.
It's all summing dx/dt + dy/dt + dz/dt, for i paths between satellites and ground stations (or more receivers for differential or rtk or vrs style). [2]
Which reduces most of the time to summing DELTA-Xi + DELTA-Yi + DELTA-Zi + delta-t(timeerrors). For i paths between each sat and ground receiver.
Which you should recognize the transformation if you've ever taken calculus. Even if you don't integrate every time you get a fix.
Part of what I describe as math 'magic' is that you can cancel out most of the unknowns and most of the unsolved calculus if you add a second fixed receiver.
Google and Apple location services 'cheat' and do this via subbing a nearby wifi MAC with known coordinates, which for them is good-enough. But augmented gps from FAA or DOT or coastguard etc work the same way, but with real gps receivers on the ground in realtime. Obviously without having to substitute anything.
Either way- the extra known variable greatly simplifies the math via canceling-out terms.
Plus there are both closed and open form solutions developed since initial GPS deployment that allow solving without direct integration.
Chapter 12 of [0] Surveying gets into the math, including transformations, if you want to see the math details.
Or [1] GPS by van Sickle for a good overview of the various methods/ technologies. (Also survey-centric).
[0]https://books.google.com/books/about/Surveying_theory_and_pr...
[1]https://books.google.com/books?id=J0fLBQAAQBAJ&pg=PA63&sourc...
[2] despite wgs84 and lat/lon being associated as default 'GPS coordinates', the 'raw' gps system data is xyz Cartesian in feet, then transformed to lat lon or whatever else.
Edit: this article seems to support my view that they don't start with velocities: https://insidegnss.com/wp-content/uploads/2018/01/marapr15-S... "How does a GNSS receiver estimate velocity?"
The doppler mostly comes into play with the small delta-t errors, but again, more math magic cancels most of it out in most cases, or what remains is negligible.
It's more of a signals/sync thing that gets into antenna design and (to simplify) getting all signal cycles from the various satellites working within a single aligned synced cycle, if that makes sense.
One reason the old gps units needed a long time to get an initial fix was waiting to download the broadcast, in ~bits/sec. This can now be downloaded much quicker via internet or other methods.
And there are dozens of other similar shortcuts possible depending on receiver capabilities/ connectivity/ observervation methods.
Which is to say that there's no one 'right' way to get a fix- and the 'most' correct original design was the ~hour long broadcast download. And no one does that anymore.
But just about every method (I'm aware of) is derived one way or another from the general eqns I gave above.
(But my exposure is almost entirely geodesy, engineering, and surveying, and my military (encrypted) knowledge comes from my PLS instructor being ex army intelligence, not hands on. But which is also why I am at least aware of so much of the missile tie-in issues.)
And there are signals processing and CS tricks also, which I only barely grasp.
But if something says it starts with baseline (propagating signal path) lengths to get position, it's skipping the step of how it measures/ estimates those initial baseline lengths.
Maybe this has changed or is ineffective now that smartphone/quadcopter IMUs have caught up.
They did not caught up. There are two kind of IMUs: one where you have to account for the rotation of the Earth during signal processing and one where there is no point because it will be lost in the noise anyway. The smartphone/quadcoptee IMUs are the second kind. The first kind is still export controlled.
Submarines can determine their position accurately enough, but their orientation data can be improved upon using stars.
The MIRV bus takes in the angular fix just before it starts giving the warheads their individual nudges.
Then they got a lot more accurate than .1%
So even though Russia withdrew from START II almost immediately, the US continued to unilaterally remove the MIRV capability from its ICBM fleet and stick to single warhead Minuteman IIIs.
https://thebulletin.org/2017/03/how-us-nuclear-force-moderni...
Random errors (i.e. noise) cancel out in the long run thanks to integration. You're then only left with systematic offset errors which can presumably be calibrated out to a large extent.
The random component I assume to be gaussian (thermal noise, for example) and therefore symmetrical around the real value. It's obvious we can remove this type of noise through averaging (of which the core operation is integration).
The non-random component I assume to be a skew that can be calibrated out.
With these two assumptions in mind you can see that yes, it's indeed a random walk, but a very well behaved one.
As far as calibrating out the skew, of course you can do that to some extent, but it's not a magic bullet. The Minuteman periodically measures skew and even applies equations for the change in skew with acceleration. The problem is that skew is not constant; it changes with time, changes with temperature, changes with position, and changes randomly, so you can't just calibrate it out. That's one reason why missiles use strategic-grade IMUs for a million dollars rather than a commercial-grade gyro for $10: you're getting drift of .0001º/hour instead of .1º/second.
Short-term random effects (as in, the part of the gyro's random walk error significantly higher in frequency than the inverse of the integration period) will get cancelled out by integration, assuming they're Gaussian.
Long-term random effects (mainly from time and temperature like you mentioned) will instead tend accumulate with integration aka worsening with time.
P.S. great fan of your many ventures into retro tech, keep them coming!
An ideal integrator has a response of 1/s. That's just a 1st order low-pass filter with the pole at 0. Therefore, it will filter out high frequency noise.
> Take a step to the left for heads and a step to the right for tails. Most of the time you won't end up where you started, i.e. you have residual error.
I wrote a quick simulation based on your suggestion [1]. Started by generating 1e6 random points and then applied a high-pass filter. Calculated the cumulative sum on both the original and the filtered version. TL;DR: filtered version has small and very fast variations but doesn't feature the much larger amplitude swings seen in the original.
Integration indeed does not help for those large slow swings (I'd call it drift in case of a gyroscope), but that's what I was trying to get at when I started to distinguish between short and long term random effects. What I was trying to get across originally is that "all the little errors" (which I read to mean tiny fast variations, forgetting that drift is a much bigger issue in gyroscopes) which OP mentioned get filtered/canceled out. I totally missed to explain that this will vary with frequency, which was my bad.
> if you buy a commercial-grade gyroscope for [us]$10, it will have a random walk error of several º/√h. So after summing the errors for an hour, you're left with several degrees of random error, which is bad. If you spend [us]$100,000 on a navigation-grade gyroscope, you'll get a random walk error < 0.002º/√h, which is much better.
if the slope was anything else, the unit of °/√h wouldn't make sense; it would have to be °/h or °/∛h or something. similarly for noise figures given in nanovolts/√Hz