displayed wind/V on a flight deck and not in GA cockpits?

[Zar_1]

Originally Posted by Double Back

Kalman filter... Learned that during my (50 Years ago, state paid ) ab initio airline pilot training, 2,5 Yr course, 250 hrs of which close to 100 hrs on twin engine jets... )...

Didn't know that the Kalman Filter was such an old tech, tbh. Just read right now that it was used in the Apollo AGC..

We've had a lot of innovations in drones nowadays, a lot of it from advanced Flight Controllers (FC's) like the PixHawk, which are super affordable and easy to procure. They pack a LOT of processing power (32-bit ARM CPU with like 2MB of Flash)

[Double Back]

The other name that played an other important role in the beginning of Inertial navigation (gyros and accelerometers mounted on "stable platforms") was Max Schuler's formula, first used in 1923, see https://en.wikipedia.org/wiki/Max_Schuler
There were some pretty smart individuals that paved the way after dead reckoning and celestial navigation proved not exactly the way to go during crossing oceans with any accuracy with fast airplanes.
No matter how long I flew large transports, be it with INS/GPS, I still wondered about seeing a W/V of 277/67 change to 278/67 or 277/68. Realising "something" measured or calculated that change, impossible for the human body to detect.
Dive into history for those interested: https://en.wikipedia.org/wiki/Inerti...igation_system

[Zar_1]

IMU/INS is such a huge enhancement to aviation safety, in terms of Situational Awareness... without it and GPS, EGPWS would probably never even be possible.

The enhancement to the situational awareness is so damn significant, especially with the kind of accuracy that modern IMU systems provide nowadays, with Ring Laser Gyros.

In drones, we use MEMS gyros, not for positioning, but for Attitude Determination, which are super cheap (just google MPU6050 gyro). The reason for their low cost is mass production, since they're widely used in phones/some game controllers.

[rigpiggy]

Originally Posted by Double Back

The W/V was extremely helpful during my airline Yrs on the B744. Especially when there was a shear to be expected, You knew it still was around the corner when you did not see the reported surface wind yet on the display. On the other hand, with crosswind during decrab, booting the rudder, the display went haywire.... Anyhow a manoeuvre that one needs to look outside, not inside.

I used to be based in NL. YYT was as I liked to call it "SPORTING"

[Luc Lion]

Originally Posted by Double Back

Kalman filter... Learned that during my (50 Years ago, state paid ) ab initio airline pilot training, 2,5 Yr course, 250 hrs of which close to 100 hrs on twin engine jets... )... Had trouble to understand it completely, it was at the time no one owned a PC, programming was for aliens only.

The idea behind Kalman filter is very simple and easy to understand, at least if you limit it to a single variable.

- The first step is about computing a new value from the previous state of the system, using physics equations.
This step is equivalent to dead reckoning; the current position is equal to the position of the previous state, plus the speed vector of the previous state multiplied by the elapsed time.

- Then this value is fitted with a gaussian probability distribution, centered on the computed value. This distribution represents the probability that the system departed from a steady evolution.
The width or variance of the gaussian may be dependent on other variables of the state (eg: dead reckoning with a slow speed is more precise than with a high speed).

- Then the measured value of the same variable is also prepared by applying a gaussian distribution on it, to model the measurement errors or noise.
For dead reckoning a horizontal position, it means taking the current GPS position and applying an error distribution using, for instance, the current GPS Horizontal Figure of Merit (HFOM).

- Then the 2 distribution are combined (in this case, multiplied) and the maximum of the resulting curve is the most probable value for the variable and it is chosen as representing the variable value for the current state.
Multiplying the 2 distributions means computing a function that describes the probability that any given "actual" value of the variable could be represented by both the computed value (dead reckoning) and the measured value.
The maximum of that function is the most probable "actual" value for the current situation.

The calculation is relatively quick (hence, its implementation in the Apollo hardware) because the product of the Probability Density Functions (PDF) of 2 Gaussian variables is another PDF function, with the mean and variance equal to:
μ = (μ1 σ2² + μ2 σ1²) / (σ2² + σ1²)
σ² = σ1² σ2² / (σ2² + σ1²)
where μ is the mean value and σ² is the variance (or σ is the standard deviation).
So there is no need to make complex calculations on the plots of these functions.

It is to be noted that the theoretical ground for the Kalman filter reasoning is the Bayes theorem, published in 1763.
Kalman filter is a special and simple case of the more general Bayes filter and is an application of a branch of statistics named after Bayes; Bayesian statistics.
Another of the Bayesian statistics applications is computing the evolution of infected populations in the context of an infectious disease outbreak and its use for monitoring and predicting COVID evolution made it known to the general public.