# quaternion.means

## mean_rotor_in_chordal_metric

```
mean_rotor_in_chordal_metric(R, t=None)
```

Source: quaternion/means.py

Return rotor that is closest to all R in the least-squares sense
This can be done (quasi-)analytically because of the simplicity of the chordal metric function. It is assumed that the input R values all are normalized (or at least have the same norm). Note that the `t`

argument is optional. If it is present, the times are used to weight the corresponding integral. If it is not present, a simple sum is used instead (which may be slightly faster). However, because a spline is used to do this integral, the number of input points must be at least 4 (one more than the degree of the spline).

## mean_rotor_in_intrinsic_metric

```
mean_rotor_in_intrinsic_metric(R, t=None)
```

Source: quaternion/means.py

## optimal_alignment_in_Euclidean_metric

```
optimal_alignment_in_Euclidean_metric(avec, bvec, t=None)
```

Source: quaternion/means.py

Return rotor R such that R*b⃗*R̄ is as close to a⃗ as possible
As in the `optimal_alignment_in_chordal_metric`

function, the `t`

argument is optional. If it is present, the times are used to weight the corresponding integral. If it is not present, a simple sum is used instead (which may be slightly faster). The task of finding `R`

is called "Wahba's problem" https://en.wikipedia.org/wiki/Wahba%27s_problem, and has a simple solution using eigenvectors. In their book "Fundamentals of Spacecraft Attitude Determination and Control" (2014), Markley and Crassidis say that "Davenport’s method remains the best method for solving Wahba’s problem". This constructs a simple matrix from a sum over the input vectors, and extracts the optimal rotor as the dominant eigenvector (the one with the largest eigenvalue).

## optimal_alignment_in_chordal_metric

```
optimal_alignment_in_chordal_metric(Ra, Rb, t=None)
```

Source: quaternion/means.py

Return Rd such that Rd*Rb is as close to Ra as possible
This function simply encapsulates the mean rotor of Ra/Rb. As in the mean_rotor_in_chordal_metric function, the t argument is optional. If it is present, the times are used to weight the corresponding integral. If it is not present, a simple sum is used instead (which may be slightly faster).
!!! note
The idea here is to find Rd such that ∫ |Rd*Rb - Ra|^2 dt is minimized. [Note that the integrand is the distance in the chordal metric.] We can ensure that this quantity is minimized by multiplying Rd by an exponential, differentiating with respect to the argument of the exponential, and setting that argument to 0. This derivative should be 0 at the minimum. We have ∂ᵢ ∫ |exp[vᵢ]

*Rd*Rb-Ra|^2 dt → 2 ⟨ eᵢ * Rd * ∫ Rb

*R̄a dt ⟩₀ where → denotes taking vᵢ→0, the symbol ⟨⟩₀ denotes taking the scalar part, and eᵢ is the unit quaternionic vector in the*R̄a dt is itself a pure scalar. This, in turn, can only happen if either (1) the integral is 0 or (2) if Rd is proportional to the conjugate of the integral: Rd ∝ ∫ Ra*R̄b dt Now, since we want Rd to be a rotor, we simply define it to be the normalized integral.

`i`

direction. The only way for this quantity to be zero for each choice of `i`

is if Rd * ∫ Rb