# 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(a⃗, b⃗, t=None)


Source: quaternion/means.py

Return rotor R such that Rb⃗R̄ 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 RdRb 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 ∫ |RdRb - 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]RdRb-Ra|^2 dt → 2 ⟨ eᵢ * Rd * ∫ RbR̄a dt ⟩₀ where → denotes taking vᵢ→0, the symbol ⟨⟩₀ denotes taking the scalar part, and eᵢ is the unit quaternionic vector in the i direction. The only way for this quantity to be zero for each choice of i is if Rd * ∫ RbR̄a 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.