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#![allow(non_snake_case)]
use nalgebra as na;
use na::{allocator::Allocator, DefaultAllocator, Dim, RealField, U1, VectorN, storage::Storage};
use crate::models::KalmanState;
use crate::noise::{CorrelatedNoise};
use crate::linalg::rcond;
impl<N: RealField, D: Dim> KalmanState<N, D>
where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>
{
pub fn predict_unscented(&mut self, f: fn(&VectorN<N, D>) -> VectorN<N, D>, noise: &CorrelatedNoise<N, D>, kappa: N) -> Result<(), &'static str>
{
let x_kappa = N::from_usize(self.x.nrows()).unwrap() + kappa;
let (mut UU, _rcond) = unscented(&self, x_kappa)?;
for c in 0..(UU.len()) {
UU[c] = f(&UU[c]);
}
kalman(self, &UU, kappa);
self.X += &noise.Q;
Ok(())
}
pub fn observe_unscented<ZD: Dim>(
&mut self,
h: fn(&VectorN<N, D>) -> VectorN<N, ZD>,
h_normalise: fn(&mut VectorN<N, ZD>, &VectorN<N, ZD>),
noise: &CorrelatedNoise<N, ZD>, s:
&VectorN<N, ZD>, kappa: N)
-> Result<(), &'static str>
where
DefaultAllocator: Allocator<N, D, ZD> + Allocator<N, ZD, ZD> + Allocator<N, U1, ZD> + Allocator<N, ZD>
{
let x_kappa = N::from_usize(self.x.nrows()).unwrap() + kappa;
let (UU, _rcond) = unscented(&self, x_kappa)?;
let usize = UU.len();
let mut ZZ: Vec<VectorN<N, ZD>> = Vec::with_capacity(usize);
ZZ.push(h(&UU[0]));
for i in 1..usize {
let mut zi = h(&UU[i]);
h_normalise(&mut zi, &ZZ[0]);
ZZ.push(zi);
}
let mut zZ = KalmanState::<N, ZD>::new_zero(s.data.shape().0);
kalman(&mut zZ, &ZZ, kappa);
for i in 0..usize {
ZZ[i] -= &zZ.x;
}
let two = N::from_u32(2).unwrap();
let x = &self.x;
let mut XZ;
{
let XX0 = &UU[0] - x;
XZ = XX0 * ZZ[0].transpose() * two * kappa;
}
for i in 1..ZZ.len() {
let XXi = (&UU[i] - x).clone_owned();
XZ += XXi * ZZ[i].transpose();
}
XZ /= two * x_kappa;
let S = zZ.X + &noise.Q;
let SI = S.clone().cholesky().ok_or("S not PD in observe")?.inverse();
let W = &XZ * SI;
self.x += &W * s;
self.X.quadform_tr(N::one().neg(), &W, &S, N::one());
Ok(())
}
}
pub fn unscented<N: RealField, D: Dim>(xX: &KalmanState<N, D>, scale: N) -> Result<(Vec<VectorN<N, D>>, N), &'static str>
where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
{
let sigma = xX.X.clone().cholesky().ok_or("unscented X not PSD")?.l() * scale.sqrt();
let mut UU: Vec<VectorN<N, D>> = Vec::with_capacity(2 * xX.x.nrows() + 1);
UU.push(xX.x.clone());
for c in 0..xX.x.nrows() {
let sigmaCol = sigma.column(c);
UU.push(&xX.x + &sigmaCol);
UU.push(&xX.x - &sigmaCol);
}
Ok((UU, rcond::rcond_symetric(&xX.X)))
}
pub fn kalman<N: RealField, D: Dim>(state: &mut KalmanState<N, D>, XX: &Vec<VectorN<N, D>>, scale: N)
where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> + Allocator<N, U1, D>
{
let two = N::from_u32(2).unwrap();
let half = N::one() / two;
let x_scale = N::from_usize((XX.len() - 1) / 2).unwrap() + scale;
state.x = &XX[0] * scale;
for i in 1..XX.len() {
state.x += XX[i].scale(half);
}
state.x /= x_scale;
{
let XX0 = &XX[0] - &state.x;
let XX0t = XX0.transpose() * two * scale;
state.X = XX0 * XX0t;
}
for i in 1..XX.len() {
let XXi = &XX[i] - &state.x;
let XXit = XXi.transpose();
state.X += XXi * XXit;
}
state.X /= two * x_scale;
}