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```
use nalgebra::{VectorN, DimName, MatrixMN, MatrixN, RealField, Dim, allocator::Allocator, DefaultAllocator};

pub struct Filter<T, DimZ, DimX>
where
T: RealField,
DimZ: Dim + DimName,
DimX: Dim + DimName,
DefaultAllocator: Allocator<T, DimX>
+ Allocator<T, DimX, DimX>
+ Allocator<T, DimZ, DimX>
+ Allocator<T, DimZ, DimZ>
{
///state estimate vector
x: VectorN<T, DimX>,
///state covariance matrix
p: MatrixN<T, DimX>,
///system model
f: MatrixN<T, DimX>,
///measurement noise covariance matrix
r: MatrixN<T, DimZ>,
///measurement function
h: MatrixMN<T, DimZ, DimX>,
///process noise covariance matrix
q: MatrixN<T, DimX>
}

impl <T, DimZ, DimX> Filter<T, DimZ, DimX>
where
T: RealField,
DimZ: Dim + DimName,
DimX: Dim + DimName,
DefaultAllocator: Allocator<T, DimX>
+ Allocator<T, DimZ>
+ Allocator<T, DimX, DimX>
+ Allocator<T, DimX, DimZ>
+ Allocator<T, DimZ, DimX>
+ Allocator<T, DimZ, DimZ>
{
pub fn new(x: VectorN<T, DimX>, p: MatrixN<T, DimX>, f: MatrixN<T, DimX>, r: MatrixN<T, DimZ>, h: MatrixMN<T, DimZ, DimX>, q: MatrixN<T, DimX>) ->  Filter<T, DimZ, DimX>{
Filter{x, p, f, r, h, q}
}

pub fn run(&mut self, z: VectorN<T, DimZ>) -> (VectorN<T, DimX>, MatrixN<T, DimX>){
//predict
let x = &self.f * &self.x;
let p = &self.f * &self.p * &self.f.transpose() + &self.q;
//update
let s = &self.h * &p * &self.h.transpose() + &self.r;
let k = &p * &self.h.transpose() * s.try_inverse().unwrap();
let y = z - &self.h * &x;
self.x = x + &k * y;
self.p = &p - k * &self.h * &p;
(self.x.clone(), self.p.clone())
}
}
pub struct Filter2<T, DimZ, DimX, DimU>
where
T: RealField,
DimZ: Dim + DimName,
DimX: Dim + DimName,
DimU: Dim + DimName,
DefaultAllocator: Allocator<T, DimX>
+ Allocator<T, DimX, DimX>
+ Allocator<T, DimZ, DimX>
+ Allocator<T, DimZ, DimZ>
+ Allocator<T, DimX, DimU>
{
///state estimate vector
x: VectorN<T, DimX>,
///state covariance matrix
p: MatrixN<T, DimX>,
///system model
f: MatrixN<T, DimX>,
///control function
b: MatrixMN<T, DimX, DimU>,
///measurement noise covariance matrix
r: MatrixN<T, DimZ>,
///measurement function
h: MatrixMN<T, DimZ, DimX>,
///process noise covariance matrix
q: MatrixN<T, DimX>
}

impl <T, DimZ, DimX, DimU> Filter2<T, DimZ, DimX, DimU>
where
T: RealField,
DimZ: Dim + DimName,
DimX: Dim + DimName,
DimU: Dim + DimName,
DefaultAllocator: Allocator<T, DimX>
+ Allocator<T, DimZ>
+ Allocator<T, DimU>
+ Allocator<T, DimX, DimX>
+ Allocator<T, DimX, DimZ>
+ Allocator<T, DimZ, DimX>
+ Allocator<T, DimZ, DimZ>
+ Allocator<T, DimX, DimU>
{
pub fn new(x: VectorN<T, DimX>, p: MatrixN<T, DimX>, f: MatrixN<T, DimX>, b: MatrixMN<T, DimX, DimU>, r: MatrixN<T, DimZ>, h: MatrixMN<T, DimZ, DimX>, q: MatrixN<T, DimX>) ->  Filter2<T, DimZ, DimX, DimU>{
Filter2{x, p, f, b, r, h, q}
}

pub fn run(&mut self, z: VectorN<T, DimZ>, u: VectorN<T, DimU>) -> (VectorN<T, DimX>, MatrixN<T, DimX>){
//predict
let x = &self.f * &self.x + &self.b * u;
let p = &self.f * &self.p * &self.f.transpose() + &self.q;
//update
let s = &self.h * &p * &self.h.transpose() + &self.r;
let k = &p * &self.h.transpose() * s.try_inverse().unwrap();
let y = z - &self.h * &x;
self.x = x + &k * y;
self.p = &p - k * &self.h * &p;
(self.x.clone(), self.p.clone())
}
}```