Distributions.hpp

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#pragma once
 
#include <Eigen/Core>
#include <vulcan/sensors/AllanVarianceNoise.hpp>
 
namespace vulcan::rng {

template <typename RNGType>
allan::IMUNoiseInput<double> generate_imu_noise(RNGType &rng) {
    allan::IMUNoiseInput<double> input;
    for (int i = 0; i < 3; ++i) {
        input.gyro_arw(i) = rng.gaussian();
        input.gyro_bias(i) = rng.gaussian();
        input.gyro_rrw(i) = rng.gaussian();
        input.accel_arw(i) = rng.gaussian();
        input.accel_bias(i) = rng.gaussian();
        input.accel_rrw(i) = rng.gaussian();
    }
    return input;
}

template <typename RNGType> Eigen::Vector3d generate_noise3(RNGType &rng) {
    return Eigen::Vector3d(rng.gaussian(), rng.gaussian(), rng.gaussian());
}

template <int N, typename RNGType>
Eigen::Vector<double, N> generate_noiseN(RNGType &rng) {
    Eigen::Vector<double, N> v;
    for (int i = 0; i < N; ++i) {
        v(i) = rng.gaussian();
    }
    return v;
}

template <int N, typename RNGType>
Eigen::Vector<double, N>
generate_correlated_noise(RNGType &rng, const Eigen::Matrix<double, N, N> &L) {
    Eigen::Vector<double, N> w;
    for (int i = 0; i < N; ++i) {
        w(i) = rng.gaussian();
    }
    return L * w;
}

template <typename RNGType>
Eigen::VectorXd generate_correlated_noise(RNGType &rng,
                                          const Eigen::MatrixXd &L) {
    Eigen::VectorXd w(L.rows());
    for (int i = 0; i < L.rows(); ++i) {
        w(i) = rng.gaussian();
    }
    return L * w;
}

template <typename RNGType>
void generate_turbulence_noise(RNGType &rng, double &noise_u, double &noise_v,
                               double &noise_w) {
    noise_u = rng.gaussian();
    noise_v = rng.gaussian();
    noise_w = rng.gaussian();
}
 
} // namespace vulcan::rng