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Copy file name to clipboardexpand all lines: website/docs/tech_insights/imu_noise_model.mdx
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- $x^{\text{sampled}}_k$ is the sampled value seen as a random variable.
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- $n^{\text{turn-on bias}}_{k0}$ is a random variable draw from a Gaussian when the device is turned-on (denoted here as step $k_0$).
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- $n^{\text{white noise}}_{k}$ is the time-independent noise components and draw from a 0-centered Gaussian at each step $k$.
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- The covariance of the Gaussian is usually characterized by the continuous noise $\sigma_c$ strength, which needs to be multiplied by the bandwidth (BW)h in Hz to get the distribution of the sample noise: $\sigma^2_c . BW$.
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- The covariance of the Gaussian is usually characterized by the continuous noise $\sigma_c$ strength, which needs to be multiplied by the bandwidth (BW)h in Hz to get the distribution of the sample noise: $\sigma^2_c \cdot BW$.
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- $\sigma_c$ is sometimes called *Angle Random walk in $rad/s/\sqrt{Hz}$ or $rad/\sqrt{s}$* for the gyrometer and the *Velocity Random Walk in $m/s^2/\sqrt{Hz}$ or $m/s/\sqrt(s)$* for the accelerometer.
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- $n^{\text{bias random walk}}_k$ is drawn from a random walk process.
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@@ -31,8 +31,8 @@ The parameters describing those noises can be derived from an Allan Variance plo
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