Virtual Labs

a: They are always normally distributed b: They follow a Poisson distribution c: They have zero mean and are independent d: They are correlated with system state variables

What does the gain matrix G(x) in the WLS state estimation algorithm represent?

a: The inverse of the covariance matrix b: The linearization of the measurement function c: The weighting of different measurements d: The sensitivity of the state to measurement residuals

What is the purpose of using the Taylor series expansion in state estimation algorithms?

a: To linearize the non-linear measurement function b: To calculate the optimal measurement error covariance c: To minimize the state estimation error d: To derive the covariance matrix

How does a state estimation algorithm update the state vector in each iteration?

a: By adding a fixed value to the state vector b: By solving the system dynamics equation c: By using the gain matrix to correct the estimated state d: By directly measuring the system state

What does the term 'convergence' refer to in the context of state estimation?

a: When the measurement error becomes constant b: When the algorithm stops iterating due to reaching a specified threshold c: When the system reaches its optimal control state d: When the state vector is reset to zero

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