### TABLE I EXTENDED KALMAN FILTER ALGORITHM

2003

Cited by 5

### Table 5 - Extended Kalman Filter PERCENTAGE BIAS

1996

"... In PAGE 18: ... To evaluate the magnitude of this bias, we performed a Monte Carlo experiment on both the (TS) and the (FDS) speci cations. Table5 collects the main results about mean, median percentage bias and its empirical S.D.... ..."

Cited by 2

### TABLE VI SUMMARY OF THE DISCRETE EXTENDED KALMAN FILTER

### TABLE IV SUMMARY OF THE CONTINUOUS-DISCRETE EXTENDED KALMAN FILTER

### Table 3.1: Algorithm of the extended Kalman filter.

2007

### Table 8: Description of terms used in the Multiple Hypothesis Kalman filter algorithm

2006

"... In PAGE 25: ... Thus, when the sensors view an area where the tracked object is expected, but no readings are found, the process noise increases drastically to reflect the notion that the object has moved. The specific notation for these algorithms is described in Table8 . The propagation algorithm for our disjoint Multiple Hypothesis tracker is shown Table 9, and the sensor update algorithm is shown in Table 10.... ..."

### Table 8: Description of terms used in the Multiple Hypothesis Kalman filter algorithm

2006

"... In PAGE 26: ... Thus, when the sensors view an area where the tracked object is expected, but no readings are found, the process noise increases drastically to reflect the notion that the object has moved. The specific notation for these algorithms is described in Table8 . The propagation algorithm for our disjoint Multiple Hypothesis tracker is shown Table 9, and the sensor update algorithm is shown in Table 10.... ..."

### Table 7.2: Comparison of road tracking results between particle filters and extended Kalman filter.

### TABLE II EXTENDED KALMAN FILTER EQUATIONS Jacobian of system evolution with respect to the state vector S(t)

### Table 1 Extended Kalman fllter.

"... In PAGE 7: ...are assumed additive, uncorrelated white and Gaussian processes, with zero mean [9,17]; V and W are the relevant time-invariant covariance matrices. Starting at t0 from an a-priori estimation ^ x0 of the state vector, endowed with the relevant covariance matrix P 0, the EKF updates the current estimates of state and model parameters according to Table1 . Here E[2] denotes the expected value of the Gaussian random variable 2 and F i is the gradient, or Jacobian of the mapping (15).... In PAGE 17: ...Sensitivity analysis We report here the details of the computation of the gradient F i of the evo- lution equations necessary in the EKF approach (see Table1 ). To simplify the notation, unlike in Section 3 in what follows the current estimates are not denoted by the hat.... In PAGE 18: ... In fact, according to Eq. (15) and Table1 , # is updated by the EKF only at the end of the time step. In (A.... ..."