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4 Calculate the posterior state and error covariance According to the measurement information, the posterior state and error covariance estimate can be acquired by the following equations. ????x?k=x?k|k?1+Kk(Zk?Y?k|k?1) ????Pk=Pk|k?1?KkPykykKkT where Zk refers to the measurement sampled from the sensors at step k. Moreover, the posterior error covariance is essential for the calculation in the next step. A simulation is performed to compare the effect in a tracking case for the EKF, UKF and particle filter (PF) [34], which is also often used in tracking applications. The simulation works on a 50-point dataset from a real Panobinostat order application, and the object moves along an approximate tangent curve. There are 100 particles participating in the computation of the PF method, and measuring errors are included in the random equation. The testing results are shown in Figure 8. The UKF acquires the least root mean square error (RMSE) of these three methods in this case, and its time cost is also acceptable. If the number of particles in PF increases, the method may perform better but require more time for calculation. The UKF does not always obtain the best results but has a high probability of performing well. Owing to its relatively less adjustable parameters and computational cost, the UKF is introduced in this this website work to reduce the influence of measurement noise in the localization, particularly for a moving target. Figure 8 (a) Tracking trace of the simulation; (b) Root mean square (RMS) errors of the three methods during the simulation; (c) Time cost of the three methods during the simulation. 4.3. Localization Procedure According to the estimated position calculated by RSSD and the moving parameters obtained by sensors, the target��s movement can be tracked using simple geometrical computation. The entire target localization procedure is described in Figure 9, where at represents the acceleration of the target at time t, athr denotes a threshold for the acceleration at, nt is the time interval count for stability, nthr is the threshold for the count nt, and xt and yt are the coordinates of the target at x-axis and y-axis at time t, GUCY1B3 respectively. When at �� athr, the target is considered to be moving, and its position is estimated by Equation (20) according to its position at time t ? 1 and the parameters obtained by the sensors. If at