EFFICENT USE OF PARTICLE FILTER TO MEASURE THE MEASUREMENT AMBIGUITY PROBLEM IN INDOOR SPACE
Particle filter (pf) method is thebest technique for indoor localization estimation and object tracing in smart building by the assistance of active or passive RFID reader and tags and Wi-Fi far more devices are used. Which can be accustomed gather indoor user position and the densityare calculated particle filter algorithm. The most use of Pf is employed used to estimate the nonlinear vector space Here the particle are going to be measure the particle densityambiguously. Once the item is measure on a 1 vector state then it store after moves the identical object to different one vector state, it’ll store and update will make some trouble this trouble is overcome by using Sequential important sampling method (SIS).Here this paper is implement supported on sequence sampling dimension problem on this paper is updating measurement of the particle filter with resampling method. At just one occasiondetect an ambiguous dimension update is detected, the proposed method flights the measurement update at the time step and feats the measurement later when the particle distribution becomes tolerable for the dimension inform. This plan delivers a preparation to the paradox problem to get the correct current position estimate with lower covariance. Numerical imitation is presented to prove effectiveness and routine of the proposed method. Compared to other methods, like the quality particle filter, the auxiliary particle filter, the mixture particle filter, and also the receding-horizon Kalman filter, the proposed method shows better performance in terms of root-mean-square error and projected covariance. Here we define the unclear measurement update that results in increase in covariance and weight of the particles. The unknown measurement update causes larger dispersal of particles and provides a less assured approximation. So we are able to use the particle filter method in this work to get essential result instead of kalmanfilter (Kf). Kf is generally utilized in linear indoor vector space to estimate the article.