Improved Pseudolinear Kalman Filter Algorithms for Bearings-Only Target Tracking
نویسندگان
چکیده
منابع مشابه
Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood functi...
متن کاملHybrid GA-PSO Based Tuning of Unscented Kalman Filter for Bearings Only Tracking
The basic problem in Target tracking is to estimate the trajectory of a object from noise corrupted measurements and hence becoming very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filt...
متن کاملSwarm Intelligence Based Tuning of Unscented Kalman Filter for Bearings Only Tracking
Kalman filter is a well known adaptive filtering Algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like extended Kalman filter (EKF) and Unscented Kalman filters (UKF) are used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning an UKF is the p...
متن کاملTarget Tracking Algorithm Based on Improved Unscented Kalman Filter
In order to improve the performance of target tracking and solve the defects of unscented Kalman filter, a target tracking algorithm based on improved unscented Kalman filter is proposed in this paper. Firstly, the fading factor is introduced into the filter based on strong tracking filter to avoid the filter divergence, and then wavelet transform is used to estimate the statistical characteris...
متن کاملAuxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking
Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a G...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2017
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2017.2749207