Radar Target Tracking Error Research Using Kalman Filter-Based Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1184-1187
Author(s):  
Qian Long Chai ◽  
Yu Long Bai ◽  
Cun Hui Dong
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Substantial Effect ◽  
Specific Model ◽  
Radar Target ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Radar Systems

The methods of radar target tracking have a substantial effect on the accuracy of the whole radar systems. The basic principles and implementing steps of the Extended Kalman filter (the EKF) and the Unscented Kalman filter (the UKF) are briefly introduced. The main sources of radar observation errors and the limitation of the current methods are analyzed. According to the requirements of tracking a CV target, the EKF and the UKF are used to simulate the experiments by establishing the specific model of radar target tracking. The results show that the tracking errors can be constrained within a certain range and the whole systems also have the high tracking accuracy.

scholarly journals Adaptive fading factor unscented Kalman filter with application to target tracking

2020 ◽  
Author(s):  
Peng Gu ◽  
Zhongliang Jing ◽  
Liangbin Wu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Mismatch Error ◽  
Tracking Accuracy ◽  
Model Mismatch ◽  
Error Covariance ◽  
Nonlinear Tracking ◽  
Fading Factor

AbstractOne purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking.

Target Tracking for Robot-Fishes Based on Unscented Kalman Filter

2014 ◽  
Vol 536-537 ◽  
pp. 979-983
Author(s):  
Peng Fei Bai ◽  
Yu Lian Jiang ◽  
Rong Fang Lei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Water Waves ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Sampling Strategy ◽  
Underwater Robot ◽  
Fast Tracking ◽  
Robot Fish

Because of the nonlinear motion of the robot-fish and the uncertain water waves, it is difficult to track dynamic target accurately and quickly for the robot-fish. To solve this problem, this paper proposed the target tracking strategy of underwater robot-fish based on Unscented Kalman filter (UKF). UKF is a nonlinear filtering on the basis of unscented transformation, which consists of prediction and update recursively, thus estimate the state of the system. To avoid the particle degeneracy, this paper selects sigma points adopting scale symmetric sampling strategy. We apply UKF method to the underwater robot-fish competition in China. Simulations show that the Robot-fish has fast tracking speed, shorter tracking path and smaller tracking error using UKF method than not using the method. It has excellent tracking performance.

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

Locality-Sensitive Non-Linear Kalman Filter for Target Tracking

2021 ◽  
Vol 13 (1) ◽  
pp. 36-57
Author(s):  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Traffic Control ◽  
Unscented Kalman Filter ◽  
Research Area ◽  
Linear Filtering ◽  
Tracking Accuracy ◽  
Propagation Of Uncertainty ◽  
Non Linear ◽  
Marine Engineering

Target tracking (TT) with non-linear kalman filtering (NLKF) has recently become a very popular research area, particularly in the field of marine engineering and air traffic control. Contemporary NLKF algorithms have been very effective, in particular, with extensions and merging with a reduced root mean square error (RMSE) value. However, there are a number of issues that confront NLKF approaches, notably weakness in robustness, convergence speed, and tracking accuracy due to large initial error and weak observability. Furthermore, NLKF algorithms significantly results in error for high non-linear systems (NLS) because of the propagation of uncertainty. Again, there is a problem of estimating future states as a result of white noise. To handle these issues, the authors propose a novel non-linear filtering algorithm, called locality-sensitive NLKF (LSNLKF) that incorporates locality-sensitive adaptors into the structure of an integrated NLKF. They are the extended kalman filter (EKF) and the unscented kalman filter (UKF) for TT.

scholarly journals Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1371 ◽  
Author(s):  
Baoshuang Ge ◽  
Hai Zhang ◽  
Liuyang Jiang ◽  
Zheng Li ◽  
Maaz Butt
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Nonlinear Problems ◽  
Linear Matrix ◽  
Time Varying ◽  
Tracking Accuracy ◽  
Linear Matrix Equation ◽  
Measurement Noise Covariance ◽  
Noise Covariance

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability.

scholarly journals Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 740 ◽  
Author(s):  
Li ◽  
Zhao ◽  
Yu ◽  
Wei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Positive Definiteness ◽  
Square Root ◽  
Tracking Accuracy ◽  
Underwater Target ◽  
State Observability ◽  
Passive Measurements

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

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