Evaluation of nonlinear filtering for radar data tracking

Sese Wang,Jihong Shen,Zhuo Sun,Yanan Liu

doi:10.1186/s13638-015-0249-x

Abstract

Abstract Radar tracking plays an important role in the area of early warning and detection system, whose precision is closely connected with filtering algorithm. With the development of noise jamming technology in radar echo signal, linear filtering becomes more and more difficult to satisfy the demands of radar tracking, while nonlinear filtering can solve problems such as non-Gaussian noises. There exist a lot of nonlinear filtering algorithms at present, owning their particular characteristics. With this in mind, we provide a comprehensive overview of different nonlinear filtering algorithms in radar tracking, including basic ideas and concrete steps of them. For a more clear presentation, we also make comparisons of them from all sides. Through the analyses of different nonlinear data filters, we find that the unscented Kalman data filter (UKF) can achieve better performance than others. Therefore, we will simulate and show the performance of UKF, and performance of the extended Kalman data filter (EKF) under the same condition will be taken as comparison, whose accuracy was not ideal for radar tracking data filtering.

Highlights

Gaussian applied generalized least squares method to radar data processing in the early nineteenth century
Nonlinear filtering methods can be classified into five types [10,11]: 1) extended Kalman filtering (EKF), 2) interpolation filtering, 3) unscented Kalman filtering (UKF), 4) particle filtering, and 5) neural network filtering
5 Conclusions We present algorithm procedures of five types of nonlinear filters used in radar tracking data filtering, respectively, and have a contrast of them, among which unscented Kalman data filter (UKF) shows the best performance

Summary

Introduction

Gaussian applied generalized least squares method to radar data processing in the early nineteenth century He created a mathematical approach to deal with observations, which became the basis of the modern filter theory [1]. Tracking filter processing: Tracking filter is the core device of a radar data processing system It can estimate the state of the dynamic system using a series of measurements containing noise and other inaccuracies and predict the coordinate position and velocity of the object according to the observation sequence of the noise. Target track processing: The tracking filter should estimate the target’s motion parameters like speed and position in real time using radar measurements and calculate the position and orientation of the target in the time using the iteration formula. Where xk is the state vector of the target, zk is the observation vector, fk and hk are the state transition and the observation of the system, and uk and vk are the unrelated status noise and measurement noise of system

Nonlinear filtering in radar tracking

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Conclusions