Automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation

A target tracking and automotive radar technology, applied in the field of target tracking, can solve problems such as state estimation errors

Active Publication Date: 2018-07-20
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

In the process of automotive radar target tracking, due to the nonlinearity of the measurement equation, the real-time irregular changes in the position and speed of the target t

Method used

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  • Automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation
  • Automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation
  • Automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation

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[0041] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] figure 1 It is the invention flow chart of the present method, specifically as follows:

[0043] 1. Kinematics model and observation model of vehicle radar target tracking;

[0044] Ignoring the height factor, in the x-y two-dimensional Cartesian coordinate system, define the motion state of the target to be measured next to the car body as a 6-dimensional state vector, namely Each variable of the vector represents the target lateral distance x and lateral velocity in the vehicle body coordinate system at time k respectively , lateral acceleration , longitudinal distance y, longitudinal velocity and longitudinal acceleration , the superscript T is the transpose of the matrix or vector, the same below. ;

[0045] The target informa...

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Abstract

The invention discloses an automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation. The method comprises the following stepsthat: firstly, setting a system initial value, and calculating a cubature point value in a time update stage; spreading the cubature point; estimating a one-step prediction state and an error covariance square root factor; in a measurement update stage, importing a Gauss-Newton nonlinear iteration method to carry out iteration update, and calculating the cubature point during each-time iteration;spreading the cubature point; calculating measurement estimation; calcauting the square root factor of an innovation covariance and a cross covariance matrix; calculating Kalman gain; updating a current iteration state, and estimating the square root factor of an error covariance; judging whether an iteration termination condition is achieved or not; updating a current state, and estimating the error covariance square root; and in the measurement update process, regulating the noise compensation factor to optimize state estimation. By use of the method, accuracy and stability in an automotiveradar target tracking process can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to an automotive radar target tracking method based on noise-compensated iterative square root CKF. Background technique [0002] The essence of the target tracking process of automotive radar is a nonlinear filtering problem. The vehicle radar directly measures the radial distance, azimuth and radial velocity of the target, and realizes the estimation of the horizontal and vertical distance and velocity of the target in front of the vehicle through nonlinear conversion. This tracking process is due to the Affected by noise and nonlinearity, the filter algorithm has high requirements for fastness, robustness and filtering accuracy. The current automotive radar target tracking mostly uses Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Non-linear filtering such as volumetric Kalman filter (CKF), these filtering algorithms have their own advantages and disadvan...

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Application Information

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IPC IPC(8): G06F17/50G01S13/72
CPCG01S13/72G06F30/20Y02T90/00
Inventor 熊星王彩玲荆晓远
Owner NANJING UNIV OF POSTS & TELECOMM
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