Sequential nonlinear tracking using UKF and raw range-rate measurements

Lei, Ming; Han, Chongzhao

doi:10.1109/taes.2007.357130

Cited by 45 publications

(38 citation statements)

References 13 publications

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“…The first part is to verify the tracking performance of DUCMKF algorithm proposed with the Doppler radar measurement and compare the DUCMKF algorithm with the sequential EKF (SEKF) [18], sequential UKF (SUKF) [17], and converted measurement KF (CMKF) algorithms [13] in the previous literatures. The second part is to verify the maneuvering target tracking performance and the MAs estimation performance of AIMM-CS-DUCMKF algorithm and compare it with the IMM algorithm, current statistical model based adaptive EKF (CS-AEKF) [20], and window-based least squares estimator (WLSE) [7].…”

Section: The Generation Of True Trajectory and Measurementmentioning

confidence: 99%

“…Because the range rate is a function of the target velocity size and the velocity direction, the range rate contains the target maneuvering state information. The range rate is used not only for the estimation of normal acceleration [12][13][14] and angle velocity [15,16], but also as the measurement information for target tracking [13,[17][18][19]. Because the radar cannot detect the acceleration of target, it is necessary to directly estimate the target acceleration based on the measurement.…”

Section: Introductionmentioning

confidence: 99%

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Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Liu

Zhou

2018

Mathematical Problems in Engineering

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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

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Section: The Generation Of True Trajectory and Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Liu

Zhou

2018

Mathematical Problems in Engineering

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“…CCS to PLCCS coordinate transform Using the transformation matrices, the a priori estimate, the error covariance matrix, and the radar measurements are converted to PLCCS. Since the measurement bias b P k (i) is generated by this coordinate transform procedure, the bias compensation technique is applied as shown in (11).…”

Section: Linear Target Tracking Filter In Plccsmentioning

confidence: 99%

“…Besides, these problems could be magnified when the range rate measurements are used for the automotive target tracking because the use of range rate measurements causes severe nonlinearity compared to the use of range or LOS measurements. To overcome the limitations of the EKF based automotive target tracking, the sequential filtering (SF) technique employing unscented Kalman filter (UKF) was proposed [10,11]. This approach comes from the idea that the UKF shows somewhat better estimation performance than the EKF for highly nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Linear Recursive Automotive Target Tracking Filter for Advanced Collision Warning Systems

Han

Sang

Whang

et al. 2014

Appl. Math. Inf. Sci.

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This paper proposes an improved automotive target tracking scheme using FMCW radar which is necessary for the advanced collision warning systems. Since there exist strong nonlinear relationships between the FMCW radar measurements and the target state, the target tracking and data association in dense road clutters have been recognized as a quite challenging problem. It is obvious that the use of accurate range rate measurement might be an excellent choice to improve both target tracking and clutter suppression performances. This motivates us to develop a novel linear recursive automotive target tracking filter based on the measurement conversion in the predicted line-of-sight (LOS) Cartesian coordinate system (PLCCS). Since the x axis of the PLCCS is set by the predicted LOS vector from the host to the target, if the LOS prediction error is imperceptible, the range rate can be approximated to the x component of the relative target velocity in PLCCS. Employing the PLCCS drastically reduces the complexity of the problem and allows us to solve it within the framework of linear recursive Kalman filtering. Through the simulations, the superiority of the proposed method is compared to the existing nonlinear automotive target tracking filters.

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“…However, as for processing measurements serially or in parallel, there is little mentioned in the literature pertaining to the SPKF. Duan et al [2] and later Lei and Han [13], introduced a SPKF serial processing approach for the problem of radar target tracking with range measurements. Their method was analogous to serial processing with the EKF and re-linearizing after incorporating each measurement.…”

Section: Reviewmentioning

confidence: 99%

A Serial Approach to Handling High-Dimensional Measurements in the Sigma-Point Kalman Filter

McManus¹,

Barfoot²

2011

Robotics: Science and Systems VII

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Abstract-Pose estimation is a critical skill in mobile robotics and is often accomplished using onboard sensors and a Kalman filter estimation technique. For systems to run online, computational efficiency of the filter design is crucial, especially when faced with limited computing resources. In this paper, we present a novel approach to serially process high-dimensional measurements in the Sigma-Point Kalman Filter (SPKF), in order to achieve a low computational cost that is linear is the measurement dimension. Although the concept of serially processing measurements has been around for quite some time in the context of the Extended Kalman Filter (EKF), few have considered this approach with the SPKF. At first glance, it may be tempting to apply the SPKF update step serially. However, we prove that without re-drawing sigma points, this 'naive' approach cannot guarantee the positive-definiteness of the state covariance matrix (not the case for the EKF). We then introduce a novel method for the Sigma-Point Kalman Filter to process high-dimensional, uncorrelated measurements serially that is algebraically equivalent to processing the measurements in parallel, but still achieves a computational cost linear in the measurement dimension.

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Sequential nonlinear tracking using UKF and raw range-rate measurements

Cited by 45 publications

References 13 publications

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement

Linear Recursive Automotive Target Tracking Filter for Advanced Collision Warning Systems

A Serial Approach to Handling High-Dimensional Measurements in the Sigma-Point Kalman Filter

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