Converted Measurement Sigma Point Kalman Filter for Bistatic Sonar and Radar Tracking

Bordonaro, Steven V.; Willett, Peter; Bar‐Shalom, Yaakov; Luginbuhl, Tod

doi:10.1109/taes.2018.2849179

Cited by 35 publications

(26 citation statements)

References 9 publications

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“…where and are the converted measurement errors in positions and pseudo measurement; the predicted error variances , , and can be obtained by Jacobian transformation on the predicted error covariance; the predicted values , , and can be obtained according to the predicted state. In addition, the position converted measurement error covariance can be obtained by (19).…”

Section: Dusq Filtering Based On Bluementioning

confidence: 99%

“…The sequential filtering process is similar with the conventional one, including the decorrelation between position and pseudo measurements and EKF. The difference is that the converted measurement error covariance matrix is replaced by (19), and the remaining items in are calculated by (25)- (28). Finally, DUSQ filtering based on the BLUE algorithm is obtained.…”

Section: Dusq Filtering Based On Bluementioning

confidence: 99%

“…An unbiased and consistent measurement conversion from range, bearing, and range rate to the Cartesian position and velocity with appropriate elimination of estimation bias was proposed in [18]. For the measurements reported in the direction cosine coordinates (COS), an effective tracking method named sequential extended Kalman filter (SEKF) using de-biased converted position and Doppler measurements in the COS (DCMSEKFcos) was proposed in [19]. To resolve the nonlinear estimation problem in the bistatic radar tracking system, the converted measurement sigma point Kalman filter (CMSPKF) [20] estimates the conversion bias and the converted measurement error covariance with a sigma point transform (SPT) using a combination of the tracker's predicted estimate and the raw measurement error covariance.…”

Section: Introductionmentioning

confidence: 99%

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De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

Peng

Cheng

2020

J. of Syst. Eng. Electron.

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Section: Dusq Filtering Based On Bluementioning

confidence: 99%

Section: Dusq Filtering Based On Bluementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

Peng

Cheng

2020

J. of Syst. Eng. Electron.

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“…Hence, the passive tracking problem will be focused on the nonlinear tracking algorithms design under the determined target model and measurement model. According to the scientific and engineering requirements of building the advanced passive underwater target tracking system, many researchers proposed their innovative tracking schemes [ 19 , 20 , 21 , 22 ]. Among all the existing underwater target tracking algorithms, the nonlinear Bayesian framework is the most common and effective approach.…”

Section: Introductionmentioning

confidence: 99%

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Hou

Zhou

Yang

et al. 2021

Entropy

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Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

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“…The purpose of extended target tracking is to simultaneously estimate the kinematic state and shape of the extended target from a sequence of noisy sensor measurements. With the development of high-resolution sensors, extended target tracking technology is becoming increasingly important for critical military and civilian applications, such as autonomous driving [1], motion and scene analysis [2], and maritime surveillance [3,4]. In contrast to point target tracking, the high-resolution sensors for extended target tracking such as X-band radar [5] provide a strongly fluctuating number of spatially distributed measurements per scan from the surface or the boundary of the extended target.…”

Section: Introductionmentioning

confidence: 99%

Tracking of Maneuvering Star-Convex Extended Target Using Modified Adaptive Extended Kalman Filter

Zhang

Chen

et al. 2020

IEEE Access

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Tracking extended targets aims to estimate the kinematic state and shape of the target of interest with a varying number of noisy detections received by a sensor. The key challenge in this problem stems from its nonlinearity and high dimensionality due to the target maneuver and model complexities. This paper presents a modified adaptive extended Kalman filter based on the random hypersurface model (RHM) to address this problem. First, the target maneuver is judged by using the input estimate (IE) chi-square detector. Then, the magnitude of the target maneuver is used to modify the prior of the shape parameters. Based on the prior information, we derive an extended Kalman filter for a closed-form recursive measurement update. The simulation and experimental results demonstrate the usefulness of the proposed method for tracking the maneuvering star-convex extended target.

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Converted Measurement Sigma Point Kalman Filter for Bistatic Sonar and Radar Tracking

Cited by 35 publications

References 9 publications

De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Tracking of Maneuvering Star-Convex Extended Target Using Modified Adaptive Extended Kalman Filter

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