Extended target tracking applied to X-band marine radar data

Vivone, Gemine; Braca, Paolo; Errasti-Alcala, Borja

doi:10.1109/oceans-genova.2015.7271630

Cited by 21 publications

(20 citation statements)

References 9 publications

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“…The state includes not only location, size, and posture of the target, but also the texture of the subarea. The texture is promising for improving the association in multi-target tracking [27]. Then, go to Step 1 to process the next seed cell in Cs .…”

Section: Proposed Methodsmentioning

confidence: 99%

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Yan

Zhao

et al. 2019

Sensors

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Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework.

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Section: Proposed Methodsmentioning

confidence: 99%

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Yan

Zhao

et al. 2019

Sensors

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“…The ultimately needed kinematic state estimation

normalE false[x_{k} | Z^{k} false]

{\hat{x}}_{k | k}

and the extension estimation

{\overset{boldfalse¯}{bold-italicX}}_{k | k} : = normalE false[X_{k} | Z^{k} false]

{\overset{boldfalse^}{bold-italicX}}_{k | k} / false({\overset{false^}{bold-italicv}}_{k | k} - 2 d - 2 false)

. Remark 3 To address the EOT with non‐linear measurements, our VB approach has three main advantages in comparison with the approach of [28]. First, our VB approach adopts a decorrelated unbiased technique to eliminate the estimation bias.…”

Section: Variational Bayesian Approach To Eot Using Random Matrixmentioning

confidence: 99%

“…The first strategy is to convert measurements between polar/spherical and Cartesian coordinates and then to estimate the converted measurement error covariance. In [28], two kinds of coordinate conversion methods (i.e. the conventional and unbiased form) were presented and then integrated into the random matrix framework.…”

Section: Introductionmentioning

confidence: 99%

Extended object tracking using random matrix with converted measurements

Zhang

Jie-gui

et al. 2020

IET Radar, Sonar & Navigation

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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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Extended target tracking applied to X-band marine radar data

Cited by 21 publications

References 9 publications

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Extended object tracking using random matrix with converted measurements

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

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