Online sequential spatiotemporal bias compensation using multisensor multitarget measurements

Bu, Shizhe; Kirubarajan, Thiagalingam; Zhou, Gongjian

doi:10.1016/j.ast.2020.106407

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…In the paper by Ge et al [8] mean spatial bias falls within 2 m and in this paper -again, in the favourable case -spatial bias falls within 0.5 m. In the same paper by Ge et al their mean error on angular bias is 0.02 • . The temporal bias estimation results can be compared with the work by Bu et al [15] who obtain root mean squared error (RMSE) values for temporal bias estimates that fall within 0.1 s of the truth: that is, with a minimal error of 0.074 s and maximal error of 0.097 s. The less favourable setup in this paper has ϵ τ = 0.8525 s.…”

Section: Resultsmentioning

confidence: 76%

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Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Macdonald

Proudler

Davies

et al. 2022

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited -such as underwater, or in hostile regions -this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m.

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Section: Resultsmentioning

confidence: 76%

“…They provide a comprehensive performance evaluation and comparison with five other methods. The sensor registration problem has been approached from many angles: random finite set formulations [12], modification of filters [13], least squares [14], and the minimum mean square error (MMSE) framework [15].…”

Section: B State Of the Artmentioning

confidence: 99%

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Macdonald

Proudler

Davies

et al. 2022

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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show abstract

“…The task of time synchronization is to synchronize this information to the same point in time. For multiple sensors under different platforms or multiple sensors under the same platform, there is a time difference between the measurement data reported by each sensor because each sensor detects the target independently and each sensor independently decides the time to send the measurement report to the fusion center, in addition to the different time of information transmission between each platform and the fusion center [8]. As shown in Fig.…”

Section: Time Synchronisationmentioning

confidence: 99%

Multi-sensor time synchronization algorithm based on Gaussian process regression

Yang

Zhao²,

Zhang³

et al. 2022

J. Phys.: Conf. Ser.

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In order to address the problem of inconsistent sampling frequency and accuracy of each sensor in multi-sensor data fusion technology, the algorithm of Gaussian Process Regression (GPR) is proposed for time alignment, and in the scenario where the target motion model is uniform linear motion and uniform turning motion, the simulation is compared and analyzed respectively. The simulation results show that the proposed algorithm can effectively improve the accuracy of time synchronization.

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“…However, these methods have limitations. In multi-sensor multi-target tracking scenarios, spatial registration and track association are interconnected, and the presence of sensor biases presents significant challenges to track association [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Zeng,

Wang,

Wei

et al. 2024

Mathematics

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Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

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Online sequential spatiotemporal bias compensation using multisensor multitarget measurements

Cited by 7 publications

References 29 publications

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Multi-sensor time synchronization algorithm based on Gaussian process regression

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

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