Sensor alignment with Earth-centered Earth-fixed (ECEF) coordinate system

Zhou, Yifeng; Leung, Henry; Blanchette, Martin

doi:10.1109/7.766925

Cited by 90 publications

(7 citation statements)

References 7 publications

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“…Sensor spatial registration has applications in robotics, augmented reality, target tracking, navigation, autonomous vehicles, remote sensing, and industrial automation. Extensive research has been conducted on spatial registration for multi-sensor systems [23][24][25][26][27][28][29][30][31][32][33][34][35]. Spatial registration can be broadly divided into two categories: one is based on common significant targets, which rely on known association relationships, and the other is based on the RFS framework.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

Zeng,

Wang,

Wei

et al. 2024

Mathematics

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“…During the estimation phase, additional information from GPS are required (zero mean, standard deviation 10 m). Note that the sensor alignment method [6] (normal least square method, using first order Taylor extension for linearizion) is also utilized to compare the performances.…”

Section: Simulationmentioning

confidence: 99%

“…Various methods have been proposed including the centralized [6,7,8] and decentralized solutions [9,10,11,12]. In the centralized solution, the exact maximum likelihood method is proposed where the sensor measurements were first projected onto the local coordinate, and then transformed to the public region [13].…”

Section: Introductionmentioning

confidence: 99%

Systematic Error Modeling and Bias Estimation

Zhang

Knoll

2016

Sensors

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“…However, the accurate track-to-track association is the precondition of the registration, and inevitably, the registration based on the false associated targets can not estimate the systematic errors correctly and at last the estimated result will lead to the invalidation of all targets' track-to-track association and fusion. In addition, the registration algorithms [15][16][17][18][19][20] often neglect this problem and suppose that the correct associated tracks of targets have been acquired (i.e. the tracks used for registration corresponding to the same target), but in practice, the target tracks are not likely to be accurately associated because of the systematic errors.…”

Section: Introductionmentioning

confidence: 99%

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

Yang¹,

Song²,

Qu³

et al. 2013

JSIP

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The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

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Sensor alignment with Earth-centered Earth-fixed (ECEF) coordinate system

Cited by 90 publications

References 7 publications

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

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

Systematic Error Modeling and Bias Estimation

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

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