Gaussian State Estimation with Non-Gaussian Measurement Noise

Tollkuhn, Andreas; Particke, Florian; Thielecke, Jörn

doi:10.1109/sdf.2018.8547146

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…This fusion structure does not require separate processing of local sensor data but processes the measurement data centrally at the fusion center to complete global track updates. Compared to the distributed fusion structure, centralized fusion utilizes raw data from each sensor and requires larger communication bandwidth, but offers slightly higher fusion accuracy [38][39][40].…”

Section: Heterogeneous Multi-sensor Sequential Filteringmentioning

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: Heterogeneous Multi-sensor Sequential Filteringmentioning

confidence: 99%

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

Zeng,

Wang,

Wei

et al. 2024

Mathematics

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“…Although Student's t-distribution provides good tracking performance, it requires fixed-point iteration to calculate various parameters in variational Bayesian inference, which increases computational complexity. Other processing methods, such as Huber Kalman filtering and maximum correlation entropy criterion Kalman filtering, are also used by a small number of researchers [17][18][19][20]. In recent years, the GSTM distribution has maintained its characteristic of modeling outlier noise using Student's t-distribution while also utilizing the Gaussian distribution to improve computational efficiency [20].…”

Section: Introduction 1literature Review and Motivationmentioning

confidence: 99%

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Wei,

Lin,

Wang

et al. 2024

Remote Sensing

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To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student’s t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time.

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“…Traditional signal processors such as underwater localization [3][4][5][6][7][8][9][10][11][12][13], underwater tracking [9,14,15], sonar imaging [16][17][18][19][20][21][22][23][24][25][26][27], direction of arrival (DOA) estimation [28][29][30][31][32], and underwater acoustic communication (UAC) are mostly based on Gaussian noise, which can be supported by a central limit theorem. Besides, the Gaussian model is just determined by the first-order and second-order statistics [33]. Under this case, the linear processors can be obtained with Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

Wang

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

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Gaussian State Estimation with Non-Gaussian Measurement Noise

Cited by 8 publications

References 3 publications

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

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

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

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