Kalman Filter Fusion With Smoothing for a Process With Continuous-Time Integrated Sensor

Movassagh, Sh.; Fatehi, Alireza; Sedigh, Ali Khaki; Shariati, A.

doi:10.1109/jsen.2023.3244659

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In this study, we applied the SAVI index to monitor the tillering and maturity stages. Considering the variations in vegetation index performance and uncertainty across different growth stages [51], we developed the KF-DGDV method to improve the LAI estimation accuracy for rice; our results indicate that this approach outperforms classical Kalman filter fusion methods [52]. Unlike methods involving radiative transfer models that yield a large variety of data types, or deep learning methods that often lack interpretability, KF-DGDV can be directly applied to enhance the accuracy of traditional empirical models, making it both stable and effective.…”

Section: Discussionmentioning

confidence: 98%

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

Yu,

He,

et al. 2024

Remote Sensing

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The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice.

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Section: Discussionmentioning

confidence: 98%

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

Yu,

He,

et al. 2024

Remote Sensing

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“…For multitarget tracking, we employ the GSTM-TCPHD filter for non-Gaussian heavytailed noise systems [43]. Nevertheless, the tracking field of view is constrained with a single sensor, and there is a lot of clutter information and little target measurement information [44]. Consequently, multi-sensor scenarios must be studied; however, as the number of sensors increases, so do the measurements, which can lead to a combinatorial explosion.…”

Section: Gstm-tcphd Filter Implementation With a Multi-sensormentioning

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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Unstructured environment navigation and trajectory calibration technology based on multi-data fusion

Wang,

Chen,

Peng

et al. 2024

Computers and Electrical Engineering

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Kalman Filter Fusion With Smoothing for a Process With Continuous-Time Integrated Sensor

Cited by 3 publications

References 27 publications

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)

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

Unstructured environment navigation and trajectory calibration technology based on multi-data fusion

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