Simultaneous spatiotemporal bias and state estimation for asynchronous multi‐sensor system

Bu, Shizhe; Zhou, Chang; Zhou, Gongjian

doi:10.1049/joe.2019.0461

“…Therefore, data fusion applications are in need for accurate spatiotemporal bias estimation techniques (Bu et al. 2019 ). Various methods are proposed for different sensors such as cameras, radars, or sonars Taghavi et al.…”

Section: Stdm Application-related Challengesmentioning

confidence: 99%

“…The temporal bias inevitably exits in the multi-sensor data fusion and requires more research efforts Bu et al. ( 2019 ).…”

Section: Stdm Application-related Challengesmentioning

confidence: 99%

Spatiotemporal data mining: a survey on challenges and open problems

Hamdi

¹

,

Shaban

²

,

Erradi

³

et al. 2021

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…Such delays might be caused by data transfer or signal processing. Therefore, data fusion applications are in need for accurate spatiotemporal bias estimation techniques [22]. Various methods are proposed for different sensors such as cameras, radars, or sonars [262,116].…”

Section: Smart Internet Of Thingsmentioning

confidence: 99%

“…However, these works only consider spatial data references of the multiple sensors. The temporal bias inevitably exits in the multi-sensor data fusion and requires more research efforts [22].…”

Section: Smart Internet Of Thingsmentioning

confidence: 99%

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Hamdi¹,

Shaban²,

Erradi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…The proposed asynchronous fusion algorithm can solve the measurements sequentially that does not need to preset fusion periods for fusion center to synchronize asynchronous data transmitted from multi sensors. In [21], a sequential processing simultaneous spatiotemporal bias and state estimation algorithm for asynchronous multi-sensor system is proposed. The spatiotemporal bias is combined with target state to obtain an augmented state vector, and the augmented state model is formulated.…”

Section: Introductionmentioning

confidence: 99%

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

Zhang

¹

,

Wang²,

Guo

³

et al. 2020

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The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively. INDEX TERMS Asynchronous fusion, multi sensors, track fusion, track quality with multiple model.

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Simultaneous spatiotemporal bias and state estimation for asynchronous multi‐sensor system

Cited by 11 publications

References 16 publications

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

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