Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications

Tafti, Abdolreza Dehghani; Sadati, N.

doi:10.1109/iciea.2008.4582491

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…This itself is a topic for ongoing research [29,46]. There has been some success in direction of adaptive kalman filters [20,69,66].…”

Section: Discussionmentioning

confidence: 99%

Architecting Dependable Learning-enabled Autonomous Systems: A Survey

Cheng,

Gulati,

Yan

2019

Preprint

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We provide a summary over architectural approaches that can be used to construct dependable learning-enabled autonomous systems, with a focus on automated driving. We consider three technology pillars for architecting dependable autonomy, namely diverse redundancy, information fusion, and runtime monitoring. For learning-enabled components, we additionally summarize recent architectural approaches to increase the dependability beyond standard convolutional neural networks. We conclude the study with a list of promising research directions addressing the challenges of existing approaches.

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“…This itself is a topic for ongoing research [29,46]. There has been some success in direction of adaptive kalman filters [20,69,66].…”

Section: Discussionmentioning

confidence: 99%

Architecting Dependable Learning-enabled Autonomous Systems: A Survey

Cheng,

Gulati,

Yan

2019

Preprint

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“…Therefore, it is vital for WSN to be capable to cope with uncertainty related to the networks and thus process the data realistically with less complexity and computational effort. To achieve that it is decided to use fuzzy logic system technology as it is flexible with a better capability to cope with uncertainty of data compare to many techniques includes statistical and Covariance Intersection (CI) based methods [18]. In this paper, we use type-II fuzzy logic system (T2FLS).…”

Section: Related Workmentioning

confidence: 99%

An Alternative Clustering Scheme in WSN

2015

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Despite significant advancements in wireless sensor networks (WSNs), energy conservation in the networks remains one of the most important research challenges. One approach commonly used to prolong the network lifetime is through aggregating data at the cluster heads (CHs). However, there is possibility that the CHs may fail and function incorrectly due to a number of reasons such as power instability. During the failure, the CHs are unable to collect and transfer data correctly. This affects the performance of the WSN. Early detection of failure of CHs will reduce the data loss and provide possible minimal recovery efforts. This paper proposes a self-configurable clustering (SCCH) mechanism to detect the disordered CHs and replace them with other nodes. Simulation results verify the effectiveness of the proposed approach.

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“…Moreover, the inflexibility of the methods prevents processing the data realistically [ 13 ]. In fact, using these methods requires a very complex and substantial computational effort to have optimal performance [ 14 ]. A Kalman Filter-based approach to correct the values measured by the sensors is discussed in [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

A Data Fusion Method in Wireless Sensor Networks

Izadi

Abawajy

Ghanavati

et al. 2015

Sensors

107

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The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches.

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Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications

Cited by 15 publications

References 17 publications

Architecting Dependable Learning-enabled Autonomous Systems: A Survey

Architecting Dependable Learning-enabled Autonomous Systems: A Survey

An Alternative Clustering Scheme in WSN

A Data Fusion Method in Wireless Sensor Networks

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