Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Li, Guchong; Battistelli, Giorgio; Yi, Wei; Kong, Lingjiang

doi:10.1016/j.sigpro.2019.107246

Cited by 56 publications

(27 citation statements)

References 56 publications

(88 reference statements)

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“…On the other hand, AA is more suitable for higher rates of missed detections since it tends to preserve all the detected objects. Taking into account these characteristics, it is clear that GCI fusion is hard to combine with sensors that have limited FoVs [27], [28], because the limited FoVs increase the chances of misdetected objects. For this reason, we choose to use AA.…”

Section: B Map Fusion At the Bsmentioning

confidence: 99%

“…Notice that such a region includes the accumulated FoV F k|k (x, m) refer to X k , the PHD D BS k|k−1 (x, m) refers to X k , and finally the PHD D (n) k|k (x, m) refers to X k . Given the decompositions in (28) and (29), the fused PHD is then computed as…”

Section: B Map Fusion At the Bsmentioning

confidence: 99%

“…To spread out the complexity over the network, the focus has been on decentralized methods. The most prevalent methods for multi-object density fusion consider generalized covariance intersection (GCI) [27], [28], which amounts to computing the intersection of information among densities, consequently it can not be directly utilized to fuse multi-object densities defined in different FoVs. This difficulty has been overcome in [20], where the PHD of each vehicle is initialized as non-zero throughout the whole area of interest.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Kim

Granström

Gao

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

122

128

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5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radiobased positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multimodel probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.

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Section: B Map Fusion At the Bsmentioning

confidence: 99%

Section: B Map Fusion At the Bsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Kim

Granström

Gao

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

122

128

View full text Add to dashboard Cite

show abstract

“…One solution is to extend the prior FoVs of all sensors to an identical and larger FoV before fusion (Battistelli et al, 2017;Li SQ et al, 2018), but this may underestimate the existence probabilities of targets in non-public areas. Another widely used method is to split the information according to the FoVs' intersection (Gan et al, 2016;Vasic et al, 2016;Li TC et al, 2019c;Da et al, 2020b;Li GC et al, 2020). Fusion will be executed in each splitting area separately, followed by a reunion operation for all areas.…”

Section: Limited Fields Of Viewmentioning

confidence: 99%

Recent advances in multisensor multitarget tracking using random finite set

Zhu

et al. 2021

Front Inform Technol Electron Eng

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In this study, we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic-and geometric-average fusion, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

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“…Li et al [9] took the target and image data as experimental inputs, and observed that not all regions of the image or target are key effective information for cross modal mapping in the generalization problem. Li et al [10] pioneered the application of zero-shot learning (ZSL) and cross domain hash to SBIR, and greatly expanded the research scope of ZS-SBIR. Experimental results show that Li's method only achieved a 1% higher map than traditional methods, but the introduction of the heterogeneous network is a breakthrough.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Data Fusion and Target Tracking of Heterogeneous Network Based on Data Mining

Xun-zhong¹,

Chen²

2021

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Thanks to the technical development of target tracking, the multi-source data fusion and target tracking has become a hotspot in the research of huge heterogenous networks. Based on millimeter wave heterogeneous network, this paper constructs a multi-source data fusion and target tracking model. The core of the model is the data mining deep Q network (DM-DQN). Through image filling, the length of the input vector (time window) was extended from 25 to 31, with the aid of CNN heterogeneous network technology. This is to keep the length of input vector in line with that of output vector, and retain the time features of eye tracking data to the greatest extent, thereby expanding the recognition range. Experimental results show that the proposed model achieved a modified mean error of only 1.5m with a tracking time of 160s, that is, the tracking effect is ideal. That is why the DM-DQN outperformed other algorithms in total user delay. The algorithm can improve the energy efficiency of the network, while ensuring the quality of service of the user. In the first 50 iterations, DM-DQN worked poorer than structured data mining. After 50 iterations, DM-DQN began to learn the merits of the latter. After 100 iterations, both DM-DQN and structured data mining tended to be stable, and the former had the better performance. Compared with typical structured data mining, the proposed DM-DQN not only converges fast, but also boasts a relatively good performance.

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Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Cited by 56 publications

References 56 publications

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Recent advances in multisensor multitarget tracking using random finite set

Multi-Source Data Fusion and Target Tracking of Heterogeneous Network Based on Data Mining

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