Implement of cooperative navigation in pedestrian navigation based on distance restraint

Chen, Ze; Mu, Hua; Chen, Changhao; Wu, Meiping

doi:10.1109/cgncc.2016.7829119

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…The remaining 18 articles were fully collaborative, as they relayed the position estimation of the nodes and/or actors completely to a collaborative method (i.e., denoted as "Cooperative methods" in Figure 5). In other words, stand-alone positioning was not performed in these systems, and the collected raw data were directly processed using the corresponding proposed collaborative method [85,88,90,95,96,99,105,112,115,121,122,126,133,[138][139][140]142,148]. Therefore, the cooperative methods are excluded from the discussion in this section devoted to the non-collaborative part; for full details on the collaborative systems, please see Section 5.2.2.…”

Section: Analysis On the Non-collaborative Partmentioning

confidence: 99%

“…Sensor fusion is not usual in CIPS, neither in the non-collaborative nor in the collaborative parts, despite state-of-the-art IPSs combining multiple technologies to enhance their accuracy, robustness, and/or precision [154][155][156]. Only [43,63,98,102,118,123,129] applied sensor fusion in the non-collaborative part and [121] in the collaborative part. Similarly, none of the works considered a scenario where different non-collaborative positioning solutions (with different technologies, techniques, and/or methods) co-exist as shown in the exemplary scenario in Figure 1.…”

Section: Overarching Concernsmentioning

confidence: 99%

“…Experimental evaluations best mimic circumstances and operational conditions of a real-life scenarios [43,79], yet they are more difficult to set up and perform [63,129], time-consuming [44,117,131], prone to various types of failure and errors [43,129], are and (potentially) costly [115,125,129]. Additional difficulties of experimental evaluations are the difficult-to-control practical issues faced in real-world scenarios, for example, the loss of precision due to signal attenuation/interference [43,121] and NLOS conditions [43].…”

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

“…Regarding the metrics, all the reviewed works included the positioning accuracy, as we can observe in Figure 4d. Going deeper in this particular evaluation metric, we observe that different measures have been provided which, in descending order, are as follows: the Cumulative distribution Function (CDF) of the positioning error [43,44,79,80,108,112,113,116,118,119,126,129,131,134,150], the Root Mean Square Error (RMSE) [48,49,77,109,121,122,126,135], the standard deviation of the error [40][41][42]63,80,110], the minimum mean square error [46,117,129,130,133], and finally the average positioning error [105,123,132,134]. This makes comparison of accuracy unfeasible.…”

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

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Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

108

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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Section: Analysis On the Non-collaborative Partmentioning

confidence: 99%

Section: Overarching Concernsmentioning

confidence: 99%

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

108

View full text Add to dashboard Cite

show abstract

“…Fortmann [14] and Sun [15] used factor graph theory to design an underwater unmanned ship cooperative navigation algorithm and verified that the positioning accuracy of the algorithm was higher than the extended Kalman. Chen [16], Liu [17], and Zhang [18] studied the indoor pedestrian positioning method, which is based on the premise that only one pedestrian can obtain GNSS positioning. Chen [16] used the distance information between pedestrians to suppress the divergence of other pedestrian inertial navigation positioning.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Localization Method Based on Hybrid Network for Aerial Swarm

Wang,

Zhang,

et al. 2024

Aerospace

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In light of the satellite rejection environment and how aircraft can obtain high-precision positioning, this paper proposes a collaborative correction algorithm for aircraft based on the rank-defect network. Aiming at the problem of insufficient anchor points, which result in insufficient observations and the divergence of aircraft inertial navigation errors, this algorithm can effectively improve the navigation performance of cluster aircraft. On the basis of the observation information provided by the anchor aircraft, the observation information between aircraft is fully utilized to improve the observability of the aircraft cluster positioning method. At the same time, the pseudo-observation equation of heterogeneous aircraft cluster positioning is introduced, and the divergence of inertial navigation positioning errors caused by insufficient observations is suppressed by the pseudo-observation solution. On the basis of introducing the pseudo-observation equation, the inertial navigation error is solved and corrected by the Newton iterative method and the divergence of the inertial navigation position error is restrained. Compared with an aircraft cluster positioning method that does not use the inertial navigation error co-correction based on the pseudo-observation solution, this paper can achieve better overall cluster positioning accuracy when the available observations are insufficient, which is suitable for practical applications.

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Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Ding

Xiong

et al. 2022

IEEE Internet Things J.

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Implement of cooperative navigation in pedestrian navigation based on distance restraint

Cited by 3 publications

References 4 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Localization Method Based on Hybrid Network for Aerial Swarm

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

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