Information Geometry-based Technique for Generating Electromagnetic Situations

Sutanu Ghosh^*
*Correspondence: Sutanu Ghosh, Department of Electronics and Communication Engineering, Institute of Engineering and Management, Salt Lake, West Bengal, India, Email:

Introduction

The varying locations of interference sources in a complicated electromagnetic environment will impact their normal function. It is required to create an electromagnetic scenario that can be clearly exhibited and simply analysed in order to seek and pinpoint the source of the interference. Existing approaches have issues with calculating speed, accuracy, and the ability to operate with various types of electromagnetic data. Based on the information geometry theory, this research offers an electromagnetic situation creation technique for distributed sensor networks that may considerably improve the function while only adding a tiny amount of calculation cost.

The statistical model of signals from each sensor is created using the Gaussian Mixture Model (GMM). The strategy based on the parameters transfer of nearby nodes is offered to speed up the scenario formation. The simulation findings suggest that, as compared to the old way, the electromagnetic situation provided by the method proposed in this study is more sensitive to the interference and its position. The proposed approach is tested in a microwave anechoic chamber, and the findings reveal that the method described in this work has a rapid response time and good accuracy when the electromagnetic condition changes [1,2].

Description

The Internet of Things and smart cities represent a new generation of electronic information technology applications that rely on positioning data. The location source, however, may fail due to diverse environmental electromagnetic interference, building shielding, and other causes. Cooperative positioning technology allows for the sharing of positioning data and can compensate for an inaccurate positioning source. When one node in a cooperative positioning network makes a mistake, the positioning stability of the entire cooperative network suffers; however positioning probability information technology can efficiently mitigate the impact of mutation error. This study offers a distributed information-geometry-assisted algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information based on this concept. Cooperative positioning is achieved by mapping and fusion of ranging information between cooperative nodes on the geometric manifold surface, which can effectively improve the stability of the positioning results when combined with the nonlinear fitting characteristics of the information geometric manifold. In terms of node positioning error, range error, convergence speed, and distribution of the cooperative positioning network, the suggested algorithm is simulated and examined. Our suggested cooperative positioning algorithm may significantly improve positioning stability and showcase superior positioning performance, according to simulation findings.

The essential underpinning of 5G application technologies like the Internet of Things and smart cities is cooperative positioning. Some nodes in the cooperative network will experience error mutation due to the intrinsic errors of satellite navigation, inertial navigation, and other navigation technologies. The stability of cooperative positioning accuracy must be investigated. The cooperative position system has gotten a lot of interest in the scientific community, and it has a lot of uses, including regional unmanned driving and an unmanned distribution network for urban trees. In the cases above, Unmanned Aerial Vehicles (UAVs) require high-stability positioning; thankfully, the cooperative positioning system offers several benefits in terms of boosting positioning accuracy stability [3,4].

To determine the positions of nodes, early cooperative positioning technologies relied mostly on range and direction finding. To achieve navigation and location, the initial generation of cooperative positioning technology mostly used centralised data processing. The positioning information of all nodes might be retrieved by measuring the distance between the centre node and the surrounding nodes, along with the node's own positioning information. When an error occurs at any node, it affects the entire cooperative positioning network, lowering positioning accuracy stability. In a high-interference situation, such as a battlefield, centralised cooperative positioning is more susceptible to interference, reducing the positioning stability of all participating cooperative nodes. The range error between cooperative network nodes and their own position error, on the other hand, would further influence positioning stability, but these faults are difficult to measure and evaluate. Because a substantial percentage of cooperative nodes lacked positioning capability in early cooperative positioning systems, high-precision positioning nodes were required as base stations to transmit positioning coordinates to all cooperative nodes. As a result, centralised cooperative positioning technology frequently overlooks node positioning accuracy stability. The MSDFC algorithm (multisensor data fusion cooperative position) is proposed in. The cooperative node sends the positioning data to the central node, which then performs an optimal estimation of all nodes in the cooperative network's positioning results [5].

Conclusion

These approaches can enhance the location accuracy of the participating nodes, but they have higher range and communication performance requirements. The bottleneck stage of centralised cooperative locating technology has arrived. It is required to do research on a distributed cooperative positioning approach without a central node to suit the objectives of future smart city development.

Distributed cooperative positioning technology may create a largescale positioning network and is more useful in practise. Each node in the cooperative network is self-contained. To actualize the positioning solution, each node receives location and ranging information from surrounding nodes in the early stages.

References

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