Lower Bound Accuracy of Bearing-Based Localization for Wireless Sensor Networks

Zhong, Wenjing; Luo, Xiaoyuan; Li, Xiaolei; Yan, Jing; Guan, Xinping

doi:10.1109/tsipn.2020.3013388

Cited by 6 publications

(2 citation statements)

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“…The PIR signal feature s f eat is the signal type that most frequently occurs in the N -times sampled signals, which has to be either s f eat = 0 or s f eat ∈ Cov det . For instance, s f eat = 1 1 1 1 = cov 5 , suggesting that the target is in Cell (5). Figure 3 shows the process of the target movements from Cell(4) to Cell( 6) and the corresponding feature PIR signals measured by the PIR detector.…”

Section: The Design Of Pir Detectormentioning

confidence: 99%

“…For instance, Naseri [ 4 ] proposed an algorithm to solve the problem of cooperative distributed localization using joint distance and direction estimates without prior information. Zhong [ 5 ] quantitatively investigated bearing-based localization accuracy from the perspective of network geometric structure. Keskin [ 6 ] proposed a cooperative localization for hybrid infrared and visible light networks, involving multiple LED transmitters having known locations (e.g., on the ceiling) and visible light communication units equipped with both LEDs and photodetectors for cooperation.…”

Section: Related Workmentioning

confidence: 99%

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Cooperative Networked PIR Detection System for Indoor Human Localization

Chen

Wen

et al. 2021

Sensors

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Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks.

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Section: The Design Of Pir Detectormentioning

confidence: 99%