Performance Analysis of Wi-Fi Indoor Localization with Channel State Information

Tian, Xiaohua; Zhu, Shu; Xiong, Shuo; Jiang, Binyao; Yang, Yucheng; Wang, Xinbing

doi:10.1109/tmc.2018.2868680

Cited by 23 publications

(13 citation statements)

References 35 publications

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“…Although the CSI-based localization method leveraging the pedestrian motion is relatively mature, there is a general lack of theoretical analysis on the localization error bound. To the best of our knowledge, most of the studies on the indoor localization error bound are based on the concept of the Cramer-Rao Lower Bound (CRLB) [31]- [34]. For example, Authors in [31] use an information-theoretic lens to view the error bound of wireless local area network localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [33] rely on an indoor signal propagation model considering the multi-path effect to analyze the impact of shadow fading and the anchor node number on the CSI-based indoor localization error bound. Authors in [34] investigate the relationship between the CSI-based indoor localization error bound and the signal propagation delay and the antenna number from the perspective of the frequency domain. However, the current studies on the indoor localization error bound based on the CRLB rarely consider the pedestrian motion.…”

Section: Related Workmentioning

confidence: 99%

“…The asynchronous clock effect caused by the clock deviation between the target and AP will bring the phase deviation of the frequency domain signal, which will affect the signal demodulation result and increase the bit error rate. Specifically, the phase deviation of the frequency domain signal introduced by the difference in hardware devices will cause the sampling time of the target and the AP to be out of sync, which in turn causes a fixed time offset τ 0 between the actual sampling time of the AP and the optimal sampling time [34], [38].…”

Section: Asynchronous Effect Analysis a Error Bound Under The Clomentioning

confidence: 99%

“…where the values of P m , Z m and U m are as follows (34). Similarly, we are only interested in the location information of the target, which can be obtained with equivalent Fisher information matrix (EFIM).…”

Section: B Error Bound With the Carrier Frequency Offsetmentioning

confidence: 99%

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Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

et al. 2020

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Indoor pedestrian motion detection based on the Wi-Fi Received Signal Strength (RSS) has been commonly deployed in recent years. However, the Channel State Information (CSI) based indoor localization methods can be selected to achieve higher localization accuracy since it contains the finer-grained physical-layer information of the signal. Lack of theoretical analysis of the CSI-based error bound that leverages the pedestrian motion posses a challenge to investigate the ideal performance. In this circumstance, this paper proposes the Cramer-Rao Lower Bound (CRLB) concept to derive out the indoor localization error bound leveraging the pedestrian motion that depends on the constructed signal propagation model by considering the relationship between the localization accuracy and the path loss, shadow fading, and multipath effect. Through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, and the impact of different experimental parameters on the localization performance is analyzed, as well as discusses the influence of the asynchronous effect between the transmitter and the receiver on the performance of the proposed localization error bound. The experimental results show that the derived error bound has the same trend as the actual error, which validate our theoretical analysis. INDEX TERMS Indoor localization, channel state information, pedestrian motion, CRLB, asynchronous effect.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Asynchronous Effect Analysis a Error Bound Under The Clomentioning

confidence: 99%

Section: B Error Bound With the Carrier Frequency Offsetmentioning

confidence: 99%

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Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

et al. 2020

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“…The proposed system achieves an accuracy of 0.5 m in the LOS scenario, whereas, in None-line-of-Sight(NLOS), it achieves 1.1 m accuracy. Tian et al [115] presented a CSI-based indoor localization system. The main idea of this system is to perform frequency domain Crame’r–Rao bound (CRB) for location detection with CSI.…”

Section: Channel State Information (Csi)mentioning

confidence: 99%

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

Al-qaness

Elaziz

Kim

et al. 2019

Sensors

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Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

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Confidence interval estimation for fingerprint-based indoor localization

Nabati

Ghorashi

Shahbazian³

2022

Ad Hoc Networks

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Performance Analysis of Wi-Fi Indoor Localization with Channel State Information

Cited by 23 publications

References 35 publications

Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

Confidence interval estimation for fingerprint-based indoor localization

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