Target Tracking in Mixed LOS/NLOS Environments Based on Individual Measurement Estimation and LOS Detection

Yi, Lili; Razul, Sirajudeen Gulam; Lin, Zhiping; See, Chong Meng Samson

doi:10.1109/twc.2013.111313.121783

Cited by 25 publications

(21 citation statements)

References 37 publications

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“…Generally, the initial deviation can be calibrated by calculating the difference between the true value and the measurement in field tests [14]. The random clock error causes a fluctuation of the received RTT data on the mobile terminal, which can be eliminated by filters such as the Kalman Filter (KF) [35], Mean filter [36], Gaussian filter [37].…”

Section: Challenges Of Indoor Positioning For Pedestriansmentioning

confidence: 99%

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Chen

et al. 2019

Remote Sensing

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More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

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Section: Challenges Of Indoor Positioning For Pedestriansmentioning

confidence: 99%

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Chen

et al. 2019

Remote Sensing

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“…In Figure 3, the estimated trajectories based on the IMD [20], Model 1, and Model 2 are given. The IMD algorithm detects if the bias exists in the measurement and uses the unbiased measurement.…”

Section: Simulationsmentioning

confidence: 99%

Measurement Bias Estimation in the Problem of Target Tracking

Ren

2019

Wireless Communications and Mobile Computing

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In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.

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“…These results are further extended to nonlinear AOA measurement by using the MUSIC method in [16]. Similar approach has also been used to observe a mobile objective in mixed LOS/NLOS environments by using individual measurement and LOS detection [17]. However, little research has been investigated to mitigate the delay and dropout problems caused by NLOS propagation.…”

Section: Introductionmentioning

confidence: 99%

“…. , ℓ} be the set of complementary observations generated at instant , the relative measures between objective and its neighbors ∈ N [ ] , that is, relative distance and relative angle, are calculated through RSS and AOA, respectively, as depicted in (4), and the measure noise covariancẽ[ ] ( ) in(17). Hereafter, the fused measurement covariance and the fused measurement variable [ ] ( ) ( ∈ {ℏ, − }) can be expressed as…”

mentioning

confidence: 99%

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Gao

Zhao

et al. 2016

Mathematical Problems in Engineering

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Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

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Target Tracking in Mixed LOS/NLOS Environments Based on Individual Measurement Estimation and LOS Detection

Cited by 25 publications

References 37 publications

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Measurement Bias Estimation in the Problem of Target Tracking

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

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