Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Section: Related Workmentioning

confidence: 99%

A Low-Cost and Efficient Indoor Fusion Localization Method

Yan

Deng

et al. 2022

“…For LBS purposes, constraints about the heading were taken into account in [1,22], allowing for a reduction of the state space-tracking model to a four-order linear one. In [23], the heading was constrained according to the type of environment: narrow passages like corridors versus wide scenes with an azimuth wheel. In [24], position constraints were considered in an UKF when at most four anchors contributed to the localization.…”

Section: Related Workmentioning

confidence: 99%

A Constrained Kalman Filter for Wi-Fi-Based Indoor Localization with Flexible Space Organization

Sircoulomb

Chafouk

2022

This paper presents a constrained Kalman filter for Wi-Fi-based indoor localization. The contribution of this work is to introduce constraints on the object speed and to provide a numerically optimized form for fast computation. The proposed approach is suitable to flexible space organization, as in warehouses, and when objects can be spun around, for example barcode readers in a hand. We experimented with the proposed technique using a robot and three devices, on five different journeys, in a 6000 m2 warehouse equipped with six Wi-Fi access points. The results highlight that the proposed approach provides a 19% improvement in localization accuracy.

“…In [ 20 ], based on an adaptively robust EKF, Yuan et al proposed a PDR/UWB (Pedestrian Dead Reckoning and Ultra-Wide Band) integrated navigation algorithm. To obtain the adaptability, the algorithm takes the positioning scene and the heading as constraints.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 23 ], two novel quantitative nonlinear observability measures are proposed to get an optimal filter design. However, both [ 22 ] and [ 23 ] inevitably use the state-dependent calculations to adjust the measured values or the weight matrix, so the same problem as in [ 20 ] mentioned above exists. In [ 24 ], an adaptive filtering method was proposed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

Wang

Zhang

Ge³

2021

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.