Pedestrian Dead Reckoning Localization Research Articles

Context-assisted personalized pedestrian dead reckoning localization with a smartphone

ABSTRACT Pedestrian Dead Reckoning (PDR) plays an important role in multi-sensor fusion of indoor positioning due to its autonomy and continuity advantages. The robustness of PDR significantly impacts indoor positioning accuracy, but various pedestrians and mobility contexts pose challenges for reliable step detection and accurate step length estimation. This paper proposes a context-assisted personalized PDR localization solution to address these challenges. Firstly, by exploiting temporal and frequency domain features, an enhanced step detection method is developed to mitigate false step detection, especially during unfavorable actions of pedestrians. Subsequently, a personalized step length model is proposed, and its parameters are dynamically updated online using other high-precision sensors available within a multi-sensor fusion positioning solution. Moreover, the personalized step length model is further refined using mobility context knowledge. Finally, a novel context-assisted pedestrian velocity model is established for PDR localization to enhance positioning accuracy, particularly when there are changes in mobility contexts. The results demonstrate that the robustness of step detection is improved, and the false detection rate is reduced from 13% to 2%. For various smartphone users, the proposed context-assisted personalized step length model exhibits a relative error of 2.01%, in contrast to 7.06% observed with the traditional flat model. Consequently, the accuracy of walking distance is enhanced from 92.2% to 98.9%, and the PDR localization error is reduced from 2.49 m to 1.63 m. Importantly, the proposed solution exhibits more robust and consistent performance across different pedestrians, smartphone models, and challenging mobility contexts.

Hybrid Indoor Positioning Method of BLE and PDR Based on Adaptive Feedback EKF With Low BLE Deployment Density

Location-based service (LBS) has gradually penetrated into all aspects of human life. The current indoor positioning algorithms include triangulation, least squares, fingerprint, maximum likelihood estimation, and so on. These positioning algorithms have requirements for the deployment density of the access point (AP). Since the AP deployment density is directly related to the equipment cost, the AP-based positioning method has a high-cost problem that cannot be ignored. Furthermore, the positioning accuracy of traditional algorithms will sharply decline in the positioning field with low AP deployment density. In order to solve the positioning problem in application scenarios with low AP deployment density, this article proposes a hybrid indoor positioning method of Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) based on the adaptive feedback extended Kalman filter (AFEKF). The AFEKF algorithm uses an adaptive feedback EKF to deeply fuse the position of the BLE, the range measurement, and the result of PDR localization, making full use of the information of the BLE (the position, the orientation, and the range measurement) instead of only the range measurement. The range measurement is deeply fed back to the estimated position, thus adaptively adjusting the position estimate at the next moment, making the position estimate more accurate and the algorithm more robust. Experiments in large underground parking lots show that the AFEKF algorithm proposed in this article can achieve high-precision and low-cost indoor positioning, and effectively solve the positioning problem in open scenes with low AP deployment density.

An Improved PDR Localization Algorithm Based on Particle Filter

Pedestrian Dead Reckoning (PDR) helps to realize step frequency detection, step estimation and direction estimation through data collected by inertial sensors such as accelerometer, gyroscope, magnetometer, etc. The initial positioning information is used to calculate the position of pedestrians at any time, which can be applied to indoor positioning technology researching. In order to improve the position accuracy of pedestrian track estimation, this paper improves the step frequency detection, step size estimation and direction detection in PDR, and proposes a particle swarm optimization particle filter (PSO-IPF) PDR location algorithm. Using the built-in accelerometer information of the smartphone to carry out the step frequency detection, the step frequency parameter construction model is introduced to carry out the step estimation, the direction estimation is performed by the Kalman filter fusion gyroscope and the magnetometer information, and the positioning data is merged by using the particle filter. The fitness function in the particle swarm optimization process is changed in the localization algorithm to improve particle diversity and position estimation. The experimental results show that the error rate of the improved step frequency detection method is reduced by about 2.1 % compared with the traditional method. The angle accuracy of the direction estimation is about 4.12◦ higher than the traditional method. The overall positioning accuracy is improved.

Pedestrian Dead-Reckoning Indoor Localization Based on OS-ELM

Smartphone-based pedestrian dead-reckoning (PDR) has become promising in indoor localization since it locates users with a smartphone only. However, existing PDR approaches are still facing the problem of accumulated localization errors due to low-cost noisy sensors and complicated human movements. This paper presents a novel PDR indoor localization algorithm combined with online sequential extreme learning machine (OS-ELM). By analyzing the process of PDR localization, this paper first formulates the process of PDR localization as an approximation function, and then, a sliding-window-based scheme is designed to preprocess the obtained inertial sensor data and thus to generate the feature dataset. At last, the OS-ELM-based PDR algorithm is proposed to address the localization problem of pedestrians. Due to the fact of universal approximation capability and extreme learning speed within OS-ELM, our algorithm can adapt to localization environment dynamically and reduce the localization errors to a low scale. In addition, by taking the movement habits of pedestrian into the process of extreme learning, our algorithm can predict the position of pedestrian regardless of holding postures. To evaluate the performance of the proposed algorithm, this paper implements OS-ELM-based PDR on a real android-based smartphone and compares it with the state-of-the-art approaches. Extensive experiment results demonstrate the effectiveness of the proposed algorithm in various different postures and the practicability in indoor localization.

A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model.

Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.