# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU  
[PERSON]  
1 School of Instrument and Electronics, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (X.-Y.Y. ); [EMAIL_ADDRESS] (C.-L.Y. ); [EMAIL_ADDRESS] (X.-Y.L.) [PERSON]  
1 School of Instrument and Electronics, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (X.-Y.Y. ); [EMAIL_ADDRESS] (C.-L.Y. ); [EMAIL_ADDRESS] (X.-Y.L.) [PERSON]  
1 School of Instrument and Electronics, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (X.-Y.Y. ); [EMAIL_ADDRESS] (C.-L.Y. ); [EMAIL_ADDRESS] (X.-Y.L.) [PERSON]  
2 Shamxi Key Laboratory of Intelligent Detection Technology & Equipment, North University of China, Taiyuan 030051, China 23 School of Mathematics, North University of China, Taiyuan 030051, China 32 School of Mathematics, North University of China, Taiyuan 030051, China 23 School of  
To restrain error accumulation of IMU, [PERSON] first proposed a commonly used method, which was named Zero Velocity Update (ZUPT) [1], by correcting error drift using the constraint that the instantaneous velocity turns to zero when the person stops walking. Subsequent studies validated the effectiveness and convenience of this method, and ZUPT has since become one of the foundational algorithms for inertial pedestrian navigation. However, the correction of heading drift in the ZUPT algorithm heavily relies on accurate detection of zero-velocity intervals. To tackle this problem, researchers have conducted further studies on zero-velocity detection algorithms. In 2010, [PERSON] proposed a zero-velocity detector based on the Generalized Likelihood Ratio Test (GLRT) [2], which incorporated more available information compared to traditional zero-velocity detection algorithms to improve the accuracy of zero-velocity interval detection. With the rapid development of smart technologies, recent years have seen the emergence of zero-velocity detection algorithms based on machine learning and neural networks. In 2020, [PERSON] et al. proposed two machine learning-based zero-velocity detectors, namely gradient boosting and random forests, achieving an accuracy of 98.68% in identifying four different movement modes [3]. In 2022, [PERSON] et al. introduced an indoor pedestrian navigation system based on deep neural networks and multiple LET signal base stations. Through experiments on walking paths involving various walking modes, the system achieved a 3D positioning error of 1.34 m [4]. In 2024, [PERSON] et al. proposed a fusion method combining Temporal Convolutional Network and Bidirectional Gated Recurrent Units for precise detection of Zero-Velocity Intervals. Through multiple experiments and training, the method achieved a cross-validation accuracy of 98.5% [5]. While these methods offer higher accuracy, they are computationally more complex and typically require large amounts of pedestrian walking data for training to improve the accuracy and robustness of zero-velocity detection, resulting in significant time resource consumption. As research deepened, the academic community gradually realized that relying solely on zero-velocity correction as a constraint method could not fully eliminate inertial sensor drift errors. Thus, researchers began exploring the integration of biomechanical principles into pedestrian navigation systems, offering new perspectives and methods for algorithm optimization. For a single IMU, constructible constraint models are limited. It was not until 2019 and 2022 that [PERSON] [6], [PERSON] [7], and others proposed algorithms that combined the concept of maximum stride length constraint with Pedestrian Dead Reckoning. The experimental results obtained from this method show a 46% reduction in error compared to the traditional ZUPT algorithm. Although these methods offer low cost and convenience in device portability, their effectiveness in further reducing errors is not very significant.