Query: I am writing a paper about datasets for indoor localization, with a special focus on machine learning approaches. I want to focus now on the related work section, which I categorized based on the type of sensors. Consider this paragraph I wrote (I wrote references inline for simplicity):

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Accelerometers and Gyroscopes
These motion-detecting sensors are the central part of inertial navigation systems embedded into smartphones. They facilitate the tracking of a user's movement and orientation, enabling path tracing and step counting. Through advanced algorithms, these sensors can deduce the user's speed and direction, contributing significantly to the enhancement of indoor localization accuracy techniques. In [Poulose, Alwin et al. “An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data.” IEEE Access 7 (2019): 11165-11177] authors present novel Kalman filter-based sensor fusion techniques for pitch and roll estimation and heading estimation, a pitch-based step detection algorithm and a position estimation algorithm to achieve significant improvements in position accuracy compared to traditional methods. In [Antsfeld, Leonid et al. “Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking.” ArXiv abs/2011.10799 (2020): n. pag.] a deep learning-based indoor localization framework combining pedestrian dead reckoning with barometer data, WiFi signals and physical environment constraints to predict a user's trajectory using smartphone sensors is proposed. The system uses a Kalman Filter for drift correction and a map-free projection method to align the predicted path with walkable paths. Convolutional and recurrent neural networks are exploited in the pre-processing phase to learn a model of user local displacement. In [Wang, Wei et al. “An Improved PDR Localization Algorithm Based on Particle Filter.” Comput. Informatics 39 (2020): 340-360.] Wang, Wei et al. enhance Pedestrian Dead Reckoning (PDR) by proposing a particle swarm optimization particle filter (PSO-IPF) algorithm to improve step frequency detection, step size estimation, and direction estimation. Specifically, the approach employs accelerometer data for detecting step frequency, constructs a model for step estimation, and fuses gyroscope and magnetometer data using a Kalman filter for accurate direction estimation. The adaptation of the fitness function within the PSO process improves particle diversity and enhances the accuracy of position estimation, addressing the limitations of traditional PDR approaches in complex indoor environments.
A deeper review of the state of the art for inertial navigation approaches is presented in surveys [Panja, Ayan Kumar et al. “Survey on inertial sensor-based ILS for smartphone users.” CCF Transactions on Pervasive Computing and Interaction 4 (2022): 319 - 337.], [Q. Wang et al., "Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review," in IEEE Sensors Journal, vol. 22, no. 23, pp. 22319-22343, 1 Dec.1, 2022, doi: 10.1109/JSEN.2022.3213836. keywords: {Inertial navigation;Fingerprint recognition;Sensors;Navigation;Location awareness;Power demand;Global navigation satellite system;Indoor positioning;inertial navigation;pedestrian dead reckoning (PDR);performance evaluation},] 
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Check if is good for a paper, and try to enhance it by expanding it, maybe, with some other related papers
Subject: computer science
Intent: Literature Understanding