A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Hybrid Solutions. <s> Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Hybrid Solutions. <s> This study discusses a simple multi-sensor multi-network positioning system that integrates global positioning satellite (GPS) measurements, accelerometers and a digital compass with wireless network localisation utilising a pedestrian motion model and dead reckoning. The feasibility of the multi-technology system for seamless outdoor to indoor pedestrian navigation is discussed with the emphasis on reliability issues and adaptability requirements. The multi-sensor multi-network positioning system is developed for challenging navigation environments such as indoors and deep urban canyons. This study considers how to estimate and improve such a multi-sensor multi-network system's reliability and estimate its accuracy. An outdoor to indoor pedestrian test is conducted. Adaptive filtering performance of the multi-technology solution as well as general measurement quality monitoring and error detection when an over-determined solution is at hand is shown. Reliability estimation utilising adaptation in the form of environment detection to estimate the final positioning accuracy is also presented. <s> BIB002
The basic fingerprinting-based indoor localization algorithms such as kNN and probabilistic methods will introduce location jitters because the original fingerprinting algorithms do not take the motion dynamic model into account. In order to achieve reliable indoor localization, hybrid solutions using both fingerprints and motion sensors are widely adopted BIB001  BIB002 . The potential fusion techniques include Kalman filter, the hidden Markov model, and particle filter. Kalman filter is a common algorithm of multisources fusion, which has been extensively discussed in previous literatures. Since the movement of a pedestrian is usually nonlinear trajectory, an extended Kalman filter (EKF) is widely employed, in which the nonlinearity can be dealt with by a Taylor expansion. When the state transition and measurement models, that is, the prediction and measurement update matrices are highly nonlinear, the EKF gives particularly poor performance because the covariance is propagated through linearization of the underlying nonlinear model  . In this survey, we introduce HMM and particle filter based hybrid indoor localization approaches. In order to mitigate the impact of Wi-Fi fingerprinting caused by RSSI variances, Liu et al. BIB001 proposed a HMMbased fusion framework, as shown in Figure 3 , to augment the Wi-Fi positioning by motion information. In the HMM approach, a user's positions are the hidden states to be estimated, and the sequence of positions has the Markov property. Observables in BIB001 are Wi-Fi RSSI, and the emission probabilities of observables are probabilistic RSSIposition dependency obtained from a knowledge database. The accurate state transition probabilities can improve the localization results using the HMM approach. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space time-series model. The state vector contains the kinematic information of a pedestrian in the localization system. The measurement vector represents noisy observations such as movements derived from accelerometers, gyroscopes, and magnetometers, and location estimated by signal fingerprinting   . The state vector can handle multivariate data and nonlinear/non-Gaussian processes. Figure 4 presents an approach which integrates state updates from PDR, fingerprints, and constraints from a floorplan to acquire the posterior distribution of a pedestrian's location  . Particles wrap the position coordinates, heading, parameters of step length, and the weights derived Floor map/fingerprint database Step detection Step length estimation from fingerprinting. Besides, the PDR parameters can also be learned and corrected during the particle propagation.