Indoor positioning using particle filters with optimal importance function

This paper presents a new calibration method, which combines the advantages of Kinect large measurement range and high accuracy of laser scanner measurement. The proposed method innovatively designs a calibration objects with special shapes to complete the calibration of laser scanner. To achieve effective fusion between two kinds of sensor information, the laser scanner and Kinect are jointly calibrated to obtain the relative coordinate transform between them. Finally, a particle filter (PF) with the optimal importance function and importance weights is designed to fuse the measurements of the target's range and bearing from the laser scanner and Kinect. The experimental results show that the proposed method can achieve satisfactory tracking performances.

For the standard particle filter, sampling efficiency is decreasing rapidly with time, especially when the state dimension is large. Rao-Blackwellized particle filter can improve the sampling efficiency by marginalizing over one part of the state and applying particle filter to the other part with lower dimension. However, Rao-Blackwellization will lead to a non-Markovian model, and this causes the existing optimal importance density function theory for the standard particle filter is inapplicable. In this paper, in the sense of the minimum conditional variance of importance weights, we derive the corresponding optimal importance density function for Rao-Blackwellized particle filter. The concrete calculation formula for optimal importance density function is provided for two particular conditionally linear Gaussian models. When optimal importance density function cannot be computed analytically, suboptimal importance density functions are designed by Gaussian approximation methods. The effectiveness of the proposed methods are illustrated through three simulation examples including two typical target tracking models and a fourth order mixed linear/nolinear Gaussian model.

The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the state-space exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging real-world image sequences.

Indoor localization is a fundamental issue in IoT (Internet of Things). On the other hand, IoT provides a lot of networked devices that would help increase the precision of indoor localization. Particle Filter (PF) is widely used in indoor localization due to its flexibility that can adapt to different, and usually complex, indoor floorplans and furniture placements. In this work, we consider the fusion of multi-sensory data using PF. We focus on three types of popular sensors: IM (inertial measurement) sensor, RF (radio frequency) sensor, and environmental visual sensor. In particular, with environmental visual sensors, there is no extra device to be attached to localized targets. We propose a PF model that can adopt these types of sensory inputs. We show that in scenarios where visual sensory inputs are available, sub-meter precision can be achieved and in places with no visual coverage, seamless localization with reasonable precision can be supported by other sensors. Field trial results are presented, which show that our model is quite suitable for areas like lobby, corridor, and meeting room.

In the present paper, optimization of functions with uncertainty by means of Genetic Algorithms (GA) is discussed. For such problems, there have been proposed methods of sampling fitness function several times and taking average of them for evaluation of each individual. However, important applications having uncertain fitness functions are online adaptation of real systems and complicated computer simulation using random variables. In such applications, possible number of fitness evaluation is quite limited. Hence, methods achieving optimization with less number of fitness evaluation is needed. In the present paper, the authors propose a GA for optimization of continuous fitness functions with observation noise utilizing history of search so as to reduce number of fitness evaluation. In the proposed method, value of fitness function at a novel search point is estimated not only by the sampled fitness value at that point but also by utilizing the fitness values of individuals stored in the history of search. Computer experiments using quadric fitness functions show that the proposed method outperforms the conventional GA of sampling fitness values several times at each search point in noisy environment.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

This paper proposes a BLE used indoor positioning method, which combines the fingerprint method with particle filter, for improving the estimation accuracy. Particle filter can be implemented by creating a likelihood function. The proposed method makes fingerprint map based on RSSI from BLE beacon, and creates particle filter which estimates indoor position from observed RSSI. The experiment results show the effectiveness of the proposed method.

<h3>Abstract</h3> Filtering-based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self-contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao-Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle-of-Arrival measurements in particle-filter-based positioning improves the 3-D positioning accuracy by about 37.3% on average.

Indoor localization can provide a number of different services such as location-aware advertisement, indoor navigation and automating different appliances based on the user location. A number of different techniques such as time-difference- of-arrival, angle-of-arrival, time-of-flight, and received signal strength indicator (RSSI) have been used to provide Location Based Services (LBS). RSSI is one of the widely used methods as it is cost efficient and easy to implement. However, RSSI's performance is limited by multipath fading and indoor noise. Particle Filter (PF) is an accurate Bayesian Filtering algorithm that can improve the performance of RSSI-based indoor localization. However, PF is not able to satisfy the high accuracy requirement (possibly 10cm) of indoor localization. In this paper, we present Particle Filter-Extended Kalman Filter (PFEKF) cascaded algorithm that combines PF and EKF in series to reduce the impact of multipath effects and noise on the RSSI. Our experimental results show that PFEKF improves the localization accuracy by 31.3% and 33.9% in 3D and 2D environments respectively when compared with using only a PF.

Particle filtering has drawn much attention in recent years due to its capacity to handle nonlinear and non-Gaussian problems. One crucial issue in particle filtering is-the selection of importance function. In this paper, we propose a new type of importance function, which possess advantages over both the posterior and the prior importance functions. In addition, we demonstrate the use of the proposed importance function in blind detection in flat fading channels. Simulation results show its efficiency and performance.

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

In this paper, we develop an improved particle filtering algorithm for nonlinear states estimation. In case of standard particle filter, the latest observation is not considered for the evaluation of the weights of the particles as the importance function is taken to be equal to the prior density function. This choice of importance sampling function simplifies the computation but can cause filtering divergence. In cases where the likelihood function is too narrow as compared to the prior function, very few particles will have significant weights. Hence a better proposal distribution that takes the latest observation into account is desired. The proposed algorithm consists of a particle filter based on minimizing the Kullback-Leibler divergence distance to generate the optimal importance proposal distribution. The proposed algorithm allows the particle filter to incorporate the latest observations into a prior updating scheme using the estimator of the posterior distribution that matches the true posterior more closely. In the comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The simulation results show that the proposed algorithm, outperforms the standard particle filter, the unscented Kalman filter, and the extended Kalman filter algorithms.

The task of detecting metal objects in different environments has always been important. To solve it metal detectors are used. They are designed to detect and identify objects that in their electric or magnetic properties different from the environment in which they are located. The most common among them are the metal detectors of the «detection of very low frequency» type (Very Low Frequency (VLF) detectors). They use eddy current testing for detecting metal targets, which solves the problem of dichotomous distinction, that is a problem of splitting (or set) into two parts (subsets): black or colored target.The target distinction is performed by a threshold level of the received signal. However, this approach does not allow to identify the type of target, if two samples of different metals are nearby.To overcome the above described limitations we propose another way of distinction based on the use of spectral analysis, which occurs in the metal detector antenna by Foucault current. We show that the problem of dichotomous distinction can be solved in just a measurement of width and area by the envelope of amplitude spectrum (hereinafter spectrum) of the received signal.In this regard the laboratory model using eddy current metal detector will combat withdrawal from two samples – steel and copper, located along and calculate its range.The task of distinguishing between metal targets reduced to determining the hit spectra of reference samples obtained spectrum. The ratio between the areas is measured and reference spectra indicates the percentage of specific metals (e.g. two identical samples of different metals lying side by side).Signal processing is performed by specially designed program that compares two spectra along posted samples of black and colored metals with base.

LCP-Net: A local context-perception deep neural network for medical image segmentation

Presents a new approach in the field of CAD-based vision starting up from the retrieval of the data from a CAD system. A viewer-centered approach is used to generate a set of normalized views. They serve as a basis for an optimal selection of features. The aspect idea is used for grouping the values of the features into aspect-trees. The sophisticated interpretation of the trees proves the ability of the image interpretation system to recognize complex objects in different environments robustly and efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>