Pedestrian Indoor Localization Method Based on Integrated Particle Filter

The human respiratory movement seriously affects the precision of the robotized radiotherapy treatment for lung cancer. In order to reduce the influence of respiratory movement, the most effective method is real-time tracking of respiratory tumor motion, which adopts tracking algorithm to predict the location of the tumor, and adjust the treatment beam to catch the motion of the target. This paper utilizes the particle filter as tracking algorithm to predict the tumor motion trajectory. Firstly, the principle and process of particle filter algorithm are introduced; Secondly, the probability correlation model based on UT transform are proposed as the observation model of the particle filter; Finally, the conventional particle filter and the UT transform modeling are used to predict the location of the tumor with respiratory motion. The effectiveness and accuracy of particle filter algorithm based on UT transform is verified by error analysis. The experiment results show that the particle filter algorithm based on UT transform has stronger robustness and higher prediction precision compared with the linear estimation and the traditional particle filter, so it can meet the requirements of real-time tumor tracking for radiotherapy robots.

Particle filter and Viterbi tracking algorithm are two representative sensor fusion methods to estimate a location of a mobile device by using various signals such as Wi-Fi, Bluetooth, magnetic fields and sensors like 3-axis accelerometer and gyroscope in indoor environments. This paper measures and compares the performance of the two sensor fusion methods on two paths in the first floor of KI building, KAIST. One path is a random walk and another is a rectangle path circumventing the area. On the rectangle path, the two methods showed a similar performance to each other, approximately 2m average error distance. On the other hand, on the random walk path, the Viterbi tracking algorithm significantly outperformed the particle filter in accuracy. This indicates that the choice of sensor fusion method influences a little to positioning accuracy when pedestrian dead reckoning (PDR) is working properly. However, when the PDR is not properly working, the choice of sensor fusion method greatly influences to positioning accuracy. In our test, the Viterbi tracking algorithm revealed to be much more resilient to PDR errors than particle filters.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with<i> ε- greedy</i> exploration εPSO), is used to tackle the object tracking. In the modified εPSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The εPSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new εPSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our εPSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.

In order to solve the problem of particle dilution in traditional particle filter (PF) estimation of Ni-Cd Alkaline battery state of charge (SOC), a bat algorithm optimized particle filter was proposed to estimate SOC. The particles are represented as bat individuals, and the predatory process of bat population is simulated to solve the problem of particle dilution in particle filter technology; the theoretical model of battery state space is constructed by combining the second-order Thevenin battery model, and the related parameters of battery are identified; the SOC is estimated by using BA-PF algorithm and standard PF algorithm under pulse current condition. The experimental results show that, compared with the traditional PF algorithm, the SOC estimation accuracy of Ni-Cd alkaline battery based on BA-PF is less than 2%, and it has good adaptability and stability for nonlinear and non-Gaussian characteristics of Ni-Cd alkaline battery.

Automatic lecture recording is an appealing alternative approach to manually recording lectures in the process of online course making as it can to a large extent save labor cost. The key of the automatic recording system is lecturer tracking, and the existing automatic tracking methods tend to lose the target in the case of lecturer’s rapid movement. This article proposes a lecturer tracking system based on MobileNet-SSD face detection and Pedestrian Dead Reckoning (PDR) technology to solve this problem. First, the particle filter algorithm is used to fuse the PDR information with the rotation angle information of the Pan-Tilt camera, which can improve the accuracy of detection under the tracking process. In addition, to improve face detection performance on the edge side, we utilize the OpenVINO toolkit to optimize the inference speed of the Convolutional Neural Networks (CNNs) before deploying the model. Further, when the lecturer is beyond the camera’s field of view, the PDR auxiliary module is enabled to capture the object automatically. We built the entire lecture recording system from scratch and performed the experiments in the real lectures. The experimental results show that our system outperforms the systems without a PDR module in terms of the accuracy and robustness.

liuyujiao2005@126.com Abstract Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing. The detection of the yarn quality is very important for cotton production. Yarn strength is the reflection of inner quality of yarn and is the necessary condition of yarn's processing performance and end-use. The unit of single-yarn strength is Newton (N) per centimeter (CN). By stretching testing with a single-yarn strength tester, the yarn's characteristic parameters will be obtained, and the physical index of breaking strength, breaking elongation, fracture strength, fracture time and strength tensile curves can be determined, so the quality of yarn will be ascertained (1) . This paper majors in the research of the parameter of the single-yarn strength of yarn. The strength of yarn will increase with increasing within a certain twist; however the strength of yarn will decrease when the twist exceeds the critical value. The relationship between yarn elongation and tension is often nonlinear. When the yarn quality is tested by single-yarn strength tester, the interference noise of test environment in the industrial field is non-Gaussian. So the processing of single-yarn strength testing data is a typical nonlinear and non-Gaussian filtering problem. The traditional digital filtering methods normally only filter out the caused by single reason and can't achieve satisfactory results for complex noise. In order to solve this problem, a large number of nonlinear recursive filtering algorithm such as extended Kalman filtering (EKF), the modified gain Kalman filter (MGEKF), U Kalman filter (UKF) algorithm come forth in recent years, and these filters are based on particular assumption to ensure the optimality. The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields (2-11) . Now, a new sequential Monte Carlo particle filter algorithm based on Bayesian principles has been paid more attention. The particle filter technology realize the recursive Bayesian filter with non- parametric Monte Carlo simulation method, which can fit to any state space model and the nonlinear systems which can't be expressed by traditional Kalman filter, and its accuracy approach the accuracy of optimal estimation (12) . This paper places emphasis on introducing the principle and method of the particle filter, and the application of the nonlinear data processing.

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.

A Map aided Pedestrian Dead Reckoning (PDR) algorithm is proposed to mitigate the drift errors and step detection limitations of pedestrian dead reckoning algorithm with handheld sensors in indoor and outdoor spaces. Specific to this context is the changing lever-arm between the handheld device and the pedestrian center of mass that introduces a misalignment between the inertial sensors and the walking directions. To address these challenges, an adaptive routing graph is built based on possible pedestrian's motions, which depend on personal mobility profile and surroundings. An adaptive decision process is also developed to fuse map data with GNSS positions and PDR outputs in a particle filter. The performance is assessed with 1km walk experiments. Main contributions are (1) the calibration of the PDR step length model using both GNSS and map data during straight line travels with miss/over-detected steps modeled by the particle filter; (2) the estimation of angular misalignment between the walking and the handheld unit pointing directions in geometrically constrained areas; (3) a dynamic choice of opportune periods and measurements to calibrate the PDR outputs and improve the positioning process.

In order to improve the indoor localization accuracy of the security patrol robot, a Ultra-Wide Band (UWB), wheel odometry and strapdown inertial navigation system three-sensor fusion localization algorithm based on particle filter was proposed to solve the problem of low localization accuracy of single sensor. Firstly, the algorithm principle of Bayesian filter and particle filter is discussed, and the filtering process is derived by the formula. Then the fusion localization of strapdown inertial navigation system and UWB, wheel odometry and UWB, strapdown inertial navigation system and wheel odometry and UWB are studied respectively. The results of computer simulation show that the accuracy of strapdown inertial navigation system, wheel odometry and UWB three-sensor fusion localization algorithm is obviously higher than other localization methods.KeywordsRobot localizationUltra-wide bandStrapdown inertial navigationWheel odometryParticle filter

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.

A novel infrared moving object tracking method based on particle filter and Mean Shift algorithm was presented.Firstly,it utilized the intensity distribution to represent infrared object,and constructed the observation probability model by statistical histogram.Then,Mean Shift algorithm was incorporated into the propagating process of particle filter,which induced particles distributing within the local area of observation.Compared to the conventional particle filter,the proposed method used much fewer particles to maintain the multi-mode distribution,and overcame the degeneration problem effectively.Experimental results on sequential images show that our method can track steadily when the object moves fast or is occluded,the overall performance of the proposed method is better than traditional particle filter algorithm.

Particle filter is commonly used in various indoor positioning schemes, but sample impoverishment and weight degradation generally exist in particle filter. To solve this problem, this article uses the firefly algorithm to optimize particle filter. Using the global optimal value in the particle swarm to guide the remaining particles to update their positions, the particles tend to move to the high likelihood area, which can more accurately describe the true state of the target observation. At the same time, to avoid particles falling into local optimum and oscillating repeatedly at the extreme point, the mathematical model of the firefly algorithm is reconstructed in this article, which is named as adaptive optimization firefly algorithm (AOFA). The experimental results show that the positioning scheme can provide the positioning accuracy with an average error of less than 0.5 m. Compared with the traditional particle filter, the positioning accuracy is improved by 120%.

The INS/GPS navigation system is obvious nonlinear under the large initial condition errors. To tackle the accuracy of integrated navigation under nonlinear model, a improved particle filter named cubature particle filter (CPF) is applied to INS/GPS integrated navigation. For this, the nonlinear state model based on the platform misalignment angle and the observation model described by the velocity error and position error is established. The CPF that utilizes the current observation information in the prior updating phase develops the importance proposal distribution function by cubature kalman filter (CKF) to relieve particle sample degeneracy and impoverishment, so the improved particle algorithm significantly improves the accuracy of the integrated navigation system than traditional particle filter (PF).