Filtering and Estimation of Vehicular Dead Reckoning System Based on Hopfield Neural Network

The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.

Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.

Recurrent neural network for vehicle dead-reckoning

For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.

An enhanced dead reckoning algorithm with hybrid extrapolation models(AisaSim 2016)

The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive simulation.The proposed algorithm perform extrapolation using a number of candidate predictors.Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories.The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.

卡尔曼滤波实现车载GPS/DR组合导航

陆地车辆是在二维平面内运动的,而在车载GPS接收机和DR组合装置中,测量值是在不同的坐标系中得到的,通过对坐标系转换和测量误差分析得到组合系统的整体状态方程和测量方程,由于这些状态方程和测量方程都是关于状态的非线性函数,用广义卡尔曼滤波器来实现GPS/DR组合导航的实时求解。最后在行驶车辆上采集数据并进行仿真试验,结果表明这种组合系统能够大大提高系统实时定位精度和容错能力。

An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion

In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.

From Coarse to Fine:Two-Stage Indoor Localization with Multisensor Fusion

Increasing attention has been paid to high-precision indoor localization in dense urban and indoor environments.Previous studies have shown single indoor localization methods based on WiFi fingerprints,surveillance cameras or Pedestrian Dead Reckoning(PDR)are restricted by low accuracy,limited tracking region,and accumulative error,etc.,and some defects can be resolved with more labor costs or special scenes.However,requesting more additional information and extra user constraints is costly and rarely applicable.In this paper,a two-stage indoor localization system is presented,integrating WiFi fingerprints,the vision of surveillance cameras,and PDR(the system abbreviated as iWVP).A coarse location using WiFi fingerprints is done advanced,and then an accurate location by fusing data from surveillance cameras and the IMU sensors is obtained.iWVP uses a matching algorithm based on motion sequences to confirm the identity of pedestrians,enhancing output accuracy and avoiding corresponding drawbacks of each subsystem.The experimental results show that the iWVP achieves high accuracy with an average position error of 4.61 cm,which can effectively track pedestrians in multiple regions in complex and dynamic indoor environments.

一种联合WiFi信息和PDR算法的智能手机室内定位方法

受室内复杂环境的影响,实现满足各类室内定位需求、准确实时的定位仍有很大的挑战性。提出了一种联合WiFi信息和行人航位推算(pedestrian dead reckoning,PDR)算法的智能手机室内定位方法,并给出了其原理和流程。实验结果表明,该方法适应性较强、定位结果准确。

A regression model-based method for indoor positioning with compound location fingerprints

This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.

Study of synthetic aperture radar and automatic identification system for ship target detection

In the past,the main goals of Synthetic Aperture Radar(SAR)systems were the study of the interaction of electromagnetic waves with the earth surface.Recently,the development of multi-channel SAR systems has enabled the development of more sophisticated techniques for the surveillance activities.SAR is the most efficient instrument,which provides high-resolution data for wide ocean area surveillance under all weather conditions.The intrinsic capability of this instrument is to provide a quick view of the oceanic surface features such as vessels,waves and currents,oil spills,laver facilities and wind fields.The ship detection or recognition is achieved in two steps:the first step is to identify the target in SAR images of a busy traffic,which corresponds to Automatic Identification System(AIS)signals by the“dead-reckoning(DR)position”,and the second step is to estimate the position,size and speed of the ship from SAR images and compare these results with the AIS“true”data.This paper presents the fundamentals of SAR and its integration with the AIS data for the ship target detection.

Indoor navigation: state of the art and future trends

This paper reviews the state of the art and future trends of indoor Positioning,Localization,and Navigation(PLAN).It covers the requirements,the main players,sensors,and techniques for indoor PLAN.Other than the navigation sensors such as Inertial Navigation System(INS)and Global Navigation Satellite System(GNSS),the environmental-perception sensors such as High-Definition map(HD map),Light Detection and Ranging(LiDAR),camera,the fifth generation of mobile network communication technology(5G),and Internet-of-Things(IoT)signals are becoming important aiding sensors for PLAN.The PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors,multi-platform/multi-device/multi-sensor information fusion,self-learning systems,and the integration with artificial intelligence,5G,IoT,and edge/fog computing.

DR-model-based estimation algorithm for NCS

A novel estimation scheme based on dead reckoning(DR)model for networked control system(NCS)is proposed in this paper.Both the detailed DR estimation algorithm and the stability analysis of the system are given.By using DR estimation of the state,the effect of communication delays is overcome.This makes a controller designed without considering delays still applicable in NCS.Moreover,the scheme can effectively solve the problem of data packet loss or timeout.