Jianping Xing

Distributed Grid-Based Localization Algorithm for Mobile Wireless Sensor Networks

Localization is an essential supporting technology of wireless sensor networks (WSNs). Most of traditional localization algorithms are proposed for static WSNs. There are only a few algorithms proposed for mobile wireless sensor networks (MWSNs). In this paper, we propose a range-free localization algorithm for MWSNs, named distributed grid-based localization algorithm (DGL). In this algorithm, anchor nodes can increase their transmitting power, that is to say, they can change their communication range. Each sensor node can establish rectangular coordinate system itself, and then divide coordinate system into square grids. Simulation results demonstrate this algorithm outperforms other well-known localization algorithms.

An Improved FastIMM Algorithm Based on α-β and α-β-γ Filters

In this paper, an Improved FastIMM algorithm is presented by optimizing the acceleration gain factor γ and tracking indicesλof the α-β and α-β-γ filters. In this algorithm, a better acceleration gain factor γ is selected to improve the FastIMM filter’s performance in target tracking. Besides, to get the appropriate ranges for tracking indices of the α-β and α-β-γ filters, numerical simulations are carried out and the Root Mean Square Error (RMSE) of the position and velocity are analyzed. Simulation results show that the Improved FastIMM has much higher tracking accuracy while keeping the same computational burden.

DISTRIBUTION MODEL OF URBAN BUS TRAVEL TIME WITH BUS LANE

In this paper, based on the previous research achievements of travel time prediction, and taking no.117 bus in Jinan Shandong province as the research object, a lot of research has been done. And the result reveals the change rule of travel time in different time period with bus lane, such as workday, non-workday, early slack, morning rush hour and flat peak. Meanwhile, on the basis of comparative analysis and summarizing, bus travel time distribution model was established, which provide reference for Bus scheduling personnel to dispatch bus operating vehicles scientifically and reasonably.

The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization

The diagonal-matrix-weight IMM (DIMM) algorithm can solve the IMM algorithm confusions of probability density functions (PDFs) and probability masses of stochastic process. However, the DIMM algorithm may generate calculating divergence, caused by the fact that the state error covariance matrix is repeatedly applied to calculate the inverse matrix. A new method, LDU factorization diagonal-matrix-weight IMM (LDU-DIMM) algorithm, is proposed in this paper to solve the factorization of asymmetric matrix error covariance of DIMM algorithm. Experiment results verify the effectiveness of the proposed algorithm.

Impact Analysis of Environment Factors on Iterative Calculation of Secondary Grid Division (ICSGD) Localization Scheme for Wireless Sensor Networks

Localization scheme is a fundamental and essential issue for wireless sensor networks (WSNs). Iterative calculation of secondary grid division (ICSGD) localization scheme could solve the inconsistency between calculation amount and location accuracy. The performance of the localization scheme was evaluated in a series of simulations performed using MATLAB software and was compared to the grid division localization scheme (GDLS). The simulation results demonstrated that the scheme outperformed the GDLS in terms of higher location accuracy, and lower location amount.

Double-Sources Dijkstra Algorithm within Typical Urban Road Networks

A Double-Sources shortest path algorithm for urban road networks is proposed in this paper. In typical urban road networks, the probability that the ratio of the shortest path length to the Euclidean distance denoted by |SD| between source station and destination station is smaller than 1.414, is larger than 95%. Based on Dijkstra algorithm and the characteristics of the typical urban road networks, this algorithm starts at searching for the shortest path from the source station and destination station respectively and simultaneously and ends at having found all stations which are less than 0.702|SD| far from the source station or destination station. Compared to the single-source Dijkstra algorithm, theory analysis and experimental results both show that the algorithm can great reduce the time-complexity, especially on condition that stations in urban road networks uniformly distribute.

Research on robust GNSS vehicle three-dimensional tracking method for urban elevated road networks

The Kalman filtering(KF) has been implemented as the primary scheme for many land vehicle navigation and positioning applications. However, it has been reported that the KF-based techniques have limitations that it assumes the noise is Gaussian white and the system model must be known exactly. Due to the complicated vehicle tracking environment in urban area(signal disappear, attenuation or reflection) and diverse vehicle motion(uniform or accelerated), The VNS inevitably exits stochastic uncertainties whose statistical property can not be priori known. This makes great difficulties in tracking vehicle robustly. In this paper, robust GNSS vehicle three-dimensional tracking method for urban elevated road networks is investigated. By exploring the geometry of the vehicle tracking problem, the three-dimensional vehicle tracking problem is formulated to one-dimensional target trajectory tracking problem. Accounting for modeling uncertainties and unpredictable disturbances problem, via robust H∞ filtering algorithm with Stochastic Uncertainties that we have developed in another paper, a three-dimensional vehicle tracking algorithm for urban elevated road networks is proposed. The experiment results confirm the effectiveness of the proposed method by comparing with the Kalman filter tracking method using the measured GNSS data.

Dynamic Public Transport Passenger Flow Forecast Based on IMM Method

In this paper, an dynamic urban public transport passenger flow forecasting approach is proposed based on interact multiple model (IMM) method. The dynamic approach (DA) maximizes useful information content by assembling knowledge from correlate time sequences, and making full use of historical and real-time passenger flow data. The dynamic approach is accomplished as follows: By analyzing the source data, three correlate times sequences are constructed. The auto-regression (AR), autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) models are selected to give predictions of the three correlate time sequence. The output of the dynamic IMM serves as the final prediction using the results from the three models. To assess the performance of different approaches, moving average, exponential smoothing, artificial neural network, ARIMA and the proposed dynamic approach are applied to the real passenger flow prediction. The results suggest that the DA can obtain a more accurate prediction than the other approaches.