Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System

Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness.

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

Research on the method of travel area clustering of urban public transport based on Sage-Husa adaptive filter and improved DBSCAN algorithm

The travel trajectory data of mobile intelligent terminal users are characterized by clutter, incompleteness, noise, fuzzy randomness. The accuracy of original data is an essential prerequisite for better results of trajectory data mining. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most effective trajectory data mining methods, but the selection of input parameters often limits it. The Sage-Husa adaptive filtering algorithm effectively controls the error range of mobile phone GPS data, which can meet the positioning accuracy requirements for DBSCAN spatial clustering having the advantages of low cost and convenient use. Then, a novel cluster validity index was proposed based on the internal and external duty cycle to balance the influence of the distance within-cluster, the distance between clusters, and the number of coordinate points in the process of clustering. The index can automatically choose input parameters of density clustering, and the effective clustering can be formed on different data sets. The optimized clustering method can be applied to the in-depth analysis and mining of traveler behavior trajectories. Experiments show that the Sage -Husa adaptive filtering algorithm proposed further improves the positioning accuracy of GPS, which is 17.34% and 15.24% higher eastward and northward, 14.25%, and 18.17% higher in 2D and 3D dimensions, respectively. The number of noise points is significantly reduced. At the same time, compared with the traditional validity index, the evaluation index based on the duty cycle proposed can optimize the input parameters and obtain better clustering results of traveler location information.

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

Forecasting maximum and minimum temperature over airports with special relevance to aviation in flight planning

Forecasting of maximum temperature and minimum temperature for aviation and non-aviation purpose has been attempted through auto regression and by employing the method of adaptive filtering and Kalman filtering during the hot weather season (March to May) over Madras. The filtering techniques have been outlined and the results are compared with the method of climatology and persistence. The Kalman filter using the model output of adaptive filtering. forecasts well the day-to-day variability of maximum and minimum temperature during hot weather season over Madras with an efficiency close to 90%. As the model performs reasonably well over Madras. a coastal station. the same has been tried over Trichy (300 km southwest of Madras), an inland airport station in Tamilnadu to ascertain its efficacy. The efficiency is better than 90% in predicting maximum and minimum temperature within an accuracy of 2°C).