As the core power source of the hydraulic transmission system, hydraulic pump has been widely used in various industrial machinery and national defense equipment. It is of great significance to explore the monitoring of the health status of the hydraulic pump. It is also necessary to extract the effective components in condition monitoring of the hydraulic pump. In this work, based on fast empirical mode decomposition (FEMD) and relative entropy, a novel method is proposed for extracting the effective components of the signal. The original signal can be automatically separated by FEMD, and the useful components of the signal can be obtained via the measurement of relative entropy. Through the validation of the numerical experiment and measured data, the results indicate that the method presents good ability in the useful component extraction for signals with multi-frequency vibration. It provides an effective solution for the reduction of the interference of useless signals, including the direct current component and noise. The desired useful signals are also accurately reconstructed.

Effective component extraction for hydraulic pump pressure signal based on fast empirical mode decomposition and relative entropy

Indoor location of interference source with fuzzy C clustering cruising AP based on double fingerprint database

Renyi entropy as a generalization of the Shannon entropy allows for different averaging of probabilities of a control parameter α. This paper gives a new perspective of the Kalman filter from the Renyi entropy. Firstly, the Renyi entropy is employed to measure the uncertainty of the multivariate Gaussian probability density function. Then, we calculate the temporal derivative of the Renyi entropy of the Kalman filter’s mean square error matrix, which will be minimized to obtain the Kalman filter’s gain. Moreover, the continuous Kalman filter approaches a steady state when the temporal derivative of the Renyi entropy is equal to zero, which means that the Renyi entropy will keep stable. As the temporal derivative of the Renyi entropy is independent of parameter α and is the same as the temporal derivative of the Shannon entropy, the result is the same as for Shannon entropy. Finally, an example of an experiment of falling body tracking by radar using an unscented Kalman filter (UKF) in noisy conditions and a loosely coupled navigation experiment are performed to demonstrate the effectiveness of the conclusion.

https://www.mdpi.com/1099-4300/22/9/982/pdf

A Novel Perspective of the Kalman Filter from the Rényi Entropy

In order to achieve large-scale positioning by the ground positioning base station network, the mode of co-address and co-frequency broadcasting between the positioning base station and the existing communication base station can be considered. Under this circumstance, in order to meet the need of ultra-dense networking of integrated communication and navigation system, an optimized cellular positioning networking scheme is proposed in this paper. In the outdoor complex transmission environment, the boundary conditions that the proposed cellular network model needs to meet are analysed for the TC-OFDM system. Besides, based on the greedy algorithm of quadratic optimization, the spreading codes with better performance are selected for the reuse of cellular network modules. Theoretical analysis and simulation results show that the optimized cellular network model can well meet the large-scale positioning requirements of integrated communication and navigation system, and the selected spreading code has better performance.

The Base Stations’ Networking Scheme and Spreading Code Optimization Strategy of TC-OFDM

In the complex environment of the city, the visible Beidou satellites are distributed along the road in a strip, which causes a significant decline in positioning performance. The high-density deployment of 5G systems provides an opportunity to improve the geometric distribution of satellites. However, due to the difference in the noise of the pseudo-range measurement between systems, there exists a large positioning error in the traditional solution to the least squares. As a result, this paper introduces the elevation prior determination weight and Helmert variance component estimation model to assign weights to Beidou and 5G systems to improve joint positioning accuracy. The simulation results show that compared to the equal weight model, the PDOP value is reduced by 11.76%, the circular probability error is reduced by 6.8%, and the positioning errors in the three directions of East North Down are reduced by 0.8%, 36.6% and 4.6% respectively. At the same time, the positioning stability has been improved.

Research on the Weight Distribution of Helmert Variance Component Estimation in Beidou + 5G Integrated Positioning

The current single data source positioning navigation systems cannot meet the high precision and high reliability required for indoor/outdoor positioning service In this study, based on an inertial navigation system, time-and-code division-orthogonal frequency division multiplexing ranging technology and a global navigation satellite system, a relative entropy-based Kalman multi-source fusion positioning model is developed First, multi-source numerical observation data are filtered, and the outliers are processed in data layers to improve data source reliability and to extract stable observation data Next, the degree of the multi-source data coupling is quantified in an information layer to analyze the multi-source information coupling degree and to develop a coupling degree factor and a Kalman fusion positioning model for multi-source heterogeneous information Tests show that this method significantly improves system positioning, navigation stability and positioning precision

Qingqing Xu

Papers

Relative entropy-based Kalman filter for seamless indoor/outdoor multi-source fusion positioning with INS/TC-OFDM/GNSS