Statistical characterization of the 2.45 GHz propagation channel aboard trains

The propagation channel aboard trains is investigated with reference to the propagation path loss within cars, the delay spread and the coherence bandwidth. Results show that the path loss exponent is slightly smaller than in free space, possibly due to reflections by metal walls, and that it does not depend significantly on the position of transmitter and receiver. The delay spread and coherence bandwidth depend on both the polarization and distance between transmitter and receiver while the effect of interaction is not statistically significant. The best fit for both delay spread\u2019s and coherence bandwidth\u2019s experimental distribution is also investigated. Results show that it does not always match models suggested in the literature and that the fit changes with the values of the input parameters. Finally, the functional law between coherence bandwidth and delay spread is determined. Results typically match expectations although the specific measurement configuration effects the model parameters

Prototype of a Dsp-Based Instrument for In-Service Wireless Transmitter Power Measurement

Abstract A prototype of a DSP-based instrument for in-service transmitter power measurements is presented. The instrument implements a signal-selective algorithm for power measurements that is suitable for use in wireless environments, where possible uncontrolled interfering sources are present in the radio channel and are overlapped to the signal emitted by the transmitter under test, possibly in both time and frequency domain. The measurement method exploits the principles of cyclic spectral analysis, which are briefly recalled in the paper. Potentialities, as well as limitations of the prototype use are discussed, and the results of experiments with both modulated and unmodulated interfering sources are presented

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters

Ultrasonic time-of-flight estimation through unscented Kalman filter

This paper deals with distance or level measurements based on ultrasonic time-of-fight estimation. Moving from a past experience concerning the proposal of a method based on discrete extended Kalman filter (DEKF) to overcome some limitations of already available ultrasonic-based techniques, a new digital signal processing method capable of granting further improvements is presented. The method is based on the unscented Kalman filter (UKF), which is a new extension of the Kalman filter theory mandated to face some DEKF problems, mainly due to its inherent linearization approach. To this aim, UKF is applied to the acquired ultrasonic signal in order to estimate the returned echo envelope as well as to locate its onset more accurately. After describing key features and implementation issues of the new method, the results obtained in a number of tests on simulated and actual ultrasonic signals, which assess its reliability and effectiveness as well as advantages with respect to the previous one, are given

A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; therefore, motor diagnostics is an issue that assumes great importance. To prevent their failures and face the considered service outages in a timely manner, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on an artificial neural network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminarily validated on a set of 28 electric motors, and its performance is compared with common state-of-the-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98% in identifying anomalous conditions of three-phase asynchronous motors