Accurate and robust positioning in multipath environments can enable many applications, such as search-and-rescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian process regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.

In recent years, the use of wireless systems in industrial applications has experienced spectacular growth. Unfortunately, industrial environments often present impulsive noise which degrades the reliability of wireless systems. OFDM is an enhanced technology used in industrial communication to monitor the work and movement of employees using high quality video. However, OFDM is sensitive to high amplitude impulsive noise because the noise energy spreads among all OFDM sub-carriers. This paper proposes a receiver structure consisting of two stages: a detector stage combining Fisher’s Quadratic discriminant and Gaussian Hypothesis techniques, and a suppression stage optimized by setting well defined thresholds. The receiver structure has been tested by simulations and measurements providing an increment in the probability of detection and improving the system performance.

A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Linköping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on time-of-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for range-based positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior.

Impulsive noise is a major source of degradation in industrial communications. Orthogonal frequency-division multiplexing (OFDM) is an extended technique used in many industrial communications, however the performance of OFDM systems is reduced under an impulsive noise source. To increase the system performance, impulsive noise detection and suppression techniques can be designed in the communication system. OFDM has high levels of peak-to-average power ratio (PAPR), thus PAPR reduction techniques, such as selected mapping (SLM), are implemented in OFDM systems. This paper proposes an impulsive noise detection exploiting the statistical properties of the OFDM envelope when applying SLM. The proposed detection technique increases the probability of detection and improves the BER of the communication system compared to other impulsive detection techniques.

Accurate positioning in harsh environments can enable many application, such as search-and-rescue in emergency situations. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, it still faces a problem in non-line-of-sight (NLOS) environments, in which range estimates based on time-of-arrival (TOA) are positively biased. There are many techniques that try to address this problem, mainly based on NLOS identification and NLOS error mitigation. However, these techniques do not exploit all available information from the UWB channel impulse response. In this paper, we propose a novel ranging technique based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto nonlinear orthogonal high-dimensional space, and a subset of these projections is then used for ranging. We tested this technique using UWB measurements obtained in a basement tunnel of Linköping university, and found that it provides much better ranging performance comparing with standard techniques based on PCA and TOA. 

This paper presents a preliminary experimental analysis of the ranging techniques using UWB measurements obtained in a tunnel at Linköping university. According to our results, range estimates based on time-of-arrival are very accurate in LOS and all NLOS scenarios with thin obstacles. However, there is a relatively large bias when a NLOS is caused by a tunnel wall.

The Ricean K-factor and antenna diversity properties for indoor industrial environments have been characterized for 433 and 868 MHz. The high amount of metallic structures gives a multipath environment that heavily differs from other environments e.g. indoor office environments. The results show that low correlation between receiving antennas can be achieved for shorter antenna distances than in other environments.

Wireless solutions are rapidly growing in machine-to-machine communications in industrial environments. These environments provide challenging conditions in terms of radio wave propagation as well as electromagnetic interference. In this article, results from the characterization of radio channel properties are summarized in order to provide some guidelines for the choice of wireless solutions in industrial environments. In conclusion, it is essential to know the sensitivity of industrial processes to time delay in data transfer. Furthermore, it is important to be aware of the radio interference environment and the manner in which different wireless technologies react upon interference. These steps will minimize the risk of unforeseen expensive disturbances in industrial processes.

Wireless communication is expected to improve the safety and the productivity in underground tunnels for industrial use. However the multiple shapes and structures of tunnels affect wireless communication characteristics in terms of signal propagation which is significantly different from terrestrial environments. This paper presents comprehensive broadband measurements and simulations of multipath propagation and path loss in two underground environments. The results can be used in the development of new communication systems in tunnels that provide industrial services.