Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning

Radio Location of Partial Discharge Sources: A Support Vector Regression Approach

Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity

Fault tolerant target localization and tracking in wireless sensor networks using binary data

This paper investigates the use of a sensor network for localizing and tracking a moving target using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate a number of misbehaving nodes. SNAP (Subtract on Negative Add on Positive), is a simple event localization algorithm designed for WSNs applications that exhibits this fault tolerant behavior. The main contribution of this paper is to combine the decentralized implementation of SNAP with Kalman Filter techniques for tracking the movement of a target. This efficient tracking procedure provides fairly accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations. © 2011 IEEE

Fault tolerant positioning using WLAN signal strength fingerprints

Accurate and reliable location estimates using wireless networks are important for enabling indoor location oriented services and applications, such as in-building guidance and asset tracking. Providing adequate level of accuracy in case of faults or attacks to the positioning system is equally significant, thus our main interest is on the fault tolerance of positioning methods, rather than the absolute accuracy in the fault-free case. We introduce several fault models to capture the effect of failures in the wireless infrastructure or malicious attacks and discuss how these models can simulate the corruption of signal strength values during positioning. The models are used to investigate the fault tolerance of positioning methods and evaluate them in terms of their performance degradation as the percentage of corrupted signal strength measurements increases. Experimental results using our fault models are also presented. © IEEE

Phase Interpolator with Improved Linearity

An analog phase interpolator with improved step linearity is presented in this paper. The linearity is improved by setting the time constant of the output nodes in suitable value and by employing a fine trimming technique. The performance and the improved linearity have been verified with post-layout simulations using a well-established CMOS 65 nm technology and transistors with standard threshold voltages. The clock frequency is at 2.5 GHz and the core voltage supply at 1.2 V. Its low phase noise makes the circuit suitable for high-speed systems where low jitter performance is required. © 2015, Springer Science+Business Media New York