This paper presents a novel cognitive and cooperative tracking (CCT) approach based on extended Kalman filter (EKF) to localize mobile nodes in wireless networks. The proposed algorithm shows three important features: energy efficient, cognitive and cooperative. More specifically, the tracking algorithm adaptively adjusts the transmission power to optimize the energy consumption while meeting the required localization accuracy Paimposed by a generic application. Moreover, it adopts a self-learning scheme to track the time-variant environment's characteristics (e.g., range measurement noise) and use this knowledge to improve tracking performance. Finally, the algorithm exploits the cooperation among unknown nodes that leads to further improved performance and reduced power consumption. Simulation results show that the proposed CCT approach is able to improve positioning performance and meet the required accuracy Pawhile energy consumption is optimized.

This work analyzes the impact of medium access control (MAC) layer on the performance of real-time cooperative positioning in ad-hoc mobile wireless networks. The probability mass function (pmf) of the latency introduced by the MAC layer is here derived to assess the influence of MAC parameters on the latency and then operate on those to improve the positioning performance while reducing the number of position estimation iterations.

This paper presents a novel ‘Energy Efficient Tracking’ (EET) algorithm that tries to meet the localization accuracy requirement, Pa, imposed by a generic location-based application while the energy consumption for ranging and communication is optimized. More specifically, given the set of range measurements performed by a mobile node with respect to its neighboring anchors (i.e., nodes whose exact positions are known a priori), the proposed EET algorithm selects, on the basis of the Cramér-Rao lower bound (CRLB), the closest set of anchors such that the resulting positioning error is lower than the required Pa. At the same time, the EET approach minimizes the average energy consumption by dynamically adapting the transmitted power, Pt, to be used in the next positioning estimation step. Based on time-of-arrival (TOA) estimation with ultra-wide bandwidth (UWB) nodes, the EET approach uses an Extended Kalman Filter (EKF) to track the mobile node's position. Simulation results show that the EET algorithm effectively reduces the energy consumption while the achieved positioning performance is almost identical to the one achieved by a classical EKF with fixed Pt, named ‘Energy Fixed Tracking’ (EFT) algorithm.

We propose a novel hybrid GNSS-terrestrial localization algorithm based on particle filter that fuses ranging data from both satellites and terrestrial receivers. The proposed positioning approach, named hybrid-cooperative particle filter (HCPF), is fully distributed and allows both increased positioning availability and accuracy compared to GNSS-only localization in challenged scenarios. Moreover, simulation results based on a realistic indoor scenario show that the proposed solution outperforms several state of the art algorithms such as unscented Kalman filter and an approach based on belief propagation.

This paper presents a wireless local area network (WLAN)-based real time system for indoors and outdoors vehicle localization. The proposed solution uses a neural network trained with a map of received power fingerprints from WLAN Access Points (APs) surrounding the vehicle. The practical implementation of the system is described and results from an outdoor experimental testbed are analyzed to address system performance in terms of calibration, estimation complexity, and location accuracy. Pre- and post-processing approaches aimed at improving system accuracy are also discussed.

The increasing availability of ubiquitous, small, low-cost devices using wireless communications to build wireless sensor networks calls for autonomous solutions and algorithms capable of calculating the location where the information is gathered, processed, used. The requirements are particularly strict when sensor nodes are mobile; in fact, mobile applications demand more accurate locating and real time tracking, with limited impact on hardware complexity, network load and latency. Despite of remarkable research efforts put into this field by the scientific community, a common unique solution has not been adopted yet, due to the great variety of scenarios and application requirements. This paper focuses on the design and performance evaluation of Kalman filters for tracking a mobile target moving at low dynamics and for smoothing range estimations from noisy measurements. Some adaptive techniques to self-tune the filter and estimate the propagation model parameters are presented. One among the most promising adaptive algorithm presented is implemented in a real-life wireless sensor network test-bed including a mobile node. The resulting experimental results illustrate the benefits of this approach with respect to traditional multilateration based on least mean squares estimators.

Moore at al. presented in [1] a cluster-based distributed algorithm for localizing wireless sensor network (WSN) nodes in presence of range measurement noise. They introduced the notion of "robust quadrilaterals" as a means of avoiding flip ambiguities that often corrupt the results of localization algorithm. This paper1proposes an enhancement of the algorithm presented in [1] that uses a new quadrilateral robustness test. A second novel distributed algorithm for anchor-less localization of WSN nodes in presence of range measurements noise is presented. This second algorithm reduces the total computational complexity with respect to the first two algorithms by a factor at least equal to the number of nodes in the WSN. It is made of two main steps: the first one, where a single cluster of nodes is localized, and a second one where the other unknown nodes are estimated by successive trilaterations. The original algorithm [1] and the two novel algorithms proposed in this paper are compared by means of simulations in terms of complexity, convergence and location accuracy performance.

In this paper we propose a semi-random technique for the generation of a class of eIRA codes (a popular class of irregular LDPC codes that can be encoded in linear time) suited to partially parallel decoder implementations. The suggested technique tries to avoid both low-weight codewords and nearcodewords, which limit the performance of the belief propagation decoder. Its effectiveness is verified by comparison with literature results. The obtained codes are versatile, in terms of code-rate and block length, and they are characterized by a low error floor.

In this paper we propose a semi-random technique to design irregular LDPC codes with low error floor. In particular, the suggested technique is related to a popular class of irregular LDPC codes that can be encoded in linear time, and tries to avoid both low-weight codewords and near-codewords, which limit the performance of the belief propagation decoder. Besides, an easy and effective rule to design puncturing patterns for this class of LDPC codes is provided. The effectiveness of the new design criteria is proved by comparison with literature results.