This paper proposes a diagonal covariance matrix approximation for Wide-Sense Stationary (WSS) signals with correlated Gaussian noise. Existing signal models that incorporate correlations often require regularization of the covariance matrix, so that the covariance matrix can be inverted. The disadvantage of this approach is that matrix inversion is computational intensive and regularization decreases precision. We use Bienayme's theorem to approximate the covariance matrix by a diagonal one, so that matrix inversion becomes trivial, even with non-uniform rather than only uniform sampling that was considered in earlier work. This approximation reduces the computational complexity of the estimator and estimation bound significantly. We numerically validate this approximation and compare our approach with the Maximum Likelihood Estimator (MLE) and Cramer-Rao Lower Bound (CRLB) for multivariate Gaussian distributions. Simulations with highly correlated signals show that our approach differs less than 0.1% from this MLE and CRLB when the observation time is large compared to the correlation time. Additionally, simulations show that in case of non-uniform sampling, we increase the performance in comparison to earlier work by an order of magnitude. We limit this study to correlated signals in the time domain, but the results are also applicable in the space domain.

This paper presents a feasibility study on smartphone localization of missing persons in Search And Rescue (SAR) operations using widely available Commercial-Off-The-Shelf (COTS) products. We assume (1) that the missing person wears an enabled smartphone and (2) that messages transmitted by this smartphone can be intercepted by mobile agents at known positions. We present a proof-of-concept that consists of several mobile agents carrying smartphones that measure the Received Signal Strength (RSS) of Wi-fi messages transmitted by the smartphone of the missing person. The positions of the mobile agents are determined using the GPS unit on the smartphones. The mobile agents send the collected RSS and GPS data to a central processing unit. The central processing unit processes the data in real-time and guides mobile agents in SAR operations to the missing person by estimating its position. Our central processing unit runs a localization algorithm that requires no calibration. This is a necessary condition for resue operations that usually take place in unknown environments with unknown hardware. Our experiments in an 250×130m2 outdoor field shows that our localization system provides an average localization performance of roughly 15 meters, which is sufficient for most SAR operations of interest. In addition, we performed several successful tests with a Quadcopter to show the feasibility of using unmanned vehicles in SAR operations.

GPS is widely used for localization and tracking, however traditional GPS receivers consume too much energy for many applications. This paper implements and evaluates the performance of a low-energy GPS prototype. The main difference is that a traditional GPS needs to sample signals transmitted by satellites for 30 seconds to estimate its position. Our prototype reduces this time by three orders of magnitude and it can compute positions from only 2 milliseconds of data. We present a new algorithm that increases robustness by filtering on estimated residuals instead of using an altitude database. In addition, we show that our new algorithm works with both fixed and moving targets. The solution consists of (1) a portable device that samples the GPS signal and (2) a server that utilizes Doppler navigation and Coarse Time Navigation to estimate positions. We performed tests in a wide variety of environments and situations. These tests show that our prototype provides a median positioning error of roughly 40 meters even when the GPS receiver is moving at 80 kilometres per hour.

This paper experimentally and theoretically investigates the fundamental bounds on radio localization precision of far-field Received Signal Strength (RSS) measurements. RSS measurements are proportional to power-flow measurements time-averaged over periods long compared to the coherence time of the radiation. Our experiments are performed in a novel localization setup using 2.4GHz quasi-monochromatic radiation, which corresponds to a mean wavelength of 12.5cm. We experimentally and theoretically show that RSS measurements are cross-correlated over a minimum distance that approaches the diffraction limit, which equals half the mean wavelength of the radiation. Our experiments show that measuring RSS beyond a sampling density of one sample per half the mean wavelength does not increase localization precision, as the Root-Mean-Squared-Error (RMSE) converges asymptotically to roughly half the mean wavelength. This adds to the evidence that the diffraction limit determines (1) the lower bound on localization precision and (2) the sampling density that provides optimal localization precision. We experimentally validate the theoretical relations between Fisher information, Cramér-Rao Lower Bound (CRLB) and uncertainty, where uncertainty is lower bounded by diffraction as derived from coherence and speckle theory. When we reconcile Fisher Information with diffraction, the CRLB matches the experimental results with an accuracy of 97-98%.

This paper focuses on optimal and automatic calibration of the propagation model of Received Signal Strength (RSS) based localization algorithms. Conventional RSS-based localization algorithms assume that optimal calibration is static and identical for all nodes, which limits its use to static environments. However realistic environments are dynamic, where each node should estimate its own optimal propagation model settings dependent on the node's hardware and location. We call this process Self-Adaptive Localization (SAL). SAL algorithms estimate the parameter settings from available localization measurements. We show that existing SAL algorithms significantly decrease the localization accuracy and stability. Our main contribution is that we determine the conditions under which SAL algorithms provide optimal results, that are shown to be constraints on the localization surface. Since the antenna orientation has a significant impact on RSS and thus optimal propagation model settings, we evaluated SAL in an environment with unknown and thus dynamic antenna orientations. Our measurements and simulations show that these constraints increase the accuracy by ~ 45% and the stability by ~ 70% in static and dynamic environments.

This paper presents a novel Radio Interferometric Positioning System (RIPS), which we call Stochastic RIPS (SRIPS). Although RIPS provides centimeter accuracy, it is still not widely adopted due to (1) the limited set of suitable radio platforms and (2) the relatively long measurement and calibration times. SRIPS overcomes these practical limitations by (1) omitting the calibration phase of the existing RIPS and by (2) applying a novel positioning algorithm. SRIPS exploits the phenomenon of the small but stable difference between two transmitted frequencies that often exists when two radios are tuned to the same frequency. We obtain an experimental measure for this stability. This approach enables the implementation of RIPS on commonly available radio platforms, such as the CC2430, because fine-tuning in small steps relative to the beat frequencies for calibration is not required. In addition, we show that SRIPS calculates the position that provides the best fit to the set of measurements, given the underlying statistical and propagation models. Therefore, SRIPS converges more accurately to the true locations in a variety of situations of practical interest. Experiments in a 20x20m2 set-up verify this and show that our SRIPS CC2430 implementation reduces the number of required measurements by a factor of three, and it reduces the measurement time to less than 0.1 seconds, while providing accuracy similar to that of the existing RIPS implementation on the CC1000 platform, which requires seconds.

This paper contains a feasibility study of Radio Interferometric Positioning (RIP) implemented on a widely used 2.4 GHz radio (CC2430). RIP is a relatively new localization technique that uses signal strength measurements. Although RIP outperforms other RSS-based localization techniques, it imposes a set of unique requirements on the used radios. Therefore, it is not surprising that all existing RIP implementations use the same radio (CC1000), which operates below the 1 GHz range. This paper analyzes to what extent the CC2430 complies with these requirements. This analysis shows that the CC2430 platform introduces large and dynamic sources of errors. Measurements with a CC2430 test bed in a line-of-sight indoor environment verify this. The measurements indicate that the existing RIP algorithm cannot cope with these types of errors, and will incur a relatively low accuracy of 3.1 meter. Based on these results, we made an initial implementation of a new algorithm, which can cope with these errors, and decreases this positioning error by a factor of two to 1.5 meter accuracy.

This paper analyzes the influence of the antenna orientation on the performance of Received Signal Strength (RSS) based localization algorithms. Existing RSS-based localization algorithms provide reliable results in environments with static sources of error. This paper analyzes the performance of three RSS-based algorithms in an environment with the antenna orientation as a dynamic source of error. We first experimentally verify that the antenna orientation has a large influence on the received signal strength by performing an extensive amount of measurements. As expected, these measurements show that the signal strength may vary more than a factor five under different antenna orientations. This paper shows that antenna orientations may decrease the performance of optimally calibrated RSS-based localization algorithms by as much as 32%, from 1.8 to 2.65 meter. In addition, it shows that improper calibration of the antenna orientation may decrease the accuracy by 64%, from 1.9 to 3.1 meter.

This paper analyzes the performance of several Received Signal Strength (RSS) based localization methods as a function of the calibration effort, hence as a function of deployment and maintenance costs. The deployment and maintenance costs determine the scalability and thus the applicability of a localization algorithm, and this is still a topic of research. This paper analyzes and compares the best available localization algorithms of the following localization methods: fingerprinting-, range- and proximity-based localization. An extensive amount of RSS measurements, performed in a realistic indoor environment show that range-based algorithms outperform fingerprinting-and proximity-based localization algorithms when there is a limited amount of calibration measurements available. In that case, range-based algorithms have ~ 30% smaller errors, ~ 1.3 meter compared to ~ 1.9 meter. Our measurements show that fingerprinting-based algorithms approximate the performance of range-based algorithms as the number of calibration measurements increases from 1 to 80.

Incorporating Embedded Systems courses in a general and broad Computer Science undergraduate curriculum can be a challenging task. The lack of experience with relevant tools and programming languages tends to limit the amount material that can be included in courses on this area. This, combined with limited familiarity and theoretical background within the field, makes motivating the students a serious issue. In this paper we describe our effort to change one of the embedded systems courses at the University of Twente in a way that enables students, without additional prior knowledge, to obtain a broad experience on the field of Wireless Sensor Networks and possibly motivate them to follow a further specialization in Embedded Systems. To achieve this goal we moved away from the traditional course where students first had to practice with all the tools and languages needed to program embedded systems, after which they could work on the real challenges, to a course where students could work on the final challenges from the start. Reversing this order eliminated the amount of time and effort students had to spent on learning tools and languages of which they did not yet understand the final purpose. This reversal led to a course that was received with great enthusiasm. Furthermore, given the progress the students showed during the course, this new approach proved to be highly effective. Hopefully the effects of this course can be seen in the following years in the form of a higher number of students choosing a specialization in Embedded Systems.

This paper investigates distributed range-free localization in wireless networks using a communication protocol called sum-dist which is commonly employed by localization algorithms. With this protocol, the reference nodes flood the network in order to estimate the shortest distance between the reference and blind nodes. Existing localization algorithms that use this communication protocol only evaluate the shortest distance. Our approach is somewhat different in that we optimize the localization performance for this communication protocol. We present a new algorithm called COM-LOC which exploits a certain part of the information inherent in the protocol that other algorithms consider as redundant or false information. We show that the use of this additional part of information increases the performance compared to other range-free algorithms by 68% to 206%. Other comparisons with several RSS-based localization algorithms show that COM-LOC outperforms these algorithms under a wide range of conditions, while keeping the communication costs equal.

Localization schemes for wireless sensor networks can be classified as range-based or range-free. They differ in the information used for localization. Range-based methods use range measurements, while range-free techniques only use the content of the messages. None of the existing algorithms evaluate both types of information. Most of the localization schemes do not consider mobility. In this paper, a Sequential Monte Carlo Localization Method is introduced that uses both types of information as well as mobility to obtain accurate position estimations, even when high range measurement errors are present in the network and unpredictable movements of the nodes occur. We test our algorithm in various environmental settings and compare it to other known localization algorithms. The simulations show that our algorithm outperforms these known range-oriented and range-free algorithms for both static and dynamic networks. Localization improvements range from 12% to 49% in a wide range of conditions.

research is done at the localization of sensor nodes in a static wireless sensor network. In this paper, the focus is only on the localization of nodes in mobile sensor networks. An algorithm is proposed for mobile sensor networks that use all available information to get better location estimates. Our goal for this algorithm was to minimize communication costs, to have a stable performance even when unpredictable movement occurs and to get an optimal position estimation. Our technique and different extensions, which are proposed throughout the paper, are analyzed at the end of the paper through simulation runs. The proposed algorithm outperforms an Iterative Weighted Least Square method by using less computation power and having more accurate position estimates.