Distributed Estimation with Information-Seeking Control in Agent Network

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.Comment: 17 pages, 10 figure

3-D Hybrid Localization with RSS/AoA in Wireless Sensor Networks: Centralized Approach

This dissertation addresses one of the most important issues present in Wireless Sensor Networks (WSNs), which is the sensor’s localization problem in non-cooperative and cooperative 3-D WSNs, for both cases of known and unknown source transmit power PT . The localization of sensor nodes in a network is essential data. There exists a large number of applications for WSNs and the fact that sensors are robust, low cost and do not require maintenance, makes these types of networks an optimal asset to study or manage harsh and remote environments. The main objective of these networks is to collect different types of data such as temperature, humidity, or any other data type, depending on the intended application. The knowledge of the sensors’ locations is a key feature for many applications; knowing where the data originates from, allows to take particular type of actions that are suitable for each case. To face this localization problem a hybrid system fusing distance and angle measurements is employed. The measurements are assumed to be collected through received signal strength indicator and from antennas, extracting the received signal strength (RSS) and angle of arrival (AoA) information. For non-cooperativeWSN, it resorts to these measurements models and, following the least squares (LS) criteria, a non-convex estimator is developed. Next, it is shown that by following the square range (SR) approach, the estimator can be transformed into a general trust region subproblem (GTRS) framework. For cooperative WSN it resorts also to the measurement models mentioned above and it is shown that the estimator can be converted into a convex problem using semidefinite programming (SDP) relaxation techniques.It is also shown that the proposed estimators have a straightforward generalization from the known PT case to the unknown PT case. This generalization is done by making use of the maximum likelihood (ML) estimator to compute the value of the PT . The results obtained from simulations demonstrate a good estimation accuracy, thus validating the exceptional performance of the considered approaches for this hybrid localization system

Enhancement of weighted centroid algorithm for indoor mobile non-cooperative localization system

Nowadays, indoor wireless localization is being challenged research by providing high accuracy of location information. However lower processing time, resistant to environmental condition, simple network topology are also become main concern. Noncooperative localization based on RSSI allow the anchor nodes as the reference nodes communicate directly to the target node by exchanging the location data. High sensitivity of RSSI to the indoor environment, make difficulties in modelling propagation characteristic called as PLE. Incompatibility the PLE value can influence to the estimated position result. Weighted centroid localization (WCL) is feasible solution for RSSI-based that can obtain the target node location just by RSSI and anchor nodes coordinate without PLE value and estimated distance. While, the centroid determination of WCL give better estimation only to the centralized position of target node between all anchor nodes position. Therefore, we propose enhancement of WCL (eWCL) by replacing the weight based on RSSI with different estimated distance from WCL calculation. The simulation result show that using eWCL can reduce the error estimation around 60.42% compared to the WCL algorithm with 1.85 meters MSE value. Then, compared to the cooperative localization based on trilateration algorithm achieve 12.15% error estimation larger than eWCL at non-cooperative scheme

Distributed Algorithm for Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel non-convex estimator based on the least squares (LS) criterion. The derived non-convex estimator tightly approximates the maximum likelihood (ML) one for small noise levels. We show that the developed non-convex estimator is suitable for distributed implementation, and that it can be transformed into a convex one by applying a second-order cone programming (SOCP) relaxation technique. We also show that the developed non-convex estimator can be transformed into a generalized trust region sub-problem (GTRS) framework, by following the squared range (SR) approach. The proposed SOCP algorithm for known transmit powers is then generalized to the case where the transmit powers are different and not known. Furthermore, we provide a detailed analysis of the computational complexity of the proposed algorithms. Our simulation results show that the new estimators have excellent performance in terms of the estimation accuracy and convergence, and they confirm the effectiveness of combining two radio measurements

On the Trade-Off Between Accuracy and Delay in Cooperative UWB Localization: Performance Bounds and Scaling Laws

Ultra-wide bandwidth (UWB) systems allow for accurate positioning in environments where global navigation satellite systems may fail, especially when complemented with cooperative processing. While cooperative UWB has led to centimeter-level accuracies, the communication overhead is often neglected. We quantify how accuracy and delay trade off in a wide variety of operation conditions. We also derive the asymptotic scaling of accuracy and delay, indicating that, in some conditions, standard cooperation offers the worst possible tradeoff. Both avenues lead to the same conclusion: indiscriminately targeting increased accuracy incurs a significant delay penalty. Simple countermeasures can be taken to reduce this penalty and obtain a meaningful accuracy/delay trade-off

Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

This dissertation addresses the target localization problem in wireless sensor networks (WSNs). WSNs is now a widely applicable technology which can have numerous practical applications and offer the possibility to improve people’s lives. A required feature to many functions of a WSN, is the ability to indicate where the data reported by each sensor was measured. For this reason, locating each sensor node in a WSN is an essential issue that should be considered. In this dissertation, a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) is presented. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. The measurements are then used to derive a convex estimator, based on second-order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. The results obtained in this dissertation confirm the novel algorithms’ performance and accuracy. Additionally, a change to the algorithms is proposed, allowing the study of a more realistic and challenging scenario where different probabilities of communication failure between neighbor nodes at the broadcast phase are considered. Computational simulations performed in the scope of this dissertation, show that the algorithms’ performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations