Wendong Xiao

Brief paper: Optimal linear estimation for systems with multiple packet dropouts

This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with multiple packet dropouts. Based on a packet dropout model, the optimal linear estimators including filter, predictor and smoother are developed via an innovation analysis approach. The estimators are computed recursively in terms of the solution of a Riccati difference equation of dimension equal to the order of the system state plus that of the measurement output. The steady-state estimators are also investigated. A sufficient condition for the convergence of the optimal linear estimators is given. Simulation results show the effectiveness of the proposed optimal linear estimators.

Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks

Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.

Optimal Full-Order and Reduced-Order Estimators for Discrete-Time Systems With Multiple Packet Dropouts

This paper is concerned with the estimation problem for discrete-time stochastic linear systems with multiple packet dropouts. Based on a recently developed model for multiple-packet dropouts, the original system is transferred to a stochastic parameter system by augmentation of the state and measurement. The optimal full-order linear filter of the form of employing the received outputs at the current and last time instants is investigated. The solution to the optimal linear filter is given in terms of a Riccati difference equation governed by packet arrival rate. The optimal filter is reduced to the standard Kalman filter when there are no packet dropouts. The steady-state filter is also studied. A sufficient condition for the existence of the steady-state filter is given and the asymptotic stability of the optimal filter is analyzed. At last, a reduced-order filter is investigated.

Optimal Filtering for Systems With Multiple Packet Dropouts

This paper is concerned with the optimal filtering problem for discrete-time stochastic linear systems with multiple packet dropouts, where the number of consecutive packet dropouts is limited by a known upper bound. Without resorting to state augmentation, the system is converted to one with measurement delays and a moving average (MV) colored measurement noise. An unbiased optimal filter is developed in the linear least-mean-square sense. Its solution depends on the recursion of a Riccati equation and a Lyapunov equation. A numerical example shows the effectiveness of the proposed filter.

Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems

In this brief, an optimal control scheme based on adaptive dynamic programming (ADP) is developed to solve infinite-horizon optimal control problems of continuous-time complex-valued nonlinear systems. A new performance index function is established on the basis of complex-valued state and control. Using system transformations, the complex-valued system is transformed into a real-valued one, which overcomes Cauchy-Riemann conditions effectively. With the transformed system and the performance index function, a new ADP method is developed to obtain the optimal control law by using neural networks. A compensation controller is developed to compensate the approximation errors of neural networks. Stability properties of the nonlinear system are analyzed and convergence properties of the weights for neural networks are presented. Finally, simulation results demonstrate the performance of the developed optimal control scheme for complex-valued nonlinear systems.

Multiple-Level Quantized Innovation Kalman Filter

Abstract In this paper, we study a general multiple-level quantized innovation Kalman filter (MLQ-KF) for estimation of linear dynamic stochastic systems. First, given a multi-level quantization of innovation, we derive the corresponding MMSE filter in terms of the given quantization levels under the assumption that the innovation is approximately Gaussian. By optimizing the filter with respect to the quantization levels, we obtain an optimal quantization scheme and the corresponding optimal MLQ-KF. The optimal filter is given in terms of a simple Riccati difference equation as in the standard Kalman filter. For the case of 1-bit transmission, our proposed optimal filter gives a better performance than the sign-of-innovation filter (SOI-KF) Ribeiro et al. [2006]. The convergence of the MLQ-KF to the standard Kalman filter is established.

An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks

Energy-based multisource localization is an important research problem in wireless sensor networks (WSNs). Existing algorithms for this problem, such as multiresolution (MR) search and exhaustive search methods, are of either high computational complexity or low estimation accuracy. In this paper, an efficient expectation-maximization (EM) algorithm for maximum-likelihood (ML) estimation is presented for energy-based multisource localization in WSNs using acoustic sensors. The basic idea of the algorithm is to decompose each sensors energy measurement, which is a superimposition of energy signals emitted from multiple sources, into components, each of which corresponds to an individual source, and then estimate the source parameters, such as source energy and location, as well as the decay factor of the signal during propagation. An efficient sequential dominant-source (SDS) initialization scheme and an incremental parameterized search refinement scheme are introduced to speed up the algorithm and improve the estimation accuracy. Theoretic analyses on the algorithm convergence rate, the Cramer-Rao lower bound (CRLB) for localization accuracy, and the computational complexity of the algorithm are also given. The simulation results show that the proposed EM algorithm provides a good tradeoff between estimation accuracy and computational complexity.

Adaptive sensor scheduling for target tracking in wireless sensor network

Target tracking is an essential capability for Wireless Sensor Networks (WSNs) and is used as a canonical problem for collaborative signal and information processing to dynamically manage sensor resources and efficiently process distributed sensor measurements. In existing work for target tracking in WSNs, such as the information-driven sensor query (IDSQ) approach, the tasking sensors are scheduled based on uniform sampling interval, ignoring the changing of the target dynamics and obtained estimation accuracy. This paper proposes the adaptive sensor scheduling strategy by jointly selecting the tasking sensor and determining the sampling interval according to the predicted tracking accuracy and tracking cost. The sensors are scheduled in two tracking modes, i.e., the fast tracking approaching mode when the predicted tracking accuracy is not satisfactory, and the tracking maintenance mode when the predicted tracking accuracy is satisfactory. The approach employs an Extended Kalman Filter (EKF) based estimation technique to predict the tracking accuracy, and adopts a linear energy model to predict the energy consumption. Simulation results demonstrate that, compared to the non-adaptive approach, the proposed approach can achieve significant improvement on energy consumption without degrading the tracking accuracy.

Integrated Wi-Fi fingerprinting and inertial sensing for indoor positioning

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. A region-based fingerprinting approach is presented for indoor positioning in Wi-Fi wireless networks. This proposed method compares the fingerprint of a Wi-Fi tag with that of a region-based group of reference points, instead of an individual reference point. With the fingerprinting position estimate obtained, and with an inertial measurement unit integrated with the Wi-Fi tag, a stochastic system model is adopted to track the targets position when it is in piecewise constant velocity motion in Wi-Fi wireless networks. The stochastic system model utilizes Wi-Fi fingerprinting position estimates as measurements and inertial sensing data as control inputs. Both simulation studies and experiment data have shown the positioning performance of the integrated mobile platform with improved accuracy, by using the proposed Wi-Fi and inertial sensing technologies.

Secure and robust Wi-Fi fingerprinting indoor localization

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. Fingerprinting techniques can provide a low-cost and high-accuracy localization solution by utilizing in-building communication infrastructures. However, existing fingerprinting localization algorithms are not resistant to outliers, for example, the accidental environment changes, access point (AP) attacks. Another drawback is that traditional K nearest neighbor (KNN) algorithm in the literature may not select the candidate reference points (RPs) correctly. In this paper, we propose a novel environmentally robust and attack resistant probabilistic fingerprinting localization method. In the offline phase, the distribution estimation of the signal strength is performed using probabilistic histogram method. Then in the online phase, a three-step location sensing method is proposed. In the first step, a simple and efficient outlier detection method named non-iterative “RANdom SAmple Consensus” (RANSAC) is run to detect and eliminate part of APs from which the signals measured are severely distorted by unexpected environment effects. In the second step, a novel region-based RP selection method which works like a “family of probability” is proposed to improve the possibility of the correctness of selection of the nearest RPs. In the final step, the location is obtained using a weighted-mean method. In the experiment section, we demonstrate the proposed method in our lab and find that the proposed strategies are resistant to outliers and can improve the localization accuracy effectively compared with existing methods.