Lanxin Lin

Bio: Lanxin Lin is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Linear least squares & Estimator. The author has an hindex of 8, co-authored 10 publications receiving 440 citations. Previous affiliations of Lanxin Lin include Shantou University.

Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization

Abstract: A conventional approach for passive source localization is to utilize signal strength measurements of the emitted source received at an array of spatially separated sensors. The received signal strength (RSS) information can be converted to distance estimates for constructing a set of circular equations, from which the target position is determined. Nevertheless, a major challenge in this approach lies in the shadow fading effect which corresponds to multiplicative measurement errors. By utilizing the mean and variance of the squared distance estimates, we devise two linear least squares (LLS) estimators for RSS-based positioning in this paper. The first one is a best linear unbiased estimator while the second is its improved version by exploiting the known relation between the parameter estimates. The variances of the position estimates are derived and confirmed by computer simulations. In particular, it is proved that the performance of the improved LLS estimator achieves Cramer-Rao lower bound at sufficiently small noise conditions.

A Prediction Approach for Multichannel EEG Signals Modeling Using Local Wavelet SVM

Abstract: Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal.

Cooperative Received Signal Strength-Based Sensor Localization With Unknown Transmit Powers

Abstract: Cooperative localization (also known as sensor network localization) using received signal strength (RSS) measurements when the source transmit powers are different and unknown is investigated. Previous studies were based on the assumption that the transmit powers of source nodes are the same and perfectly known which is not practical. In this paper, the source transmit powers are considered as nuisance parameters and estimated along with the source locations. The corresponding Cramer-Rao lower bound (CRLB) of the problem is derived. To find the maximum likelihood (ML) estimator, it is necessary to solve a nonlinear and nonconvex optimization problem, which is computationally complex. To avoid the difficulty in solving the ML estimator, we derive a novel semidefinite programming (SDP) relaxation technique by converting the ML minimization problem into a convex problem which can be solved efficiently. The algorithm requires only an estimate of the path loss exponent (PLE). We initially assume that perfect knowledge of the PLE is available, but we then examine the effect of imperfect knowledge of the PLE on the proposed SDP algorithm. The complexity analyses of the proposed algorithms are also studied in detail. Computer simulations showing the remarkable performance of the proposed SDP algorithm are presented.

Impact of Multipath Reflections on the Performance of Indoor Visible Light Positioning Systems

Abstract: Visible light communication (VLC) using light-emitting-diodes (LEDs) has been a popular research area recently. VLC can provide a practical solution for indoor positioning. In this paper, the impact of multipath reflections on a two-dimensional indoor VLC positioning is investigated, considering a complex indoor environment with walls, floor, and ceiling. For the proposed positioning system, an LED bulb is the transmitter and a photo-diode is the receiver to detect received signal strength information. Combined deterministic and modified Monte Carlo method is applied to compute the impulse response of the optical channel. Since power attenuation is applied to calculate the distance between the transmitter and receiver, the received power from each reflection order is analyzed. The positioning errors are further estimated for all the locations over the room and compared with the previous works where no reflections considered. Finally, calibration approaches are proposed to decrease the effect of multipath reflections.

On Received-Signal-Strength Based Localization with Unknown Transmit Power and Path Loss Exponent

Abstract: In this letter, we consider the received-signal-strength (RSS) based localization problem with unknown transmit power and unknown path loss exponent (PLE). For the case of unknown transmit power, we derive a weighed least squares (WLS) formulation to jointly estimate the sensor node location and the transmit power, based on the unscented transformation (UT). For the case of unknown PLE, we propose an alternating estimation procedure to alternatively estimate the sensor node location and the PLE. The estimation procedure can also be applied to the case when both the transmit power and the PLE are unknown. Simulation results confirm the effectiveness of the proposed method.