Kenneth Wing-Kin Lui

Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

Finding the positions of nodes in an ad hoc wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramer-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB.

Semidefinite Programming Approach for Range-Difference Based Source Localization

A common technique for passive source localization is to utilize the range-difference (RD) measurements between the source and several spatially separated sensors. The RD information defines a set of hyperbolic equations from which the source position can be calculated with the knowledge of the sensor positions. Under the standard assumption of Gaussian distributed RD measurement errors, it is well known that the maximum-likelihood (ML) position estimation is achieved by minimizing a multimodal cost function which corresponds to a difficult task. In this correspondence, we propose to approximate the nonconvex ML optimization by relaxing it to a convex optimization problem using semidefinite programming. A semidefinite relaxation RD-based positioning algorithm, which makes use of the admissible source position information, is proposed and its estimation performance is contrasted with the two-step weighted least squares method and nonlinear least squares estimator as well as Cramer-Rao lower bound.

A study of two-dimensional sensor placement using time-difference-of-arrival measurements

Finding the position of a radiative source based on time-difference-of-arrival (TDOA) measurements from spatially separated receivers has important applications in sonar, radar, mobile communications and sensor networks. Each TDOA defines a hyperbolic locus on which the source must lie and the position estimate can then be determined with the knowledge of the sensor array geometry. While extensive research works have been performed on algorithm development for TDOA estimation and TDOA-based localization, limited attention has been paid in sensor array geometry design. In this paper, an optimum two-dimensional sensor placement strategy is derived with the use of optimum TDOA measurements, assuming that each sensor receives a white signal source in the presence of additive white noise. The minimum achievable Cramer-Rao lower bound is also produced.

Maximum A Posteriori Approach to Time-of-Arrival-Based Localization in Non-Line-of-Sight Environment

A conventional approach to mobile positioning is to utilize the time-of-arrival (TOA) measurements between the mobile station (MS) and several receiving base stations (BSs). The TOA information defines a set of circular equations from which the MS position can be calculated with the known BS geometry. However, when the TOA measurements are obtained from the non-line-of-sight (NLOS) paths, the position estimation performance can be very unreliable. Assuming that the NLOS probability and distribution are known and the NLOS-induced error dominates the corresponding TOA measurement, two maximum a posteriori probability (MAP) algorithms for NLOS detection and MS localization are derived in this paper. The first provides a standard MAP solution, while the second is a simplified version based on geometric constraints. It is shown that the former achieves more accurate estimation performance at the expense of higher computational cost.

Two Simplified Recursive Gauss–Newton Algorithms for Direct Amplitude and Phase Tracking of a Real Sinusoid

In this letter, the problem of adaptive tracking the amplitude and phase of a noisy sinusoid with known frequency is addressed. Based on approximating the recursive Gauss-Newton approach, two computationally simple algorithms, which provide direct parameter estimates, are devised and analyzed. Simulation results show that the proposed methods can attain identical estimation performance as their original one.

Fast communication: Accurate time delay estimation based passive localization

A conventional approach for source localization is to utilize time delay measurements of the emitted signal received at an array of sensors. The time delay information is then employed to construct a set of hyperbolic equations from which the target position can be determined. In this paper, we utilize semi-definite programming (SDP) technique to derive a passive source localization algorithm which can integrate the available a priori knowledge such as admissible target range and other cues. It is shown that the SDP method is superior to the well-known two-step weighted least squares method at lower signal-to-noise ratio conditions.

Accurate three-step algorithm for joint source position and propagation speed estimation

A popular strategy for source localization is to utilize the measured differences in arrival times of the source signal at multiple pairs of receivers. Most of the time-difference-of-arrival (TDOA) based algorithms in the literature assume that the signal transmission speed is known which is valid for in-air propagation. However, for in-solid scenarios such as seismic and tangible acoustic interface applications, the signal propagation speed is unknown. In this paper, we exploit the ideas in the two-step weighted least squares method [ 1] to design a three-step algorithm for joint source position and propagation speed estimation. Simulation results are included to contrast the proposed estimator with the linear least squares scheme as well as Cramer-Rao lower bound.

Distributed particle filter for target tracking in sensor networks

In this paper, we present a distributed particle fllter (DPF) for target tracking in a sensor network. The proposed DPF consists of two major steps. First, particle compression based on support vector machine is performed to reduce the cost of transmission among sensors. Second, each sensor fuses the compressed information from its neighboring nodes with use of consensus or gossip algorithm to estimate the target track. Computer simulations are included to verify the efiectiveness of the proposed approach.

SENSOR SELECTION FOR TARGET TRACKING IN SENSOR NETWORKS

This paper addresses the sensor selection problem which is a very important issue where many sensors are available to track a target. In this problem, we need to select an appropriate group of sensors at each time to perform tracking in a wireless sensor network (WSN). As the theoretical tracking performance is bounded by posterior Cramer-Rao lower bound (PCRLB), it is used as a criterion to select sensors. Based on the PCRLB, sensor selection algorithms with and without sensing range constraint are developed. Without sensing range limit, exhaustive enumeration is flrst adopted to search all possible combinations for sensor selection. To reduce complexity of enumeration, second, we restrict the selected sensors to be within a flxed area in the WSN. With sensing range constraint, a circle will be drawn with the help of communication range for sensor selection. In a similar manner, two approaches, namely, selecting all sensors inside the circle or using enumeration to select sensors within the circle are presented. The efiectiveness of the proposed methods is validated by computer simulation results in target tracking for WSNs.

Sum Capacity of One-Sided Parallel Gaussian Interference Channels

The sum capacity of the one-sided parallel Gaussian interference channel is shown to be a concave function of user powers. Exploiting the inherent structure of the problem, we construct a numerical algorithm to compute it. Two suboptimal schemes are compared with the capacity-achieving scheme. One of the suboptimal schemes, namely iterative waterfilling, yields close-to-capacity performance when the cross link gain is small.