Research On Wireless Localization Algorithms And Target Motion Analysis

Wireless location finding has emerged as an essential feature in various important applications. Wireless localization and tracking of objects of interest are two canonical issues in wireless sensor networks and roaming robots. The related digital signal processing algorithms are key factors which impact directly on positioning accuracy, stability and real-time implementation. While intensive research in this area continued to deepen, there are still many technical bottlenecks and practical problem need to solve. Especially after the UAV and wireless sensor networks appeared, such low-cost, ultra-compact, network-based detection form became widely used, the corresponding research on practical technology lags behind.This dissertation deals systematically with passive point source localization and target motion analysis algorithm in Gaussian noise environment, focusing on improving precision and robustness, finding low-cost practical solutions. The contributions can be concluded as follows:(1) Approximate maximum likelihood estimation algorithms for stationary target localization.Stationary target localization based on Angle Of Arrival (AOA) and Time-Difference Of Arrival(TDOA) is investigate systematically. Fisher Information Matrixes based on TDOA/AOA algorithms are calculated. By introducing an intermediate variable, the nonlinear equations relating estimates can be transformed into a set of equations which are linear in the unknown parameters. Under some mild conditions, the noise term behaves like normal distributed. Therefore, the solutions are presented as approximate realization of the maximum likelihood estimators(MLE). Simulations show that the proposed algorithms outperform the previous contribution.(2) Constrained WLS and wireless localization based on TDOA measurementsSource localization is studied based on assumed Gauss distributed noisy measurements of TDOA (time-difference of arrival). The solution to the constrained weighted least-squares (WLS) is derived and applied to the source localization problem based on TDOAs. Under some mild conditions, the proposed algorithm is shown to be an approximate maximum likelihood estimation (MLE) algorithm. The simulation results show that the proposed approximate MLE algorithm compares favorably with the existing solution methods.(3) Constrained Kalman Filter for Localization and Tracking Based on TDOA and DOA MeasurementsThe problem of localization and tracking of moving target is investigated based on measurements of TDOA (time-difference of arrival) and DOA (direction of arrival) for which the measurement noises are assumed to be independent and identically distributed. The problem of the constrained linear MMSE (minimum mean-squared error) estimation is formulated by employing the pseudo-measurement model that imposes a quadratic constraint on the state vector associated with the target dynamics. Randomization of the state vector for the moving target process suggests to replace the hard constraint by its expectation. We first derive a solution to a quadratically constrained MMSE estimation problem. The constrained Kalman filtering is then developed for those estimation problems involving quadratic constraints, applicable to localization and tracking of moving target based on TDOA and DOA measurements. Moreover, the constrained Kalman filter admits a simple recursive solution with complexity comparable to that of the conventional Kalman filter. A simulation example is used to illustrate our proposed constrained Kalman filter outperform EKF in localization and tracking.(4) Target Motion Analysis for smart device by using Power and Doppler measurementsTarget motion analysis for smart device is investigated based on noisy measurements of power and Doppler from a radio signal emitter. The research is motivated by the fact that Power & Doppler measurements for RF signals are much easier to obtain on small detective device. The corresponding Fisher information matrix shows that power and Doppler measurements complement each other. An iterative least-squares algorithm based on Power & Doppler measurements is developed. It is shown to be effective through a numerical simulation.(5) Target Motion Analysis Based on Peak Power Measurements Using Networked Sensors Target Motion Analysis (TMA) using a network of wireless sensors/receivers which measure the power from a mobile Radio Frequency (RF) emitter is considered. Due to the limited communication capability of each sensor node, only peak power measurements from sensor nodes are transmitted to the fusion center. We present two main results that yield the optimum sensors' configuration such that the asymptotically achievable error variance of the target trajectory's estimate is minimized, and we derive efficient numerical algorithms for computing the optimum estimates of the trajectory of the moving target, thus achieving the goal of TMA. Simulation experiments showed that: even in the case of small signal to noise ratio, the proposed method has been able to achieve considerable estimation accuracy.