Research On Multidimensional-scale Based Passive Localization Method

Passive localization is playing a more and more important role in the national securaty and modern war for its far-distance detection and self-hiding.Robust localization has been the foucus of research in passive localization.Passive localization is used widely,but it also faces with more and more challenges.This dissertation focuses on multidimensional scaling(MDS)localization in the presence of sensor position and synchronization clock uncertainties.What's more,a hybrid localization estimator with time of arrival(TOA)and Angle of Arrival(AOA)measurments is investigated.The main contents and contributions are as follows,1.A multidimensional scaling-based passive emitter localization estimator from TOA measurement with sensor position uncertaintis is proposed.This estimator is a closed-form and unbiased solution.It is shown theoretically,in small noise region,to achieve the Cramer-Rao lower bound(CRLB)performance.Simulation results validate the theoretical results and show that the proposed estimator has better performance than the the two-step weighted least square(TSWLS)estimator and weighted MDS estimator based on TOA measurements at moderate noise level.Additionally,the three estimators have comparable computational complexity.2.A novel weighted MDS estimator for estimating the position of a stationary emitter with sensor position uncertainties using time difference of arrival(TDOA)measurements is proposed.The solution is closed-form and unbiased.It is shown analytically to achieve the CRLB performance in small noise region.Simulation results show that the proposed estimator offers smaller bias and mean square error than the TSWLS estimator and the weighted MDS estimator based on TDOA measurements at moderate noise level.Additionally,the three estimators have comparable computational complexity.3.A novel weighted MDS algorithm for estimating the position and velocity of a moving emitter with sensor location uncertainties using TDOA and FDOA(Frequency Difference of Arrival)measurements is proposed.This method gives a closed-form solution for emitter position and velocity estimates.After a perturbation analysis,the bias and covariance of the proposed algorithm are derived,indicating that the proposed algorithm is an unbiased estimator and it is shown analytically to achieve the CRLB performance in small noise region.Simulation results show that the proposed estimator achieves better performance than the TSWLS estimator,the weighted MDS estimator and the constrained total least square(CTLS)estimator based on TDOA and FDOA measurements at moderate noise level.Additionally,the proposed estimator has small computational complexity than the CTLS estimator and it has comparable computational complexity than the other two estimators.4.Multi-nodes localization algorithm using TOA measurement with sensor position and synchronization clock uncertaintis is researched.The CRLB is proposed for analysising the influences of measuremnt noise,sensor position uncertainties and synchronization clock uncertaintis for localization accuracy.After that,a novel MDS based multi-nodes localization estimator is proposed.Simulations verify the effectiveness of the estimator.5.The relationship between the hybrid MDS localization with TOA and AOA measurements and the subspace-based MDS localization algorithm is investigated.The result indicates that they have the same inner product matrix.Then another hybrid MDS localization estimator using TOA and AOA measurments is proposed.This estimator only needs one AOA measurement,which decreases the complexity of the location system.The simulation results indicate that the proposed estimator performs better than the estimators using TOA measurements only and the Chan estimator using TOA and AOA measurements.