Cooperative sensing for target estimation and target localization

Abstract

As a novel sensing scheme, cooperative sensing has drawn great interests in recent years. By utilizing the concept of "cooperation", which incorporates communications and information exchanges among multiple sensing devices, e.g. radar transceivers in radar systems, sensor nodes in wireless sensor networks, or mobile handsets in cellular systems, the sensing capability can achieve significant improvement compared to the conventional noncooperative mode in many aspects. For example, cooperative target estimation is inspired by the benefits of MIMO in communications, where multiple transmit and/or receive antennas can increase the diversity to combat channel fading for enhanced transmission reliability and increase the degrees of freedom for improved data rate. On the other hand, cooperative target localization is able to dramatically increase localization performance in terms of both accuracy and coverage. From the perspective of cooperative target estimation, in this dissertation, we optimize waveforms from multiple cooperative transmitters to facilitate better target estimation in the presence of colored noise. We introduce the normalized MSE (NMSE) minimizing criterion for radar waveform designs. Not only is it more meaningful for parameter estimation problems, but it also exhibits more similar behaviors with the MI criterion than its MMSE counterpart. We also study the robust designs for both the probing waveforms at the transmitter and the estimator at the receiver to address one type of a priori information uncertainties, i.e., in-band target and noise PSD uncertainties. The relationship between MI and MSEs is further investigated through analysis of the sensitivity of the optimum design to the out-band PSD uncertainties as known as the overestimation error. From the perspective of cooperative target localization, in this dissertation, we study the two phases that comprise a localization process, i.e., the distance measurement phase and the location update phase. In the first distance measurement phase, thanks to UWB signals' many desirable features including high delay resolution and obstacle penetration capabilities, we adopt UWB technology for TOA estimation, and then translate the TOA estimate into distance given light propagation speed. We develop a practical data-aided ML timing algorithm and obtain its optimum training sequence. Based on this optimum sequence, the original ML algorithm can be simplified without affecting its optimality. In the second location update phase, we investigate secure cooperative target localization in the presence of malicious attacks, which consists of a fundamental issue in localization problems. We explicitly incorporate anchors' misplacements into distance measurement model and explore the pairwise sparse nature of the misplacements. We formulate the secure localization problem as an ℓ1-regularized least squares (LS) problem and establish the pairwise sparsity upper bound which defines the largest possible number of identifiable malicious anchors. Particularly, it is demonstrated that, with target cooperation, the capability of secure localization is improved in terms of misplacement estimation and target location estimation accuracy compared to the single target case.