(A) study on the compressive sensing based high resolution methods and AOA-based three-dimensional localization = 압축센싱 기반 고분해능 기법 및 도래각 기반 3차원 위치추정 연구

다국어 초록 (Multilingual Abstract)

The compressive sensing based direction-of-arrival estimation algorithm is a high-resolution method that estimates the incident angles of multiple targets by using the spatial sparsity of the incident signal. The compressive sensing based direction-of-arrival estimation method has the advantage of being more robust to the phased array failure and noise than beamforming-based methods and subspace-based methods with high resolution. In the case of a localization method that estimates the location coordinates of a threaten target based on the angle-of-arrival, it is very important research topic for collecting location information of threats that do not provide GPS information.
The compressed sensing-based super-resolution methods have been studied with a focus on the passive phased array system and the bistatic MIMO system. The first proposed scheme is an optimal weight regularization parameter selection method of a covariance fitting algorithm with respect to the number of snapshots. A covariance fitting algorithm for the estimation of direction-of-arrivals (DOAs) of multiple incident signals is addressed in this dissertation. The scheme takes advantage of the fact that the incident signals are spatially sparse. A previous study has presented the regularization parameters of the covariance fitting for a very large number of snapshots. In this dissertation, a strategy on how to determine the regularization constant of the covariance fitting for a general number of snapshots is presented. The strategy essentially exploits the norm of the noise covariance matrix. The proposed algorithm has been validated via numerical simulations.
The second proposed scheme is an enhanced smoothed l0-norm algorithm for the passive phased array system, which uses covariance matrix of the received signal. The SL0 (smoothed l0-norm) algorithm is a fast compressive sensing-based DOA(direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this dissertation, a covariance fitting based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.
The third proposed scheme is a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm which is proposed to apply the superior performance of the compressive sensing method to the bistatic MIMO systems. The algorithm proposed in this dissertation optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately.
In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.
The fourth proposed scheme is a three-dimensional linear least square based localization algorithm. Closed-form expression of three-dimensional emitter location estimation using azimuth and elevation measurements at multiple locations is presented in this dissertation. The three dimensional location estimate is obtained from three dimensional sensor locations and the azimuth and elevation measurements at each sensor location. Since the formulation is not iterative, it is not computationally intensive and does not need an initial location estimate. Numerical results are presented to show the validity of the proposed scheme.

목차 (Table of Contents)

• 1. Introduction 1
• 2. Study on the compressive sensing (CS) based DOA estimation methods in the passive phased array system 11
• 3. Study on the compressive sensing based DOD/DOA estimation method in the bistatic MIMO system 70
• 4. Study on the AOA-based localization methods 114
• 5. Conclusions 140
• Bibliography 143
• List of Abbreviations 151