Azimuth-Range Joint Estimation And High Resolution Profiling In Antenna Array Radar

Frequency Modulated Continuous Wave (FMCW) antenna array radar is widely used in automotive scenario since its excellent environment adaptability, high resolution and stable system performance. However, the complexity of current system architecture and the azimuth resolution of current signal processing methods can not satisfy system requirement. To address these issues, this thesis studies system model of antenna array radar and high resolution algorithms in automotive scenario.In the aspect of system model, the architecture of time-division multiplexing antenna array radar is presented. And then, existing multiple-cycle time-division multiplexing mode and corresponding signal model is introduced. In this mode, multiple transmit cycle data sampling is required to estimate the azimuth, range and velocity, which restricts the movement of targets. To solve this problem, single-cycle time-division multiplexing mode is proposed and corresponding signal model, coupling problem between time and space is investigated in detail. Further, the signal models of two special cases: slow switching mode and fast switching mode are analyzed. These two models and the signal model of multiple-channel antenna array can be unified into a same framework. Based on these, multiplexing mode selection in real system is discussed.To overcome the restriction on azimuth resolution and coherent signals problem, sparse recovery technique is employed to estimate the azimuth of targets. Independent sparse recovery based azimuth estimation algorithm estimates the azimuth of each range cell separately. Considering joint sparseness of the signals, joint sparse recovery based azimuth estimation algorithm is proposed to enhance the robustness of the recovery results. Beyond these, we extend the sparse recovery based azimuth estimation algorithm to advocate a new estimation method: sparse recovery based azimuth-range joint estimation algorithm, for settling the range sidelobe problem in joint sparse recovery based azimuth estimation algorithm. It is shown in simulation, compared with other algorithms, sparse recovery based azimuth-range joint estimation algorithm and its improvement yield superior multiple-target resolution, and attain better performance in terms of estimation error, estimation bias and recovery rate under high signal-to-noise ration (SNR).Some issues that need further investigation are also given at the end of this thesis.