Multi-antenna Millimeter-wave Radars: Algorithms and Performance Analysis
Millimeter-wave (mmWave) radars with multi-antenna systems have become popular in numerous automotive and industrial sensing applications. For these applications, target estimation is a crucial function. However, accurately estimating a target’s parameters becomes challenging due to either the limitations of the system parameters or the presence of clutter and interference. To address these challenges, this dissertation focuses on developing robust signal processing algorithms and studying their performance analyses. Direction of arrival (DOA) estimation of a target is a fundamental problem for radar sensors. Super-resolution algorithms like MUltiple SIgnal Classification (MUSIC) have been proposed for better DOA estimation performance compared to classical approaches. MUSIC relies on accurate partitioning of the eigenvectors of the spatial correlation matrix between the signal eigenvectors (i.e., signal subspace) and noise eigenvectors (i.e., noise subspace). In the first part of this dissertation, we present a novel statistical framework for analyzing the resolution performance of the MUSIC algorithm in resolving two closely spaced targets according to the number of noise eigenvectors used in the spectrum evaluation. Using this framework, we derive an analytical expression for the probability of resolution of the MUSIC algorithm. Multiple-input multiple-output (MIMO) radar achieves high angular resolution at the expense of certain limitations of its systems realized using different multiplexing techniques such as time division multiplexing (TDM), frequency division multiplexing (FDM), and code division multiplexing (CDM). Of all these multiplexing techniques, TDM is the appropriate choice for automotive applications due to its low hardware complexity. The latter part of this dissertation focuses on the Doppler ambiguity problem associated with a standard TDM MIMO radar. In a standard TDM MIMO radar, transmitters are activated sequentially according to their natural spatial order. The drawback of the standard TDM MIMO approach is the coupling of velocity and DOA information of the targets. The coupling reduces the unambiguous estimation interval of the Doppler frequencies of the targets by the number of transmit antennas being multiplexed. To solve this problem, we propose a novel cost function for jointly estimating the Doppler frequency and DOA of the targets. In the last part of this dissertation, we address the mutual interference problem between automotive radars. With the increasing demand for radar sensors in automotive applications, this mutual interference between them is inevitable due to their unregulated transmissions. To reliably estimate the target’s parameters, this interference needs to be detected and mitigated. To mitigate automotive interference, we propose a two-stage signal decomposition approach.