A Comprehensive Analysis of the Impact of System Parameters on Subspace-based DoA Estimation Performance

This work provides an explanatory analysis of the influence input parameters on performance subspace-based Direction Arrival (DoA) estimation algorithms. The objective this is twofold. First, to drive a Steering Vector (SV) that works for arbitrary array configuration rather than just Uniform Linear Array (ULA) geometry. Second, identify how algorithms affected by tuning parameters. later crucial as it allows optimizing algorithm through selecting optimum set appropriate tradeoff between complexity and based intended applications. Toward end, we firstly SV followed revealing working principle subspace DoA techniques. Secondly, evaluate impact several namely Signal Noise Ratio (SNR), number snapshots, elements, separation available sources, dependency sources conduct our analysis. Numerical simulations over wide range scenarios along with intensive Monte Carlo are conducted show these resolution, accuracy, algorithm. As demonstrated obtained results, class method determined mostly values Furthermore, simulation results computational needs be considered when system chosen