MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure

Über p-Verallgemeinerungen sphärisch-invarianter stochastischer Prozesse

In dieser Arbeit werden die Existenzen reeller stochastischer Prozesse mit achsenparallel l_{n,p}elliptisch konturierten endlich-dimensionalen Verteilungen mithilfe des Existenzsatzes von Kolmogorov nachgewiesen. Anschließend werden Eigenschaften skalengemischter p-verallgemeinerter Gauß-Prozesse mit achsenparallel konturierten endlich-dimensionalen Verteilungen wie Stationaritäten, die Existenzen von Erwartungs- und Kovarianzfunktionen und die Abgeschlossenheit unter linearen Transformationen studiert sowie einige Spezialfälle simuliert