Knowledge-Aided STAP Using Low Rank and Geometry Properties

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the fact that the clutter subspace is only determined by the space-time steering vectors, {red}{where the Gram-Schmidt orthogonalization approach is employed to compute the clutter subspace. Specifically, for a side-looking uniformly spaced linear array, the} algorithm firstly selects a group of linearly independent space-time steering vectors using LRGP that can represent the clutter subspace. By performing the Gram-Schmidt orthogonalization procedure, the orthogonal bases of the clutter subspace are obtained, followed by two approaches to compute the STAP filter weights. To overcome the performance degradation caused by the non-ideal effects, a KA-STAP algorithm that combines the covariance matrix taper (CMT) is proposed. For practical applications, a reduced-dimension version of the proposed KA-STAP algorithm is also developed. The simulation results illustrate the effectiveness of our proposed algorithms, and show that the proposed algorithms converge rapidly and provide a SINR improvement over existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic Systems, 201

Design methodology in management consulting

In dit proefschrift staat de studie van bedrijfskundige ontwerppraktijken centraal, in het bijzonder in het domein van het organisatie-advieswerk. De probleemstelling is: Welke beargumenteerd productieve strategieën hanteren competente organisatie-adviseurs om bedrijfskundige ontwerpen te creëren?Deze vraag wordt beantwoord in vier stappen. Eerst wordt een theoretisch raamwerk geconstrueerd bestaande uit een schets van de ontwikkeling van de bedrijfskundige ontwerpliteratuur, een achtergrondperspectief over hoe de wereld in elkaar zit waarin ontwerpers leven en werken, en een vocabulaire om ontwerppraktijken en praktijkgebaseerde methodologie te kunnen beschrijven. De tweede stap is het karakteriseren van het domein waarbinnen ontwerppraktijken bestudeerd worden: het organisatie-advieswerk. De derde stap is de empirische exploratie van bedrijfskundige ontwerppraktijken, waarvoor een mix van kwantitatieve en kwalitatieve methoden gebruikt is, te weten een enquete onder Nederlandse adviseurs en een serie diepte-interviews met 24 zeer goede organisatie-adviseurs, die op basis van de enqueteresultaten geselecteerd zijn. In deze empirische studie worden de praktijken van adviseurs geëxploreerd, gebaseerd op het theoretisch raamwerk dat in de eerste stap is geconstrueerd. Een belangrijk aandachtspunt in deze exploratie geldt de eventuele rol van stappenplannen, met de bedoeling om de uitgangsdiagnose van dit onderzoek te testen en verder uit te werken, en om de daadwerkelijke rol van stappenplannen in ontwerppraktijken te achterhalen. De vierde en laatste stap in het onderzoek is het formuleren van productieve ontwerpstrategieën

MIMO radar space–time adaptive processing using prolate spheroidal wave functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space–time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity

Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm

This paper addresses the problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge, under the framework of knowledge-aided space-time adaptive processing (KA-STAP). More precisely, a Gaussian scenario is considered where the covariance matrix of the secondary data may differ from the one of interest. Additionally, some knowledge on the primary data is supposed to be available and summarized into a prior matrix. Two KA-estimation schemes are presented in a Bayesian framework whereby the minimum mean square error (MMSE) estimates are derived. The first scheme is an extension of a previous work and takes into account the non-homogeneity via an original relation. {In search of simplicity and to reduce the computational load, a second estimation scheme, less complex, is proposed and omits the fact that the environment may be heterogeneous.} Along the estimation process, not only the covariance matrix is estimated but also some parameters representing the degree of \emph{a priori} and/or the degree of heterogeneity. Performance of the two approaches are then compared using STAP synthetic data. STAP filter shapes are analyzed and also compared with a colored loading technique