Article
Temporal Difference Learning Waveform Selection.
JCP 09/2010; 5:13941401. DOI: 10.1109/CCCM.2009.5267516
Source: DBLP
 Citations (18)
 Cited In (0)

Article: Cognitive radar: a way of the future
[Show abstract] [Hide abstract]
ABSTRACT: This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echolocation system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remotesensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.IEEE Signal Processing Magazine 02/2006; · 3.37 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Cognitive radar is a recently proposed approach in which a radar system may adaptively and intelligently interrogate a propagation channel using all available knowledge including previous measurements, task priorities, and external databases. A distinguishing characteristic of cognitive radar is that it operates in a closed loop, which enables constant optimization in response to its changing understanding of the channel. In this paper, we compare two different waveform design techniques for use with active sensors operating in a target recognition application. We also propose the integration of waveform design with a sequentialhypothesistesting framework that controls when hard decisions may be made with adequate confidence. The result is a system that updates multiple target hypotheses/classes based on measured data, customizes waveforms as the class probabilities change, and draws conclusions when sufficient understanding of the propagation channel is achievedIEEE Journal of Selected Topics in Signal Processing 07/2007; · 3.30 Impact Factor 
Article: Cubature Kalman Filters
[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we present a new nonlinear filter for highdimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a sphericalradial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a thirddegree sphericalradial cubature rule that provides a set of cubature points scaling linearly with the statevector dimension. The CKF may therefore provide a systematic solution for highdimensional nonlinear filtering problems. The paper also includes the derivation of a squareroot version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the secondorder statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters.IEEE Transactions on Automatic Control 07/2009; · 2.72 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.