Information Theoretic Criterion for Waveform Selection

Roman, J.R.; Garnham, J.W.; Antonik, Paul

doi:10.1109/sam.2006.1706172

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…The research in [5] described a criterion for waveform selection for the next target measurement in radar target tracking, which is based on maximizing the MI between the state of knowledge of the dynamical model for the target and the radar return arising from a certain waveform. The authors in [6] introduced a criterion for waveform selection in adaptive radar and other sensing applications, which is also based on information theory.…”

Section: Introductionmentioning

confidence: 99%

Minimax Robust MIMO Radar Waveform Design

Yang

Blum

2007

IEEE J. Sel. Top. Signal Process.

146

124

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Abstract-We consider the problem of minimax robust waveform design for multiple-input multiple-output (MIMO) radar based on mutual information (MI) and minimum meansquare error (MMSE) estimation for target identification and classification. Recognizing that a single, precise characterization of target power spectral density (PSD) is rare in practice, we assume the PSD lies in an uncertainty class of spectra bounded by known upper and lower bounds, which markedly relaxes the required target a priori knowledge. Based on this band model, we develop minimax robust waveforms for MIMO radar under both MMSE and MI criteria. These robust waveforms bound the worst-case performance at an acceptable limit, and also could insure performance will be sufficiently good for any PSD in the uncertainty class. Our findings also indicate that the MI and MMSE criteria lead to different minimax robust waveform solutions, which is in contrast to the case of the perfectly known target PSD.Index Terms-Extended radar targets, minimum meansquare error (MMSE), multiple-input multiple-output (MIMO) radar, mutual information (MI), radar waveform design, robust signal processing, target classification and identification.

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Section: Introductionmentioning

confidence: 99%

Minimax Robust MIMO Radar Waveform Design

Yang

Blum

2007

IEEE J. Sel. Top. Signal Process.

146

124

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“…. , K} are the real-valued singular values (sv) of R zx , as defined in (15). Notice that the MIC for the proper Gaussian case is a simple function of a set of scalars, although non-trivial computations are involved to generate those scalars.…”

Section: Shannon Mutual Informationmentioning

confidence: 99%

“…These results (as well as those in [15,16]) were generated following Steps 2-5 in the Waveform selection procedure of Section 4. The simulation parameters are: 120 Hz bandwidth (0-120 Hz), 121-point (N = 121) waveform duration at Nyquist sampling rate, 182 ground scatterers (M = 182), and 62 measurements (K = 62).…”

Section: Simulation-based Examplesmentioning

confidence: 99%

“…Also recently, Venkata and Goodman [14] use the waveform design approach in [12] for target discrimination in a multi-target environment (one true target, many false ones), but again, without regard to the temporal adaptivity of the waveform. Lastly, in the domain of research that involves information-theoretic concepts for waveform design, Roman et al [15,16] present a preliminary and partial version of this paper which considers only waveform type and polarization type selection, respectively, using MI. Those results are extended here to include a discussion of waveform design using the MIC, the formulation of canonical correlations for complex-valued random processes, and the link between these two.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Waveform diversity via mutual information

Roman

Davis

Garnham

et al. 2009

Digital Signal Processing

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“…An information theoretic approach is used in [4] to design radar waveforms suitable for simultaneously estimating and tracking the parameters of multiple targets. The authors in [5] have introduced a criterion for waveform selection in adaptive radar and other sensing applications, which are also based on information theory.…”

Section: Introductionmentioning

confidence: 99%

MIMO radar waveform design with peak and sum power constraints

Arulraj¹,

Jeyaraman

2013

EURASIP J. Adv. Signal Process.

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Optimal power allocation for multiple-input multiple-output radar waveform design subject to combined peak and sum power constraints using two different criteria is addressed in this paper. The first one is by maximizing the mutual information between the random target impulse response and the reflected waveforms, and the second one is by minimizing the mean square error in estimating the target impulse response. It is assumed that the radar transmitter has knowledge of the target's second-order statistics. Conventionally, the power is allocated to transmit antennas based on the sum power constraint at the transmitter. However, the wide power variations across the transmit antenna pose a severe constraint on the dynamic range and peak power of the power amplifier at each antenna. In practice, each antenna has the same absolute peak power limitation. So it is desirable to consider the peak power constraint on the transmit antennas. A generalized constraint that jointly meets both the peak power constraint and the average sum power constraint to bound the dynamic range of the power amplifier at each transmit antenna is proposed recently. The optimal power allocation using the concept of waterfilling, based on the sum power constraint, is the special case of p = 1. The optimal solution for maximizing the mutual information and minimizing the mean square error is obtained through the Karush-Kuhn-Tucker (KKT) approach, and the numerical solutions are found through a nested Newton-type algorithm. The simulation results show that the detection performance of the system with both sum and peak power constraints gives better detection performance than considering only the sum power constraint at low signal-to-noise ratio.

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Information Theoretic Criterion for Waveform Selection

Cited by 8 publications

References 7 publications

Minimax Robust MIMO Radar Waveform Design

Minimax Robust MIMO Radar Waveform Design

Waveform diversity via mutual information

MIMO radar waveform design with peak and sum power constraints

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