TY - GEN
T1 - Numerically efficient mean squared error threshold SNR prediction for adaptive arrays
AU - Richmond, Christ D.
PY - 2010/12/20
Y1 - 2010/12/20
N2 - The method of interval estimation (MIE) is an established technique for extending asymptotic mean squared error (MSE) predictions like the Cramér-Rao bound to lower signal-to-noise ratio. While application of MIE to the adaptive array problem was successful in [1], the numerical integration required to compute the pairwise error probabilities central to MIE is computationally expensive. This is primarily due to the double integral required, moreover, the integrand itself involves the Marcum Q-function, a specialize function that can be represented as an integral or infinite series. System analysis and design often requires computing MSE performance over a wide search space that easily demands hundreds to tens of thousands of repeated calculations of the pairwise error probabilities. To support this demand two approaches to approximating the required error probabilities are explored herein, one yielding a near ∼235 times speedup factor in computation without major loss in accuracy of MSE prediction.
AB - The method of interval estimation (MIE) is an established technique for extending asymptotic mean squared error (MSE) predictions like the Cramér-Rao bound to lower signal-to-noise ratio. While application of MIE to the adaptive array problem was successful in [1], the numerical integration required to compute the pairwise error probabilities central to MIE is computationally expensive. This is primarily due to the double integral required, moreover, the integrand itself involves the Marcum Q-function, a specialize function that can be represented as an integral or infinite series. System analysis and design often requires computing MSE performance over a wide search space that easily demands hundreds to tens of thousands of repeated calculations of the pairwise error probabilities. To support this demand two approaches to approximating the required error probabilities are explored herein, one yielding a near ∼235 times speedup factor in computation without major loss in accuracy of MSE prediction.
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U2 - 10.1109/SAM.2010.5606709
DO - 10.1109/SAM.2010.5606709
M3 - Conference contribution
AN - SCOPUS:78650159261
SN - 9781424489770
T3 - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
SP - 101
EP - 104
BT - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
T2 - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Y2 - 4 October 2010 through 7 October 2010
ER -