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In this paper, we develop efficient methods for devising lower complexity receivers that can achieve performance close to the full complexity receivers for passive/active multiple-input multiple-output (MIMO) radar. The method employed eliminates some parts of the test statistic to lower either hardware or software complexity. For the case of spatially uncorrelated reflection coefficients and spatially white clutter-plus-noise, the test statistic requires the computation of a set of matched filters, each matched to a signal from a different transmitter. In this case, our method is equivalent to selecting a specific set of transmitters to provide optimum performance. In the more general case of correlated clutter-plus-noise and reflection coefficients, then the test statistic requires the computation of a larger set of matched filters. These matched filters correlate the clutter-plus-noise free signal received at one receive antenna due to the signal transmitted from some transmit antenna and the signal received at another receive antenna. In the more general case, our algorithm picks the best of these matched filters to implement when the total number of these matched filters one can implement is limited.
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Nonlinear Matched Filters via Chen–Fliess Series
A class of nonlinear matched filters is introduced suitable for detection problems using Chen–Fliess functional series. Such series can be viewed as a noncommutative analogue of Taylor series. They are written in terms of a weighted sum of iterated integrals indexed by words over a noncommuting set of symbols. The primary goal is to identify within this class the set of filters which maximizes the signal-to-noise ratio at a given time instant in order to provide a statistic for detecting a known signal.
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- Award ID(s):
- 1839378
- PAR ID:
- 10205780
- Date Published:
- Journal Name:
- 2020 59th IEEE Conference on Decision and Control
- Page Range / eLocation ID:
- 4796-4801
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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