Beam control using the parametric adaptive matched filter STAP approach

Michels, J.H.; Roman, J.R.; Himed, Braham

doi:10.1109/nrc.2003.1203433

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…The SINR loss curves for each bistatic pair are generated using equations (15) and (16). For plotting the probability of detection as a function of SINR, we fix the probability of false alarm to 10 −3 and generate data samples for both H 0 and H 1 .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The part of equation of (13) in parenthesis can be viewed as temporal whitening, and the multiplication of D −1/2 P performs the spatial whitening. In [16], a matrix equivalent form for the reduced order AR filter was derived, and it was shown that it can be specified in the form of a weight vector given by…”

Section: Parametric Matched Filtermentioning

confidence: 99%

“…The form of the matrix C is based on the AR co-efficients. For a full derivation, see [16]. The SINRs for the optimum (clairvoyant) and the PMF are given by…”

Section: Parametric Matched Filtermentioning

confidence: 99%

See 2 more Smart Citations

Improving multistatic MIMO radar performance in data-limited scenarios

Qureshi

Rangaswamy

Bell

2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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A MIMO Multistatic radar system consists of multiple bistatic MIMO pairs working in potentially different configurations. If a bistatic pair in a Multistatic MIMO radar system employs multiple transmit and receive elements, this increases the dimensionality of the data received over a Coherent Processing Interval (CPI), which in turn increases the training data needed to reliably estimate the covariance matrix. This, coupled with the non-stationarity in the received data resulting from the bistatic geometry further degrades the quality of the covariance matrix estimate used in the adaptive detector. In [1], Bell et al. presented a physics based MIMO clutter model, and showed that lack of training data support renders the MIMO radar unfeasible in that the individual bistatic pairs can outperform the overall MIMO system. For these systems, we need to investigate techniques that perform reasonably well in data limited scenarios. In this paper, we show that the physics based clutter model presented in [1] can be approximated as an AR process of model order 4. This has implications for the amount of data that is needed to reliably estimate the AR parameters. For the purpose of this discussion, we use the optimum AR coefficients for every model order generated using the clairvoyant clutter covariance matrix, and characterize the performance using two metrics: SINR loss, and the probability of detection as a function of SINR.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Parametric Matched Filtermentioning

confidence: 99%

See 1 more Smart Citation

Improving multistatic MIMO radar performance in data-limited scenarios

Qureshi

Rangaswamy

Bell

2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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Reducing the effects of training data heterogeneity in multistatic MIMO radar

Qureshi

Rangaswamy

Bell

2015

2015 49th Asilomar Conference on Signals, Systems and Computers

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A Modified Parametric Adaptive Matched Filter without Dimensionality Loss

Dong

2006

2006 CIE International Conference on Radar

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The parametric adaptive matched filter (PAMF) for space-time adaptive processing (STAP) has been an interesting research area in the airborne radar data processing community over the last decade. The existing PAMF however has a problem of dimensionality loss which in turn causes a loss in coherent processing gain and a loss in Doppler resolution. Proposed is a modified PAMF which uses both forward and backward predictions to eliminate the dimensionality loss. The performance of PAMF using simulated data generated by the high fidelity airborne radar simulation software, RLSTAP, as well as real airborne data collected by the Multi-channel Airborne Radar Measurements (MCARM) system is assessed with the result of the conventional STAP as a benchmark. The PAMF outperforms the STAP in the sample reduced space.

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Beam control using the parametric adaptive matched filter STAP approach

Cited by 7 publications

References 10 publications

Improving multistatic MIMO radar performance in data-limited scenarios

Improving multistatic MIMO radar performance in data-limited scenarios

Reducing the effects of training data heterogeneity in multistatic MIMO radar

A Modified Parametric Adaptive Matched Filter without Dimensionality Loss

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