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Michael A. van Wyk

Publications and source records attributed to Michael A. van Wyk.

2 recordsLinked to original sources

Placing the Beamspace Window on a Hopping Carrier: Bin Drift, Exact Capture and the Cost of Rounding

A frequency-agile radar with a large digital array moves the spatial frequency of its look direction across the beams of a spatial FFT every time it changes carrier, so a beamspace adaptive processor must decide where to put its window at each hop. Because the beamspace transform is orthonormal, the output of the processor depends only on the span of the window, and a hop changes nothing but where the window sits. Centring the window exactly on the fractional bin of the look direction keeps the look vector fully inside it at every carrier, for the price of one phase ramp before the FFT. On a 32 by 32 array over 4 to 8 GHz the look bin drifts by 1.6 to 6.9 bins for look directions from u=0.2 to 60 degrees. Rounding to the nearest bin costs up to 1.34, 0.74 and 0.50 dB of look-direction capture for 3 by 3, 5 by 5 and 7 by 7 windows. In a three-jammer scenario with 41 carriers, a window frozen at the lowest carrier loses 39 to 60 dB at the worst carrier for a look direction of u=0.55, the exact window beats nearest-bin placement by 0.31 to 0.65 dB there, and the advantage reverses for a nine-beam window near broadside, where nearest-bin was 0.61 dB better.

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Closed-Form Subspace Loss and Snapshot-Limited Dimension Selection for Beamspace MVDR Radar

A beamspace minimum variance distortionless response (MVDR) processor adapts a window of B beams instead of all N elements of a large array, trading a subspace loss that falls with B against a covariance estimation loss that rises with B for a fixed number K of training snapshots. We give the subspace loss of a DFT beam window in point jamming in closed form: exactly for any jammer power, and in the high interference-to-noise ratio (INR) limit through the Dirichlet kernel of the array. Combined with the known scaled-Beta law of sample matrix inversion (SMI) with a fixed transform, it gives a window size B*(K) for unloaded SMI. On a simulated 32x32 array with carriers from 4 to 8 GHz, noise jammers and Billingsley land clutter, the exact form is within 0.04 dB of the clairvoyant subspace loss for 25- and 49-beam windows at jammer levels from 10 to 50 dB. For a 9-beam window whose three columns are taken by three jammers near the target's elevation, the clutter that the form leaves out costs up to 5 dB. With the nominal K, B*(K) picks the lowest-loss window in all 30 cases with more than one runnable window. The rule is an oracle: it needs the jammer parameters or the true covariance, and errors of 0.25 bin rms in the jammer directions add a mean loss of 0.04 dB. With diagonal loading the best window was larger still wherever the rule chose more than the smallest window.

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