Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. To do so, we cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the natural selection of elements at the edges plus the center of the linear array. This contrasts with the typical solutions obtained from minimizing the single-target Cramér-Rao bound.@en

Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. We propose to tackle the sensor selection problem for direction of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated equal power sources on planar arrays. We cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. We illustrate the proposed method through numerical examples related to planar arrays. We show that our method selects a combination of edge and center elements, which contrasts with solutions obtained by minimizing the single-target Cramér-Rao bound.@en

Integrated sidelobe level is a useful measure to quantify robustness of a waveform-filter pair to unknown range clutter and multiple closely located targets. Sidelobe suppression on receive will incur a loss in the signal to noise ratio after pulse compression. We derive a pulse compression filter that has the greatest integrated sidelobe suppression possible for a given acceptable signal to noise ratio loss. The solution is given in a closed form, which can be adjusted using a single parameter to chose between greater sidelobe or interference and noise suppression. We verify the derived filter using simulations, comparing it to other proposed mismatched filter designs. To expand the robustness of the filter, we additionally investigate noise uncertainty robustness. We derive two robustness measures for noise uncertainty and analyze the performance through simulation.@en

Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead of bursts, brings potential performance benefits. Hence, with radar transceiver arrays growing increasingly larger in their number of elements over the years, it may be time to re-evaluate how Doppler velocity can be estimated when using large planar arrays. In this work, we present variance bounds on the estimation of velocity using the Doppler shift as it appears in the array model. We also propose an efficient method of performing the velocity estimation and we verify its performance using Monte Carlo simulations.

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