Closed loop adaptive radar resource allocation

Publications

  1. Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound
    Costas Kokke; Mario Coutino; Laura Anitori; Richard Heusdens; Geert Leus;
    In 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    pp. 1--5, June 2023. DOI: 10.1109/ICASSP49357.2023.10094942
    Keywords: ... Acoustics,array processing,Array signal processing,Computational modeling,cramér-rao bound,Estimation,Hardware,multi-target estimation,sensor selection,Sensors,sparse sensing,Throughput.

    Abstract: ... 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.

  2. Sensor Selection Using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation
    Costas A. Kokke; Mario Coutino; Laura Anitori; Richard Heusdens; Geert Leus;
    Submitted to ISCS2023, July 2023. A 2-page abstract of Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound.. DOI: 10.48550/arXiv.2307.16478
    Keywords: ... Electrical Engineering and Systems Science - Signal Processing.

    Abstract: ... 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. We propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cramér-Rao bound of two uncorrelated sources. 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 selection of elements at the edges plus the center of the linear array.

  3. Single-Pulse Estimation of Target Velocity on Planar Arrays
    Costas Kokke; Mario Coutino; Richard Heusdens; Geert Leus; Laura Anitori;
    In 2022 30th European Signal Processing Conference (EUSIPCO),
    pp. 1811--1815, August 2022. ISSN: 2076-1465. DOI: 10.23919/EUSIPCO55093.2022.9909976
    Keywords: ... Adaptation models,array signal processing,Cramér-Rao bound,Data models,Doppler processing,Estimation,Parallel processing,Planar arrays,pulse-Doppler radar,Signal processing algorithms,Transceivers,velocity estimation.

    Abstract: ... 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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