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Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound

Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Muralidhar Rangaswamy

TL;DR

This work tackles precise radar target localization of azimuth and velocity by comparing gradient-descent estimation with a data-driven CNN approach in a realistic RFView simulation. It shows that gradient-descent methods can closely approach the CRB for unbiased estimation, but a regression CNN can achieve lower MSE due to bias, not implying CRB violation. The paper derives CRBs for azimuth and velocity, and empirically demonstrates that the CNN reduces MSE relative to traditional methods while exposing the bias-driven nature of its performance. The findings highlight the promise and limitations of data-driven radar localization in cluttered, dynamic environments and motivate bias-aware network design in future work.

Abstract

In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algorithms to reach the theoretical limits of the Cramer Rao Bound (CRB) for the error of the parameter estimates. As an extension, we demonstrate on a realistic simulated example scenario that our earlier presented data-driven neural network model outperforms these traditional methods, yielding improved accuracies in target azimuth and velocity estimation. We emphasize, however, that this improvement does not imply that the neural network outperforms the CRB itself. Rather, the enhanced performance is attributed to the biased nature of the neural network approach. Our findings underscore the potential of employing deep learning methods in radar systems to achieve more accurate localization in cluttered and dynamic environments.

Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound

TL;DR

This work tackles precise radar target localization of azimuth and velocity by comparing gradient-descent estimation with a data-driven CNN approach in a realistic RFView simulation. It shows that gradient-descent methods can closely approach the CRB for unbiased estimation, but a regression CNN can achieve lower MSE due to bias, not implying CRB violation. The paper derives CRBs for azimuth and velocity, and empirically demonstrates that the CNN reduces MSE relative to traditional methods while exposing the bias-driven nature of its performance. The findings highlight the promise and limitations of data-driven radar localization in cluttered, dynamic environments and motivate bias-aware network design in future work.

Abstract

In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algorithms to reach the theoretical limits of the Cramer Rao Bound (CRB) for the error of the parameter estimates. As an extension, we demonstrate on a realistic simulated example scenario that our earlier presented data-driven neural network model outperforms these traditional methods, yielding improved accuracies in target azimuth and velocity estimation. We emphasize, however, that this improvement does not imply that the neural network outperforms the CRB itself. Rather, the enhanced performance is attributed to the biased nature of the neural network approach. Our findings underscore the potential of employing deep learning methods in radar systems to achieve more accurate localization in cluttered and dynamic environments.
Paper Structure (12 sections, 13 equations, 2 figures, 1 table, 2 algorithms)

This paper contains 12 sections, 13 equations, 2 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. RFView® Example Scenario
  3. Signal Model
  4. Evaluations and Error Metric
  5. Gradient Descent Parameter Estimation
  6. Azimuth Estimation
  7. Velocity Estimation
  8. Cramér-Rao Bound
  9. Azimuth Estimation
  10. Velocity Estimation
  11. Empirical Results
  12. Conclusion

Figures (2)

  • Figure 1: The RFView® example scenario map. The blue triangle is the platform location and the red region is the radar processing area for tatget placement.
  • Figure 2: Comparing azimuth and velocity estimation errors of the peak cell midpoint, gradient descent, and regression CNN methods with the CRB.