Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound (2401.11176v3)
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.
- S. Blackman, “Multiple hypothesis tracking for multiple target tracking,” IEEE Aerospace and Electronic Systems Magazine, vol. 19, no. 1, pp. 5–18, 2004.
- T. Ort, I. Gilitschenski, and D. Rus, “Autonomous navigation in inclement weather based on a localizing ground penetrating radar,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3267–3274, 2020.
- Z. Cheng, B. Liao, S. Shi, Z. He, and J. Li, “Co-design for overlaid mimo radar and downlink miso communication systems via cramér–rao bound minimization,” IEEE Transactions on Signal Processing, vol. 67, no. 24, pp. 6227–6240, 2019.
- P. Stoica and A. Nehorai, “Music, maximum likelihood, and cramer-rao bound,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 5, pp. 720–741, 1989.
- F. Xi, Y. Xiang, Z. Zhang, S. Chen, and A. Nehorai, “Joint angle and doppler frequency estimation for mimo radar with one-bit sampling: A maximum likelihood-based method,” IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 6, pp. 4734–4748, 2020.
- S. Venkatasubramanian, S. Gogineni, B. Kang, A. Pezeshki, M. Rangaswamy, and V. Tarokh, “Data-driven target localization using adaptive radar processing and convolutional neural networks,” 2023.
- L. Scharf and L. McWhorter, “Adaptive matched subspace detectors and adaptive coherence estimators,” in Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers, 1996, pp. 1114–1117 vol.2.
- S. Kraut, L. Scharf, and L. McWhorter, “Adaptive subspace detectors,” IEEE Transactions on Signal Processing, vol. 49, no. 1, pp. 1–16, 2001.
- B. Van Veen and K. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE ASSP magazine, vol. 5, no. 2, pp. 4–24, 1988.
- S. Venkatasubramanian, C. Wongkamthong, M. Soltani, B. Kang, S. Gogineni, A. Pezeshki, M. Rangaswamy, and V. Tarokh, “Toward data-driven stap radar,” in 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1–5.
- S. Gogineni, J. R. Guerci, H. K. Nguyen, J. S. Bergin, D. R. Kirk, B. C. Watson, and M. Rangaswamy, “High fidelity rf clutter modeling and simulation,” IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 11, pp. 24–43, 2022.
- S. Venkatasubramanian, S. Gogineni, B. Kang, A. Pezeshki, M. Rangaswamy, and V. Tarokh, “Subspace perturbation analysis for data-driven radar target localization,” in 2023 IEEE Radar Conference (RadarConf23), 2023, pp. 1–6.
- D. Slepian, “Estimation of signal parameters in the presence of noise,” Transactions of the IRE Professional Group on Information Theory, vol. 3, no. 3, pp. 68–89, 1954.