Joint angle and velocity-estimation for target localization in bistatic mmWave MIMO radar in the presence of clutter
Joint angle and velocity-estimation for target localization in bistatic mmWave MIMO radar in the presence of clutter
Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar systemin the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D)sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cramer-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived.
Maity, Priyanka
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Srivastava, Suraj
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Jagannatham, Aditya K.
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Hanzo, Lajos
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Maity, Priyanka
c4d75693-90e7-47b6-b6e5-40bae23351f9
Srivastava, Suraj
7b40cb6c-7bc6-402c-8751-24346d39002c
Jagannatham, Aditya K.
ae9274e6-c98c-4e15-a5be-f4eb0fc179ff
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Maity, Priyanka, Srivastava, Suraj, Jagannatham, Aditya K. and Hanzo, Lajos
(2025)
Joint angle and velocity-estimation for target localization in bistatic mmWave MIMO radar in the presence of clutter.
IEEE Transactions on Communications.
(In Press)
Abstract
Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar systemin the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D)sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cramer-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived.
Text
BL_target_sensing_bistatic
- Accepted Manuscript
More information
Accepted/In Press date: 9 June 2025
Identifiers
Local EPrints ID: 504368
URI: http://eprints.soton.ac.uk/id/eprint/504368
ISSN: 0090-6778
PURE UUID: e822adce-9f13-41a4-b3e7-1a742ef9fde5
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Date deposited: 08 Sep 2025 16:53
Last modified: 09 Sep 2025 01:33
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Contributors
Author:
Priyanka Maity
Author:
Suraj Srivastava
Author:
Aditya K. Jagannatham
Author:
Lajos Hanzo
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