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| Citation: | LI Yi, XIA Weijie, ZHOU Jianjiang, et al. A range-angle joint imaging algorithm for automotive radar systems based on Doppler domain compensation[J]. Journal of Radars, 2023, 12(5): 971–985. doi: 10.12000/JR23097
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A Range-angle Joint Imaging Algorithm for Automotive Radar Systems Based on Doppler Domain Compensation
doi: 10.12000/JR23097
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Abstract
Single snapshot forward-looking imaging technology with high performance and resolution is crucial for enabling the development of automotive radars. However, range migration issues can limit the implementation of coherent integration methods, and improving system resolution is generally difficult due to hardware parameter limitations. Based on the Time-Division Multiplexing Multiple-Input-Multiple-Output (TDM-MIMO) forward-looking imaging systems of automotive millimeter wave radar, this paper proposes Doppler domain compensation and point-to-point echo correction measures for achieving multidomain signal decoupling. However, the accuracy of traditional single-dimension range and angle imaging is limited by the number of finite array elements and significant noise interference. Therefore, this paper proposes a multidomain joint estimation algorithm based on the Improved Bayesian Matching Pursuit (IBMP) method. The Bayesian method is based on the Bernoulli-Gaussian (BG) model, and the estimated parameters and support domain are iteratively updated in this method while adhering to the Maximum a Posteriori (MAP) criterion constraint to achieve the high-precision reconstruction of multidimensional joint signals. The final set of simulation and actual measurement results demonstrate that the proposed method can effectively solve the problem of range migration and improve the angle resolution of radar forward-looking imaging while exhibiting excellent noise robustness. -
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References
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- Figure 1. System configuration diagram
- Figure 2. The flowchart of joint estimation algorithm based on Doppler compensation
- Figure 3. Tree-search-based search strategy
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Figure 4. The iterative process of calculating
$ {{\boldsymbol{c}}}_q $ - Figure 5. Point target simulation
- Figure 6. The velocity profiles of simulation target
- Figure 7. Doppler channel compensation effect
- Figure 8. Range-angle simulation point target distribution and comparison of imaging results
- Figure 9. NMSE comparison of range-angle estimation
- Figure 10. Comparison of imaging results of measured targets under Doppler walking and Doppler ambiguity
- Figure 11. The velocity profiles of measured target
- Figure 12. Comparison of point cloud imaging results from 400 frames of measured data (the color indicates the velocity of the target)
- Figure 13. Measured imaging results comparison (the color indicates the velocity of the target)