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| Citation: | ZHENG Guimei, SONG Yuwei, HU Guoping, et al. Height measurement for meter-wave MIMO radar based on block orthogonal matching pursuit preprocessing[J]. Journal of Radars, 2020, 9(5): 908–915. doi: 10.12000/JR20042
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Height Measurement for Meter-wave MIMO Radar Based on Block Orthogonal Matching Pursuit Preprocessing
doi: 10.12000/JR20042
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Abstract
Meter-wave radar has good anti-stealth performance. The waveform diversity of Multiple-Input Multiple-Output (MIMO) radar can result in a higher degree of freedom, which makes MIMO radar more advantageous in detection and parameter estimation. Therefore, meter-wave MIMO radar has been widely studied. The radar height measurement is one of the most important research problems of the meter-wave MIMO radar. The maximum likelihood and generalized multiple signal classification algorithms are effective for measuring the radar height. However, they feature heavy computation complexity. In this paper, a preprocessing method based on Block Orthogonal Matching Pursuit (BOMP) is proposed to reduce the computation. First, the received data of MIMO array are sparse-processed, and then, using a mathematical operation, they are transformed into a signal model suitable for the BOMP algorithm; then coarse angle estimation is obtained using a large search grid. The coarse angle estimation is taken as the initial value, and the MIMO radar beam width as the search range. The simulation results show that the proposed algorithm can effectively reduce the computation of the search-type height measurement algorithm. -
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References
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- Figure 1. Schematic diagram of height measurement of low elevation target with meter wave MIMO radar under specular reflection model
- Figure 2. Support position estimation results based on BOMP sparse recovery algorithm
- Figure 3. Estimation results of maximum likelihood and generalized MUSIC under whole airspace searching
- Figure 4. 10 independent experiments results based on maximum likelihood after BOMP preprocessing
- Figure 5. 10 independent experiments results based on generalized MUSIC after BOMP preprocessing
- Figure 6. RMSE of angle estimation with maximum likelihood and generalized MUSIC algorithms
- Figure 7. RMSE of height measurement with maximum likelihood and generalized MUSIC algorithms
- Figure 8. Comparison results of run time of preprocessing and non-preprocessing algorithms