Low Elevation Angle Estimation Method for MIMO Radar in Complex Terrain

WANG Jiajia; GUO Rui; LIU Qi; ZHANG Yue; CHEN Zengping

doi:10.11999/JEIT250236

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WANG Jiajia, GUO Rui, LIU Qi, ZHANG Yue, CHEN Zengping. Low Elevation Angle Estimation Method for MIMO Radar in Complex Terrain[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250236

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WANG Jiajia, GUO Rui, LIU Qi, ZHANG Yue, CHEN Zengping. Low Elevation Angle Estimation Method for MIMO Radar in Complex Terrain[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250236

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Low Elevation Angle Estimation Method for MIMO Radar in Complex Terrain

doi: 10.11999/JEIT250236 cstr: 32379.14.JEIT250236

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: the National Natural Science Foundation of China (U2133216), Science and Technology Planning Project of Key Laboratory of Advanced IntelliSense Technology, Guangdong Science and Technology Department (2023B1212060024), Guangdong Provincial Science and Technology Program (2019ZT08X751), Shenzhen Science and Technology Program (KQTD20190929172704911)

Received Date: 2025-04-07
Rev Recd Date: 2025-09-03

Available Online: 2025-09-09

Abstract

Abstract

Objective Conventional low-elevation angle estimation algorithms for Multiple-Input Multiple-Output (MIMO) radar generally assume a single-path propagation model, which limits their applicability in complex terrain where multipath effects are time-varying. Compressive Sensing (CS) algorithms exploit the sparsity of direct and multipath signals in the spatial domain and remain effective in such environments. Nonetheless, CS-based approaches for MIMO radar require the construction of a two-dimensional grid dictionary, and their computational complexity increases sharply as the number of multipath components grows. Existing complexity-reduction methods sacrifice array aperture, leading to degraded estimation accuracy. To resolve the trade-off between aperture utilization and computational complexity in low-elevation angle estimation for MIMO radar under complex terrain conditions, a tensor-based two-step estimation algorithm is proposed. Methods A three-dimensional tensor observation model is first established to fully preserve the multi-dimensional structure of the received signal, and the tensor signal subspace is extracted using High-Order Singular Value Decomposition (HOSVD). After eliminating redundancy in the tensor subspace, Sparse Bayesian Learning (SBL) is applied to rapidly obtain initial estimates of the low-elevation and multipath angles. These initial results are then refined by an alternating iterative Generalized Multiple Signal Classification (GMUSIC) algorithm, which leverages the complete tensor subspace. The proposed method maintains full array aperture, adapts to scenarios with unknown numbers of multipaths, and achieves a favorable balance between estimation accuracy and computational efficiency. Results and Discussions Simulation results demonstrate that the proposed algorithm achieves high estimation accuracy under both single- and double-reflection paths (Fig. 2–5) compared with other benchmark algorithms, while maintaining lower computational complexity (Table 1). Relative to the sub-optimal Alternative Projection Maximum Likelihood (APML) algorithm, the running speed is improved by 92.16%. In addition, the method remains robust under time-varying multipath conditions (Fig. 6) without requiring prior knowledge of the spatial distribution of reflection paths. Validation with real measured data (Fig. 8–10) further confirms its practical applicability: 86.95% of estimates fall within the 0–0.4° error range, and the error remains consistently below 0.6° across the observation window. These findings highlight the superior estimation accuracy and reliability of the proposed method, supporting its suitability for real-world engineering applications. Conclusions By integrating tensor modeling, sparse preliminary estimation, and alternating iterative optimization, the proposed algorithm fully exploits the multi-dimensional structure of the received signal and the complete array aperture of MIMO radar. It demonstrates high estimation accuracy while maintaining low computational complexity. Simulation results confirm its effectiveness and robustness in complex terrain, and validation with measured data further verifies its feasibility and engineering applicability. Nonetheless, this study is limited to a single-target scenario with a relatively simple motion trajectory. Future research should extend the method to address complex motion patterns with multiple targets.
- Low-elevation angle estimation,
- Complex terrain,
- Multiple-Input Multiple-Output (MIMO) radar,
- Alternating iteration

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References(28)

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