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Robust MIMO radar target localization based on lagrange programming neural network
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DOI：10.1016/j.sigpro.2020.107574.png)
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In a multiple-input multiple-output (MIMO) radar system, there are a number of transmitters and receivers. We can use a set of range measurements from MIMO system to locate a target. Each range measurement is the sum of the transmitter-to-target distance and target-to-receiver distance, which corresponds to elliptic localization. This paper addresses the MIMO radar target localization problem with possibly outlier measurements. We formulate the problem via non-smooth constrained optimization with an l(1)-norm objective function, which is non-differentiable, and the Lagrange programming neural network (LPNN) is adopted as the solver. As the LPNN framework cannot handle non-differentiable objective functions, we utilize two techniques, namely, approximation of the l(1)-norm and locally competitive algorithm, to develop two LPNN based algorithms. Moreover, the stability of the LPNN-based algorithms is studied. Simulation results demonstrate that the proposed algorithms outperform two state-of-the-art algorithms. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Multiple-input multiple-output (MIMO) radar
Target localization
Lagrange programming neural network (LPNN)
Locally competitive algorithm (LCA)
Outlier
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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Weighted least squares algorithm for target localization in distributed MIMO radar.png)
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