Robust MIMO radar target localization based on lagrange programming neural network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number107574
Journal / PublicationSignal Processing
Online published30 Apr 2020
Publication statusPublished - Sep 2020


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 ℓ1-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 ℓ1-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.

Research Area(s)

  • Lagrange programming neural network (LPNN), Locally competitive algorithm (LCA), Multiple-input multiple-output (MIMO) radar, Outlier, Target localization