Robust MIMO radar target localization based on lagrange programming neural network

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.