Dual radar-guided glide path error correction based on the Izhikevich neuron model

TL;DR

This paper introduces a novel dual radar error correction method utilizing the Izhikevich neuron model to simulate biological neuron behavior, effectively reducing measurement errors caused by noise and target reflection.

Contribution

It presents a new neural network-based approach employing the Izhikevich model and STDP for dynamic error correction in dual radar systems.

Findings

Effective suppression of trajectory distortion due to noise and measurement errors.

Improved accuracy in dual radar tracking performance.

Adaptive error correction through biologically inspired neural modeling.

Abstract

Aiming at the ranging and angle measurement errors caused by target reflection characteristics and system noise in dual radar tracking, this paper proposes a dual radar track error correction method based on the Izhikevich neural model. The network uses the dynamic differential equation of the Izhikevich model to simulate the discharge characteristics of biological neurons. Its input layer integrates the coordinate measurement data of the dual radar, and the output layer represents the error compensation amount through the pulse emission frequency. The spike-timing-dependent plasticity (STDP) is used to adjust the neuron connection weights dynamically, and the trajectory distortion caused by system noise and radar ranging and angle measurement errors can be effectively suppressed.