Frequency Modulated Continuous Wave (FMCW) radar systems traditionally rely on Fourier-based methods, such as the Fast Fourier Transform (FFT), to estimate target range and velocity. While computationally efficient, these approaches require storing and processing large blocks of data, which can become a bottleneck in memory-constrained or low-latency applications. In this work, we propose a neuromorphic-inspired signal processing method based on adaptive resonate-and-fire (ARF) neurons formulated as a discrete-time dynamical system. Each neuron dynamically adjusts its internal frequency to match dominant frequency components of the input radar signal, enabling direct estimation of target ranges and velocities without computing the full frequency spectrum. The proposed model operates in a sample-by-sample fashion, resulting in memory requirements that scale with the number of tracked targets rather than the signal length. A feedback mechanism is also introduced to enable multiple neurons to lock on distinct frequency components in multi-target cases. Results on simulated and experimental data demonstrate that the method can successfully track multiple targets. Compared to conventional FFT-based approaches, the proposed method offers reduced memory usage proportional only to the number of tracked targets, making it suitable for resource-constrained and edge-based radar applications.

翻译：调频连续波（FMCW）雷达系统传统上依赖基于傅里叶的方法（如快速傅里叶变换，FFT）来估计目标距离和速度。尽管计算高效，这些方法需要存储和处理大规模数据块，这在内存受限或低延迟应用中可能成为瓶颈。本文提出一种基于类脑启发的信号处理方法，采用离散时间动力系统框架下的自适应振激（ARF）神经元模型。每个神经元能动态调整其内部频率以匹配输入雷达信号的主要频率分量，从而无需计算完整频谱即可直接估计目标距离与速度。所提模型以逐样本方式运行，内存需求随跟踪目标数量而非信号长度扩展。进一步引入反馈机制，使多目标场景下不同神经元能锁定各自频率分量。仿真与实验数据结果表明，该方法能成功跟踪多个目标。相比传统基于FFT的方法，所提方法内存占用仅与跟踪目标数成线性比例，特别适合资源受限的边缘计算雷达应用。