###### Abstract  
The range resolution of FMCW radar is critical for accurately distinguishing between multiple objects. Higher range resolution allows for better object separation and more precise location determination. While increasing the bandwidth can improve range resolution, it also raises costs and may be subject to regulatory constraints on the available frequency spectrum. This paper proposes an approach to enhance range resolution by increasing the effective bandwidth through signal processing, specifically by introducing the Estimated Signal Padding (ESP) method. ESP extends the bandwidth by lengthening the signal in the time domain, creating a synthesized signal for each target. The proposed algorithm can improve range resolution by a factor of at least 2.25. Unlike existing methods such as Zero Padding and Neural Network Padding, the proposed algorithm can distinguish targets of varying sizes rather than only targets of the same size. The performance of the algorithm has been validated through MATLAB simulations and FMCW radar experiments. FMCW; FMCW radar; range resolution; DSP +
Footnote †: journal: Applied Sciences  
_Applied Sciences_  
**Citation:** [PERSON]; [PERSON], [PERSON], [PERSON]; [PERSON], [PERSON] Signal Extension Method for Improved Range Resolution of Improved Range Resolution of Frequency-Modulated Continuous Wave Radar in Indoor Indoor Environments. **Citation:** [PERSON]; [PERSON], [PERSON], [PERSON], [PERSON] Signal Extension Method for Improved Range Resolution of Frequency-Modulated Continuous Wave Radar in Indoor Indoor Environments. **Citation:** [PERSON]; [PERSON], [PERSON], [PERSON]; [PERSON] Signal Extensionresolution performance is proportional to bandwidth. A radar with wider bandwidth can transmit more information, leading to more accurate target data. However, this comes with the challenge of requiring higher frequencies. Research is therefore needed to enhance range resolution by increasing bandwidth through signal processing. Various algorithms exist to improve range resolution, including methods that extend the signal length in the time domain to achieve a broader bandwidth. As the length of a signal in the time domain increases, frequency resolution in the frequency domain improves. In other words, as the number of samples in the time domain increases, frequency components can be distinguished more accurately, resulting in effects such as increasing bandwidth. Extending the signal length in the time domain using padding techniques, such as Zero or Mirror Padding can result in a broader frequency bandwidth [18; 19; 20]. Applying this to the FFT algorithm results in finer frequency intervals, thereby improving range resolution. However, since the padding is not a seamless continuation of the original signal, and the padded signal is not identical to the original, it can lead to significant side lobes post-FFT, making it difficult to distinguish between multiple objects. Another method to enhance range resolution involves using phase information [21; 22; 23]. By performing signal processing that combines phase correction with phase and frequency information, range resolution can be improved. This method has the advantage of reducing multipath propagation but may lead to overlap in the time domain, requiring complex algorithms. Thus, it is not suitable for improving the range resolution of low-cost FMCW radar systems. Deep learning techniques offer another approach by extending signal length through neural networks [24; 25; 26]. Using neural network training and inference, range resolution can be enhanced. As deep learning can effectively learn and analyze various signal patterns encountered in radar systems, it can be very effective. However, the drawback is that the network needs to be retrained whenever the environment changes, and the complexity is high due to the substantial computational requirements.