## 4 Measurement Setup and Results
### Measurement Results
The proposed LTSP algorithm closely followed, benefiting from its subspace projection and noise mitigation capabilities, particularly at medium-to-high SNRs. MPM and FFT detection offered moderate performance, with MPM outperforming FFT in low-SNR conditions due to its peak prominence filtering. In contrast, the SVD-based approach struggled at low SNRs and became reliable only at high SNRs due to its sensitivity to noise and computational burden. As the distance increased from 1.2 m to 2.0 m, detection performance slightly degraded for all methods, reflecting the reduced signal strength and increased noise power. Notably, the detection curves shifted rightward in SNR, indicating that higher SNRs were required to maintain the same detection probability at longer ranges. This behavior was consistent with the system model described in Section 2, where the increased two-way path loss in the harmonic frequency band exacerbated phase noise and reduced SNR. Figure 12: Sampled time-domain and frequency-domain representations of the received IF signal measured at a challenging distance of 2.1 m. (**a**) Time-domain beat signal \(y_{\text{norm}}[n]\) prior to range compression. (**b**) Frequency-domain magnitude spectrum \(Y_{\text{norm}}[k]\) obtained via FFT, illustrating the effect of range compression. Despite the increased propagation loss and reduced SNR at this longer distance, a clear spectral peak remained observable, enabling peak-based range estimation. This figure emphasizes the robustness of range compression under low-SNR conditions. Table 2 summarizes the key characteristics of all five evaluated peak detection techniques. It contrasts their detection strategies, sensitivity to phase noise, computational complexity, and suitability for hardware implementation. The proposed LTSP method did not aim to outperform all alternatives in detection accuracy; rather, it bridged the gap between robustness and feasibility. LTSP offered superior performance over FFT and MPM in mid-SNR and phase-noise scenarios, while avoiding the full complexity of SVD-based methods. This table emphasizes LTSP's practical role as a middle ground between high performance and real-time deployability. Figure 14: Probability of detection versus SNR from measurement data at a distance of 1.6 m. Compared to the 1.2 m case, a slight degradation in detection performance was observed across all algorithms due to increased path loss and lower received signal strength. CA-CFAR remained the most robust, achieving early saturation. LTSP continued to outperform MPM and FFT at mid-range SNRs, while SVD-based detection showed delayed improvement, becoming effective only beyond 12 dB. These results highlight the growing impact of range-dependent attenuation on algorithmic performance. Figure 13: Measurement-based probability of detection versus SNR at a distance of 1.2 m. CA-CFAR demonstrated the highest robustness, achieving reliable detection at low SNR values. The proposed LTSP method closely followed, outperforming FFT and MPM, especially in the mid-SNR regime. SVD-based detection showed limited sensitivity at low SNR but improved significantly beyond 10 dB. These results validate the simulation trends under real-world conditions and highlight the trade-off between complexity and robustness across the evaluated methods. Although the LTSP technique did not achieve the highest detection rate across all tested SNR conditions, it offered a meaningful balance by improving robustness compared to FFT and MPM, while significantly reducing the hardware burden of full SVD methods. Its inclusion demonstrates the trade-off space between computational cost and resilience to noise and distortion. Importantly, LTSP is not intended to replace CA-CFAR but rather