Ammonia （NH₃） is a fundamental chemical agent extensively utilized in industrial synthesis， agricultural fertilizer production， and large-scale refrigeration systems. However， its leakage poses severe threats to both human physiological health and the atmospheric environment， necessitating the development of high-precision， real-time concentration monitoring techniques. Currently， Tunable Diode Laser Absorption Spectroscopy （TDLAS） combined with Wavelength Modulation Spectroscopy （WMS） is the preferred method for trace gas detection due to its high selectivity and sensitivity. Nevertheless， traditional TDLAS-WMS systems typically employ a low-frequency sawtooth wave for wavelength scanning. This conventional approach faces two critical technical bottlenecks： first， the inherent non-linearity and discontinuous nature of the sawtooth waveform lead to spectral energy dispersion and significant baseline fitting inaccuracies； second， the sawtooth scanning often overlaps with low-frequency environmental noise and mechanical vibrations， which expands the noise integration bandwidth of the lock-in amplifier and consequently limits the Signal-to-Noise Ratio （SNR） in complex industrial scenarios.

To overcome these limitations， this paper proposes an optimized detection method based on dual-sinusoidal compound modulation for second-harmonic signal extraction. The core innovation lies in the simultaneous application of a low-frequency sinusoidal scanning signal （

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） to achieve precise control of the laser frequency. Unlike the abrupt transitions of a sawtooth wave， the smooth and continuous nature of the dual-sinusoidal waveform effectively eliminates time-d

omain non-linearities， thereby suppressing baseline drifts at the source. Theoretically， this compound modulation concentrates the signal energy more efficiently near the specific harmonic sidebands （

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）， which significantly reduces the bandwidth requirements for the detection system and enhances its immunity to

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noise. By utilizing narrow-band lock-in amplification to extract the normalized second-harmonic （2

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） signal， the proposed method ensures a more robust and stable representation of the gas absorption line shape， providing a theoretical foundation for higher detection accuracy and lower cost.

The experimental validation was conducted using a 1 512 nm Distributed Feedback （DFB） laser and a 100 cm quartz gas cell under controlled laboratory conditions. A comprehensive platform based on a high-speed data

acquisition card （NI-USB6363） and LabVIEW was established to compare the performance of the dual-sinusoidal method against the traditional sawtooth-plus-sine modulation. Precision calibration was performed using a combination of spatial cavity and fiber etalons to optimize the modulation depth. The experimental results demonstrate a substantial performance enhancement： the linear fitting degree （

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） between NH₃ concentration and the second-harmonic amplitude improved from 0.991 9 to 0.997 8. Quantitatively， the detection sensitivity was enhanced from 0.089 ppm to 0.053 ppm， representing a significant improvement of approximately 40.5%. Furthermore， Allan variance analysis confirms that the system achieves its optimum detection limit within a shorter integration time of 81.5 seconds， exhibiting superior long-term stability and a more concentrated Gaussian distribution of measurement data compared to the traditional scanning mode. These results quantitatively confirm that the dual-sine approach ensures higher repeatability， lower detection limits， and faster convergence to optimal precision.

In conclusion， this research successfully demonstrates that dual-sinusoidal compound modulation is a highly feasible and stable approach for high-precision trace gas detection. By circumventing the intrinsic flaws of sawtooth scanning-specifically baseline fitting errors and high sensitivity to low-frequency noise—this method provides a new technical pathway for the robust， real-time monitoring of NH₃ industrial emissions. The findings verify that the optimized system can maintain high accuracy and stability even in environments with significant physical disturbances. Future research will focus on further refining the modulation parameters and integrating advanced digital signal processing algorithms， such as adaptive filtering or artificial neural networks， to address multi-component gas interference in even more complex scenarios. Additionally， the development of integrated， FPGA-based hardware platforms and the incorporation of multipass cells will be explored to further enhance the detection limits and promote the widespread application of this technology in portable environmental monitoring and industrial safety systems.