Abstract: To address the susceptibility of laser gas remote-sensing echo signals to multi-source noise in complex environments such as disaster rescue, which leads to signal-to-noise ratio degradation and insufficient long-term stability, equivalent modeling and suppression methods for echo signals under multi-source interference are investigated in accordance with the engineering requirements of laser gas remote sensing. Under weak-absorption and small-perturbation conditions, a linearized analysis is performed on laser power fluctuations, target reflectivity variations, and system noise in disaster-rescue scenarios, and an equivalent signal model is established in which multi-source multiplicative interference is transformed into additive disturbances. On this basis, a joint signal-processing method combining wavelet denoising (WD) and Kalman filtering (KF) is proposed. High-frequency random noise is suppressed in the frequency domain by wavelet multiresolution analysis, whereas low-frequency baseline drift is corrected in the time domain through the prediction-update mechanism of Kalman filtering, thereby enabling layered processing of multi-source noise. A multi-source noise simulation model is constructed in MATLAB, in which low-frequency disturbances (200−600 Hz), high-frequency random noise (5−15 kHz), and mixed-noise conditions are configured. Comparative analyses are carried out for WD, KF, and the combined WD-KF algorithm by using signal-to-noise ratio (SNR), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) as evaluation metrics. Meanwhile, a laser methane remote-sensing experimental platform based on a non-cooperative target is established, and experimental data are acquired at different methane volume fractions and integration times to verify engineering applicability and long-term stability. Under 15 dB Gaussian white noise, an SNR of 39.58 dB and an RMSE of 0.003 1 are achieved after WD-KF processing. Under low-frequency disturbances and high-frequency random noise, the RMSE is maintained on the order of 10⁻⁵, and the PSNR is higher than 30 dB. It is further shown experimentally that, after WD-KF processing, no obvious drift is observed in the Allan deviation curve of the system output signal when the integration time exceeds 100 s, and the long-term fluctuation characteristics are improved. The WD-KF joint signal-processing method based on equivalent additive-interference modeling is therefore shown to regulate the frequency-domain structure and temporal stability of laser gas remote-sensing echo signals under complex noise conditions and to provide a feasible technical approach for signal processing in disaster-rescue scenarios.