Lidar, a high-performance active sensing technology, has gained significant traction in recent years across fields such as meteorology, climate science, and environmental monitoring. It serves as a principal methodology for detecting atmospheric physical properties by analyzing echo signals generated from interactions between emitted narrow-pulse lasers and atmospheric constituents, offering high resolution and extended detection range. The quadratic attenuation of lidar echo signals with distance causes single-pulse returns to be overwhelmed by ambient noise, necessitating multiple accumulations (time integration) to enhance the signal-to-noise ratio (SNR). However, conventional lidar data acquisition and integration methods involve onboard storage of data chains collected from a single trigger event. After reaching the predetermined number of accumulations, the stored data are sequentially read out and averaged. This approach introduces a time overhead during data readout, as data acquisition cannot occur simultaneously with readout, creating an acquisition dead time. When the repetition frequency of the lidar system increases to the kHz level, the interplay between acquisition and readout becomes a limitation, resulting in extended dead time and potential pulse omissions.

In this paper, a “read-accumulate-store” intellectual property (IP) was proposed, which was developed within a field-programmable gate array (FPGA) with the dual-port RAM and an adder, enabling temporal integration of corresponding points in a data linked list. Its core innovation lies in concurrent acquisition and integration: during each acquisition cycle, prior results are retrieved, accumulated with current data, and stored iteratively until the preset accumulation count is achieved. The temporal integration IP architecture, implemented in FPGA, comprises the components of photoelectric conversion module (transforms optical signals into electrical signals), analog-to-digital (A/D) module, accumulation (ADD) module, dual-port RAM storage and control unit. To achieve the designed function, the control unit executes the following steps. First, prior to the first trigger, all storage units in the linked list are reset to zero. Second, the first data point is acquired when the first trigger arrives, and the value stored in the first position of the linked list is read. These two values are summed and stored back in the first position. This process repeats N times to complete the sampling for the first trigger. Since the storage units were cleared before the first trigger, each unit contains the result of a single acquisition after this step. For the second trigger, the same procedure as in the previous step is repeated. As the values in each storage unit of the linked list are read during this acquisition, each unit now contains the cumulative result of the corresponding points from the first and second acquisitions after completion. For the M-th trigger, this process continues, with each storage unit holding the cumulative result of the corresponding points from the previous M acquisitions. Finally, once the predetermined number of accumulations, P, is reached, the accumulated data are read out and transmitted to the host computer, yielding the final integrated results. This method ensures efficient data processing by integrating acquisition, accumulation, and storage, thereby facilitating high-fidelity temporal integration for lidar systems.

To verify the effectiveness of the proposed design, a square wave signal, superimposed with a 0.2 V Gaussian white noise, with a period of 100 Hz and an amplitude of 1 V, was used as the input signal for testing. The results demonstrate that the signal becomes progressively smoother with the increase in accumulations, indicating significant noise signal attenuation (Fig. 5). The SNR increased by 42 dB after 1000 accumulations, confirming that the data acquisition and integration module can achieve multiple acquisition accumulation to reduce background noise and improve SNR. For further verification, this module was compared with the DPO5104 digital oscilloscope of Tektronix and the PXI-9826 acquisition card of ADLINK Technology in actual measurements of laser radar echo signals. After correcting the signals obtained by the three devices by the square of the distance, The RSCS, which eliminates the factor of attenuation factor of light transmission, revealed that despite some variations, the waveform trends were fundamentally consistent, and the position data of the thin cloud layer measurements were largely concordant. Finally, this method was applied to a high-repetition-frequency polarized Mie lidar system, with a 5 kHz repetition frequency, achieving data acquisition at a 50 MHz sampling rate and performing over 80000 cumulative averages, successfully determining the extinction and depolarization ratio coefficients (Fig. 8).

A distinctive requirement in the digitization of lidar echo signals, setting it apart from other methods, is the need for temporal integration. While current acquisition techniques effectively handle data collection and temporal integration at low repetition frequencies, limited research addresses these processes at lidar repetition frequencies in the kHz range. This paper presents a novel “read-accumulate-store” method that enables temporal integration of corresponding data points within a linked list structure. This approach simultaneously reads previous acquisition results, accumulates them with current acquisition data, and stores the resulting sum, achieving seamless temporal integration. To implement this method, an intellectual property architecture for temporal integration was developed within a FPGA, utilizing dual-port RAM and an adder. SNR analyses and practical testing demonstrate that this method enables high-speed data acquisition and temporal integration in high-repetition-frequency lidar systems. Additionally, the method offers flexible configuration of parameters, as channels, sampling frequency, sampling length, and integration settings can be adjusted by the hardware description language. Its single-chip integration capability enhances both cost-effectiveness and compactness. Given its versatility and performance, this method shows potential for standardization as a modular component in lidar systems, promoting widespread adoption in advanced sensing applications.