Adaptive data processing system and method for laser altimeter satellite calibration equipment

Abstract

This invention discloses an adaptive data processing system and method for a laser altimeter satellite calibration device, relating to the field of spaceborne laser altimeter and data processing technology. The system includes: an analog signal processing module that amplifies and reduces noise in weak photoelectric signals through high-precision amplification and multi-level filtering; an ADC acquisition module responsible for converting analog signals into digital signals in real time, possessing dynamic sampling rate control and multi-channel sampling capabilities; and a digital signal processing module that, based on a dynamic background model, random forest intelligent classification, and physical model fusion for secondary judgment, adaptively extracts effective laser digital signals in complex environments, achieving high-confidence recognition and false detection suppression. A control module interacts with a host computer via CAN bus communication, enabling command parsing, status monitoring, and adaptive control. This invention has the advantages of effectively improving the accuracy of laser pulse signal recognition, enhancing system environmental adaptability, and ensuring real-time data processing.

Description

Technical Field

[0001] This invention relates to the field of spaceborne laser altimetry and data processing technology, specifically to an adaptive data processing system and method for a laser altimetry satellite calibration device. Background Technology

[0002] In the verification and calibration of the on-orbit measurement accuracy of a spaceborne laser altimeter system, ground calibration is a crucial step in ensuring the accuracy and reliability of the satellite laser altimeter. Ground calibration typically involves deploying photodetector arrays or energy detectors at the experimental site to measure and evaluate the return signals of the laser pulses, thereby obtaining the energy distribution at the laser endpoints, beam direction error, and other key parameters. The quality of the calibration data directly affects the accuracy of the satellite's on-orbit measurements and the effectiveness of the calibration.

[0003] In existing ground calibration technologies, the signals received by laser detectors need to undergo analog signal amplification and filtering before being converted into digital signals by analog-to-digital converters (ADCs), which are then analyzed and controlled by digital signal processing systems. However, traditional signal processing systems have significant shortcomings under complex lighting, atmospheric, and interference conditions. Regarding signal processing accuracy, the received signal amplitude in ground calibration environments is weak and easily affected by solar radiation, cloud scattering, and electronic noise. Traditional fixed threshold or static filtering methods struggle to accurately extract effective laser signals under low signal-to-noise ratio conditions, resulting in low signal recognition rates. In terms of adaptability and intelligent recognition capabilities, existing systems mostly employ traditional algorithms that cannot dynamically adjust detection thresholds according to environmental changes and lack machine learning-based signal recognition mechanisms, causing instability when the solar altitude angle changes, cloud thickness fluctuates, or background radiation intensity fluctuates. Regarding real-time performance and multi-channel synchronous processing capabilities, traditional architectures struggle to achieve real-time filtering, discrimination, and buffering of high-speed data streams, especially under conditions of parallel acquisition by multiple detectors or high-frequency pulse incidence. Processing delays affect the integrity and timing accuracy of the laser echo signal. Furthermore, the lack of coordinated design among the analog signal amplification, filtering, analog-to-digital conversion, digital signal processing, and control modules leads to the cumulative effects of quantization errors, random interference, and processing delays, negatively impacting the accuracy of calibration data and resulting in insufficient overall system robustness. These deficiencies severely restrict the accuracy and reliability of laser altimeter satellite ground calibration, especially under complex environmental conditions, where traditional systems struggle to provide stable, high-confidence signal processing results, failing to meet the urgent need for high-precision calibration in modern spaceborne laser altimetry technology.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] In view of this, the present invention provides an adaptive data processing system and method for laser altimetry satellite calibration equipment, which has the advantages of effectively improving the accuracy of laser pulse signal recognition, enhancing the system's environmental adaptability, and ensuring the real-time performance of data processing.

[0006] In a first aspect, the present invention provides an adaptive data processing system for a laser altimeter satellite calibration device, comprising:

[0007] The analog signal processing module, whose input is connected to the photodetector, is used to amplify and filter the weak electrical signal output by the photodetector to obtain the processed analog signal.

[0008] The ADC acquisition module, whose input is connected to the output of the analog signal processing module, is used to convert the processed analog signal into a digital signal and has dynamic sampling rate control and multi-channel sampling capability.

[0009] A digital signal processing module, whose input is connected to the output of the ADC acquisition module, is used to adaptively identify and determine the input digital signal in order to extract the effective laser pulse signal;

[0010] The control module is connected to the ADC acquisition module, the digital signal processing module, and the host computer, respectively. It is used to execute control logic, manage data, and communicate with the host computer to realize the system's adaptive control and status detection.

[0011] In one optional implementation, the analog signal processing module includes:

[0012] An amplifier circuit, connected to the output terminal of the photodetector, is used to amplify the weak electrical signal using a high-bandwidth, low-noise operational amplifier.

[0013] A filtering circuit, connected to the output of the amplifier circuit, is used to remove noise and interference from the amplified signal using a multi-stage filter to obtain a clean analog signal.

[0014] A protection circuit is provided between the photodetector and the amplifier circuit to provide overvoltage, overcurrent and short-circuit protection for the weak input electrical signal.

[0015] In one optional implementation, the ADC acquisition module includes:

[0016] A high-precision ADC chip, whose input terminal is connected to the output terminal of the analog signal processing module, is used to convert the processed analog signal into a digital signal with a resolution of not less than 12 bits;

[0017] A sampling rate controller, connected to the high-precision ADC chip, is used to dynamically adjust the sampling rate according to environmental conditions and signal type.

[0018] A multi-channel sampling switcher is positioned between the output terminals of multiple sensors and the input terminal of the high-precision ADC chip to enable the selection and parallel acquisition of multi-channel signals.

[0019] In one optional implementation, the digital signal processing module includes:

[0020] The data input and buffer unit is connected to the ADC acquisition module and is used to receive the digital sampling sequence from the ADC acquisition module, and to perform buffering and separation processing on the digital sampling sequence using a double buffer structure.

[0021] The background modeling and noise estimation unit is connected to the data input and caching unit. It is used to perform real-time statistics on the cached and separated data, calculate the mean and standard deviation within the sliding window, and establish a dynamic background model based on the mean and standard deviation.

[0022] A dynamic threshold detection and candidate pulse recognition unit, connected to a background modeling and noise estimation unit, is used to set a detection threshold according to the dynamic background model and to recognize candidate pulses according to the detection threshold.

[0023] The pulse feature recognition and validity determination unit is connected to the dynamic threshold detection and candidate pulse recognition unit, and is used to extract features and determine the validity of candidate pulses, and output a valid laser digital signal;

[0024] The result output and data encapsulation unit is connected to the pulse feature recognition and validity determination unit, and is used to encode and encapsulate the valid laser digital signal and output key parameters; the key parameters include pulse amplitude, arrival time, duration width and confidence level.

[0025] In one optional implementation, the dynamic background model established by the background modeling and noise estimation unit is:

[0026] ;

[0027] in, This represents the average background value within the sliding window. The standard deviation within the sliding window. The first adaptive coefficient is obtained by dynamically adjusting the signal-to-noise ratio, ambient light intensity, solar irradiance, and cloud scattering intensity.

[0028] The detection threshold set by the dynamic threshold detection and candidate pulse recognition unit is:

[0029] ;

[0030] in, and These represent the mean and standard deviation of the background within the sliding window, respectively. The second adaptive coefficient is obtained by dynamically adjusting the signal-to-noise ratio and the real-time noise level.

[0031] In one optional implementation, the pulse feature recognition and validity determination unit includes:

[0032] The feature extraction subunit is used to extract features from candidate pulses to form a pulse feature vector; the pulse feature vector includes peak amplitude, pulse width, rising edge slope, falling edge slope, waveform symmetry features, and signal-to-noise ratio;

[0033] The random forest classification subunit is used to input pulse feature vectors into a pre-trained random forest classification model for intelligent classification and output confidence scores; and to take the first target pulse with a confidence score greater than a preset threshold as an effective laser digital signal.

[0034] The physical model determination module is used to make a secondary determination on second target pulses with a confidence level less than or equal to a set threshold based on physical characteristics and preset empirical thresholds; when the second target pulse meets the determination conditions, the second target pulse is regarded as a valid laser digital signal.

[0035] The determination criteria are as follows:

[0036] ;

[0037] and,

[0038] ;

[0039] and,

[0040] ;

[0041] in, For the amplitude of the sampling point, and These represent the mean and standard deviation of the background within the sliding window, respectively. The first adaptive coefficient, For pulse width, For signal-to-noise ratio, , , All are preset experience thresholds.

[0042] In one optional implementation, the digital signal processing module and the control module interact in real time through a shared storage area to enable rapid response to control commands and high-speed data caching.

[0043] The control module communicates with the host computer via a CAN bus to realize control command parsing, status monitoring of each module, and adaptive control.

[0044] Secondly, the present invention also provides an adaptive data processing method for a laser altimeter satellite calibration device, comprising:

[0045] S1. The weak electrical signal output by the photodetector is amplified and filtered to obtain the processed analog signal;

[0046] S2. Convert the processed analog signal into a digital signal and dynamically adjust the sampling rate and channel selection;

[0047] S3. Adaptively identify and determine the validity of the input digital signal to extract the valid laser pulse signal;

[0048] S4. Transmit the effective laser pulse signal to the host computer, and dynamically adjust the operating parameters and control logic of each module according to the feedback information returned by the host computer.

[0049] As can be seen from the above, the adaptive data processing system and method for laser altimetry satellite calibration equipment provided in this application, by integrating an adaptive signal processing module and a dynamic control mechanism, realizes real-time modeling and threshold adjustment of complex environmental interference, effectively overcoming the shortcomings of traditional systems in terms of insufficient signal recognition accuracy and poor environmental adaptability under low signal-to-noise ratio conditions. It has the advantages of effectively improving the laser pulse signal recognition accuracy, enhancing the system's environmental adaptability, and ensuring the real-time performance of data processing. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the adaptive data processing system of the laser altimeter satellite calibration equipment according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the structure of an analog signal processing module according to an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of a PCB board according to an embodiment of the present invention; wherein, (a) is the top layer of the PCB board; (b) is the bottom layer of the PCB board; (c) is an internal interlayer of the PCB board; and (d) is another internal interlayer of the PCB board.

[0054] Figure 4 This is a block diagram of the operational logic of the ADC acquisition module according to an embodiment of the present invention;

[0055] Figure 5 This is a block diagram of the operational logic of a digital signal processing module according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, the present invention provides an adaptive data processing system for a laser altimeter satellite calibration device, comprising: an analog signal processing module, an ADC acquisition module, a digital signal processing module, and a microcontroller.

[0058] The analog signal processing module is connected to the photodetector at its input, amplifying and filtering the weak electrical signal output by the photodetector to obtain a processed analog signal. The ADC acquisition module is connected to the output of the analog signal processing module at its input, converting the processed analog signal into a digital signal and possessing dynamic sampling rate control and multi-channel sampling capabilities. The digital signal processing module is connected to the output of the ADC acquisition module at its input, adaptively identifying and judging the input digital signal to extract the effective laser pulse signal. The control module is connected to the ADC acquisition module, the digital signal processing module, and the host computer, respectively, to execute control logic, manage data, and communicate with the host computer, realizing adaptive control and status detection of the system.

[0059] In this embodiment, the weak electrical signal output by the photodetector is received by the analog signal processing module, amplified by a high-bandwidth, low-noise operational amplifier, and then filtered through multiple stages to remove noise and interference, thus obtaining a processed analog signal. The processed analog signal is converted into a digital signal by the ADC acquisition module. The sampling rate is dynamically adjusted according to the system operating status, environmental conditions, and data type, and the multi-channel sampling capability enables parallel acquisition of multiple signals. The digital signal processing module adaptively identifies and judges the input digital signal, extracting the effective laser pulse signal. The microcontroller connects to the ADC acquisition module, the digital signal processing module, and the host computer, executing control logic, managing data, and communicating with the host computer to achieve adaptive control and status detection of the system. Each module forms a closed loop through strict input-output connections, ensuring the integrity of the signal processing link and environmental adaptability.

[0060] Therefore, the present invention improves the accuracy of weak signal processing under complex lighting, atmospheric scattering and strong noise conditions, and avoids noise accumulation in the analog stage; the adaptability of signal acquisition under different environments is ensured by dynamic sampling rate control, and quantization distortion is reduced; dynamic adjustment of real-time background improves the accuracy of laser pulse extraction; and optimization of processing delay and parameter matching enhances the real-time performance and reliability of the system.

[0061] In one alternative implementation, such as Figure 2 As shown, the analog signal processing module includes: an amplifier circuit, a filter circuit, and a protection circuit.

[0062] The amplifier circuit is connected to the output of the photodetector and is used to amplify weak electrical signals using a high-bandwidth, low-noise operational amplifier. The filter circuit is connected to the output of the amplifier circuit and is used to remove noise and interference from the amplified signal using a multi-stage filter to obtain a clean analog signal. The protection circuit is located between the photodetector and the amplifier circuit and is used to protect the weak input electrical signal from overvoltage, overcurrent, and short circuit to prevent abnormal signals from damaging the system.

[0063] In this embodiment, the amplifier circuit is used to amplify the weak electrical signal output by the photodetector with high gain and low noise, so that the signal amplitude reaches a level range suitable for analog-to-digital conversion acquisition. The amplifier circuit uses a high-bandwidth, low-noise operational amplifier (such as the OPA series), and comprehensively considers bandwidth, gain, and noise figure to ensure the linearity and signal-to-noise ratio of the amplified signal, adapting to the transmission requirements of wideband signals.

[0064] Filtering circuits are used to remove high-frequency noise and low-frequency interference from signals, ensuring the purity of the signal's spectrum. Filtering circuits include structures such as RC low-pass filters, LC filters, and active filters, which can effectively suppress power supply ripple, common-mode interference, and electromagnetic coupling noise, maintaining the stability of the signal's amplitude-frequency characteristics. By employing multi-stage filters to remove high-frequency and low-frequency noise, the purity and integrity of the signal are guaranteed. Its main purpose is to maintain the integrity of the original characteristics before the signal enters the digitization stage.

[0065] The protection circuit is located between the photodetector and the amplifier circuit, and includes overvoltage protection, overcurrent protection, and short-circuit protection circuits to prevent damage to subsequent circuits caused by abnormal input signals. The protection circuit is composed of transient voltage suppressor diodes (TVS), current-limiting resistors, and self-resetting fuses, and has fast response and automatic recovery functions. It can clamp the voltage and restore normal operation under high voltage pulse or electrostatic discharge conditions.

[0066] It should be noted that, to reduce noise interference, the analog signal processing module adopts a multi-layer PCB structure design, such as... Figure 3 As shown, (a) is the top layer, (b) is the bottom layer, and (c) and (d) are both internal sandwich layers; the signal layer is the probe passing through the top layer (e.g., Figure 3 (a) The part marked in the red dashed box) is detected and then amplified by the underlying device before being output to the underlying detection part (e.g. Figure 3 (b) The part marked in the blue dashed box); the ground layer (i.e., the shielding layer) is secured by mounting holes on the bottom and top PCBs (e.g. Figure 3 (b) After opening the window at the location marked by the green dashed box, it is then fixedly connected to the bottom layer for shielding and to the star grounding structure for communication via brass pillars; the signal layer and ground layer are arranged adjacently to form electromagnetic shielding, and differential signal transmission and short-path wiring are used in key channels to reduce crosstalk; an isolation strip and star grounding structure are provided between the amplifier circuit and the ADC acquisition module to reduce the impact of ground loop noise. Electromagnetic shielding covers are set in key areas outside the module to prevent external electromagnetic fields from interfering with sensitive circuits. To improve system stability, key components inside the module (including operational amplifiers and high-frequency filters) are equipped with miniature heat sinks, and heat dissipation is achieved through thermal pads in contact with the outer shell. Ventilation channels are reserved in the PCB layout for high-heat-generating components to prevent heat accumulation. Through the above structural design, the analog signal processing module achieves high-fidelity amplification, effective filtering and input protection of photoelectric signals, and has low-noise, strong anti-interference and high-reliability signal front-end processing capabilities, providing stable and accurate input signals for subsequent analog-to-digital conversion and digital signal processing modules.

[0067] Through the above technical solutions, the present invention can effectively solve the problem of signal-to-noise ratio degradation caused by the introduction of additional noise during signal amplification in complex lighting and field environments, effectively suppress multi-band interference (such as high-frequency noise caused by solar radiation and low-frequency interference caused by power supply ripple), and provide a complete input protection mechanism to prevent electrostatic discharge or voltage surge from damaging the subsequent circuits, thereby significantly improving the accuracy of signal processing and system reliability of laser altimetry satellite calibration equipment.

[0068] In one alternative implementation, the ADC acquisition module includes: a high-precision ADC chip, a sampling rate controller, and a multi-channel sampling switch.

[0069] The input terminal of the high-precision ADC chip is connected to the output terminal of the analog signal processing module to convert the processed analog signal into a digital signal with a resolution of not less than 12 bits. The sampling rate controller is connected to the high-precision ADC chip to dynamically adjust the sampling rate according to environmental conditions and signal type. The multi-channel sampling switch is set between the output terminals of multiple sensors and the input terminal of the high-precision ADC chip to realize the selection and parallel acquisition of multi-channel signals.

[0070] Specifically, the ADC acquisition module is responsible for converting the amplified and filtered analog signal into a digital signal, and dynamically adjusting the sampling rate through the sampling rate controller. At the same time, the multi-channel sampling switcher selects different sensor inputs to achieve multi-channel parallel acquisition to enhance the system's fault tolerance.

[0071] In this embodiment, the operational logic block diagram of the ADC acquisition module is as follows: Figure 4 As shown, a high-precision ADC chip is used to convert analog signals from the analog signal processing module into high-resolution digital signals, enabling real-time acquisition of sensor output signals (including environmental parameters such as temperature, humidity, and pressure). A model with low temperature drift, high linearity, and high sampling accuracy is selected, with a resolution of at least 12 bits to ensure accurate capture of even subtle signal changes. Simultaneously, to reduce noise impact, the ADC acquisition module uses a differential signal input method, and matching resistors and low-noise filters are added to key sampling paths, thereby improving the accuracy and consistency of signal sampling.

[0072] A sampling rate controller dynamically adjusts the sampling rate based on system operating status, environmental conditions, and data type. For example, the system operates at a normal sampling frequency under stable conditions, and automatically increases the sampling rate during critical data acquisition or when there are significant environmental changes to ensure the integrity and timeliness of signal capture. The sampling rate controller works in conjunction with software algorithms and host computer instructions, automatically optimizing the sampling frequency and channel scheduling based on set task priorities and thresholds, thereby balancing sampling accuracy and system power consumption.

[0073] A multi-channel sampling switcher is used to achieve rapid switching and synchronous sampling between multiple sensor signals. This switcher supports multi-channel parallel acquisition mode; when a channel malfunctions or experiences sampling distortion, it can automatically switch to a backup channel, thereby improving the system's fault tolerance and reliability. Under critical tasks, the system employs a multi-channel redundant sampling mechanism, further improving signal acquisition accuracy and system robustness by comparing and fusing multiple data streams. The ADC acquisition module features dynamic data response and intelligent protection functions. The acquired data, after being processed by digital filtering and denoising algorithms, is compared in real-time with the system's preset safety thresholds. When the detection result exceeds the safe range (excessive temperature or abnormal pressure), the system enters a multi-level response process:

[0074] First, adjust the operating parameters of the relevant equipment (reduce power consumption or improve heat dissipation). If the abnormality persists, the system will automatically trigger a self-test program, record fault information, and report the status to the host computer to ensure safe operation of the system under abnormal conditions. Regarding anti-interference, the ADC acquisition module adopts an independent power supply and grounding layout, uses a multi-layer PCB structure to achieve layered isolation between signals and ground lines, and sets electromagnetic shielding covers in key areas to reduce the impact of external electromagnetic interference. The internal reference voltage source of the module undergoes temperature compensation and low-noise voltage regulation design to ensure sampling stability and accuracy consistency during long-term operation.

[0075] Through the above technical solution, the present invention achieves high-precision digital conversion of multi-source analog signals, and has dynamic sampling, anti-interference and adaptive protection functions, ensuring accurate and stable data acquisition in complex environments, and providing a reliable data foundation for subsequent digital signal processing and system control.

[0076] In one optional implementation, the digital signal processing module includes: a data input and buffering unit, a background modeling and noise estimation unit, a dynamic threshold detection and candidate pulse recognition unit, a pulse feature recognition and validity determination unit, and a result output and data encapsulation unit.

[0077] The data input and buffer unit is connected to the ADC acquisition module to receive digital sampling sequences from the ADC acquisition module and uses a double-buffered structure to buffer and separate the digital sampling sequences. The background modeling and noise estimation unit is connected to the data input and buffer unit to perform real-time statistics on the buffered and separated data, calculate the mean and standard deviation within the sliding window, and establish a dynamic background model based on the mean and standard deviation. The dynamic threshold detection and candidate pulse recognition unit is connected to the background modeling and noise estimation unit to set the detection threshold according to the dynamic background model and identify candidate pulses based on the detection threshold. The pulse feature recognition and validity determination unit is connected to the dynamic threshold detection and candidate pulse recognition unit to extract features and determine the validity of candidate pulses, and output a valid laser digital signal. The result output and data encapsulation unit is connected to the pulse feature recognition and validity determination unit to encapsulate the valid laser digital signal and output key parameters, including pulse amplitude, arrival time, duration, and confidence level.

[0078] In one alternative implementation, the dynamic background model established by the background modeling and noise estimation unit is as follows:

[0079] ;

[0080] in, This represents the average background value within the sliding window. The standard deviation within the sliding window. The first adaptive coefficient is obtained by dynamically adjusting the signal-to-noise ratio, ambient light intensity, solar irradiance, and cloud scattering intensity.

[0081] The detection threshold set by the dynamic threshold detection and candidate pulse recognition unit is:

[0082] ;

[0083] in, and These represent the mean and standard deviation of the background within the sliding window, respectively. The second adaptive coefficient is obtained by dynamically adjusting the signal-to-noise ratio and the real-time noise level.

[0084] In one optional implementation, the impulse feature recognition and validity determination unit includes: a feature extraction subunit, a random forest classification subunit, and a physical model determination module.

[0085] The feature extraction subunit is used to extract features from candidate pulses to form pulse feature vectors. The pulse feature vectors include peak amplitude, pulse width, rising edge slope, falling edge slope, waveform symmetry features, and signal-to-noise ratio. The random forest classification subunit is used to input the pulse feature vectors into a pre-trained random forest classification model for intelligent classification and output confidence scores. The first target pulse with a confidence score greater than a preset threshold is taken as a valid laser digital signal. The physical model determination module is used to make a secondary determination on the second target pulse with a confidence score less than or equal to a set threshold based on physical features and a preset empirical threshold. When the second target pulse meets the determination conditions, the second target pulse is taken as a valid laser digital signal.

[0086] The determination criteria are as follows:

[0087] ;

[0088] and,

[0089] ;

[0090] and,

[0091] ;

[0092] in, For the amplitude of the sampling point, and These represent the mean and standard deviation of the background within the sliding window, respectively. The first adaptive coefficient, For pulse width, For signal-to-noise ratio, , , All are preset experience thresholds.

[0093] In this embodiment, as Figure 5 As shown, the digital signal processing module receives the digital signal from the ADC acquisition module, performs digital filtering and noise suppression on the signal to reduce quantization errors and random interference. Based on this, an adaptive background model is established and dynamic threshold detection is performed to identify candidate pulses. Subsequently, feature parameters such as peak value, pulse width, and signal-to-noise ratio are extracted from the candidate pulses, and a random forest classification algorithm is used for intelligent discrimination. When the confidence level is insufficient, the system further performs a secondary judgment based on the physical feature model, thereby achieving adaptive extraction and false detection suppression of valid laser digital signals, and transmitting the processing results to the control module.

[0094] Its specific functions are as follows:

[0095] (1) Data input and buffer unit:

[0096] This unit receives digital sampling sequences from the ADC acquisition module. To ensure real-time performance and continuity of the data, the system employs a dual-buffer structure to separate sampling and processing: one buffer is responsible for acquiring new data, while the other buffer performs signal analysis and feature extraction in parallel, thus avoiding sampling blocking issues. All input data is timestamped to ensure the synchronization and sequence integrity of multi-channel signals, providing a time reference for subsequent dynamic modeling.

[0097] (2) Background modeling and noise estimation unit:

[0098] This unit performs real-time statistics on the cached and separated data, calculating the mean within a sliding window. and standard deviation And establish a dynamic background model. .

[0099] Among them, the first adaptive coefficient in the background model k The system dynamically adjusts its settings based on signal-to-noise ratio, ambient light intensity, solar irradiance, and cloud scattering intensity. This mechanism allows the system to automatically correct background levels under varying lighting and weather conditions, effectively distinguishing stable background signals from anomalous energy fluctuations and improving the environmental adaptability of signal detection.

[0100] (3) Dynamic threshold detection and candidate pulse recognition unit:

[0101] This unit determines the detection threshold based on the background model. The digital sequence is judged point by point, and when the sampled value The time marker is used as a candidate pulse. Second adaptive coefficient. It dynamically adjusts based on the signal-to-noise ratio and real-time noise level, ensuring that the detection process maintains sensitivity in weak signal environments and stability under strong interference conditions. This method can quickly screen candidate pulses with laser echo characteristics.

[0102] (4) Pulse feature recognition and validity determination unit:

[0103] This unit is divided into two decision stages: random forest classification decision and physical model secondary decision.

[0104] Random Forest Classification and Judgment Stage: First, morphological analysis is performed on candidate impulses to extract peak amplitude A. p Pulse width Rising slope , slope of falling edge Waveform symmetry characteristics and signal-to-noise ratio These characteristic parameters constitute the pulse feature vector:

[0105] .

[0106] Then, using real sample data collected in the calibration field (including laser signals, background, cloud interference, and solar stray light, etc.) as the training set, and taking the pulse feature vector X as input and the category label (laser / interference / background) as output, a random forest classification model is trained.

[0107] Finally, in real-time detection, the extracted pulse features are input into a random forest classification model, which outputs the class and its probability distribution P(class); the class with the highest probability is taken as the judgment result, and the confidence score C=P(laser signal) is output. The confidence score model can be further defined as:

[0108]

[0109] when The system directly determines that the pulse is a valid laser digital signal; when Then, the physical model enters the second determination stage. Among these, To set a threshold.

[0110] Secondary judgment stage of physical model: This stage reviews pulses with insufficient confidence based on physical characteristics and empirical thresholds.

[0111] The judgment criteria include:

[0112] ;

[0113] ;

[0114] ;

[0115] in, For the amplitude of the sampling point, and These represent the mean and standard deviation of the background within the sliding window, respectively. The first adaptive coefficient, For pulse width, For signal-to-noise ratio, , , All of these are preset empirical thresholds. It should be noted that a pulse is only considered a valid laser digital signal when all of the above conditions are met simultaneously.

[0116] This two-layer decision mechanism combines the advantages of machine learning and physical constraints, effectively improving the robustness and accuracy of signal recognition.

[0117] (5) Result output and data encapsulation unit:

[0118] This unit encodes and encapsulates the valid digital information confirmed by the dual-determination mechanism, outputting key parameters such as pulse amplitude, arrival time, duration, and confidence level. This module supports multi-channel synchronous data output and achieves real-time communication with the host computer via a CAN bus. The final extracted results will serve as the basic data input for subsequent energy calibration and signal reconstruction, providing reliable support for the system's accurate measurement and control.

[0119] In one alternative implementation, the control module includes a microcontroller, a memory, and an input / output interface.

[0120] A microcontroller (MCU) executes control logic and data processing tasks; memory and input / output interfaces (I / O interfaces) are used to store programs and running data; the I / O interface provides data interaction functions with other modules and external devices, realizing signal input / output and control command transmission;

[0121] The control module employs a multi-layer PCB layout, with independent signal and power grounds. Differential pair transmission is used for critical signal lines to enhance anti-interference capabilities. Filter capacitors are added at the power input and along the main signal transmission paths. Signal lines and power lines are separated and kept as far away as possible from high-current paths and switching power supplies to reduce electromagnetic interference. The entire module is encapsulated in a grounded metal casing, forming shielding and further enhancing electromagnetic interference immunity. To meet the heat dissipation requirements of the high-performance processor and power devices, the module features large-area aluminum heat sinks on key components, combined with thermally conductive silicone pads to quickly conduct heat to the heat sinks. Ventilation channels are designed around the heat sinks, and a high thermal conductivity casing material is used to accelerate heat release. The module is also equipped with a temperature control system that dynamically adjusts the fan speed based on the ambient temperature, ensuring stable and reliable operation of the control module under various working conditions.

[0122] In one optional implementation, the digital signal processing module and the control module interact in real time through a shared storage area to enable rapid response to control commands and high-speed data caching.

[0123] The control module communicates with the host computer via a CAN bus to realize control command parsing, status monitoring of each module, and adaptive control.

[0124] Specifically, a shared memory area refers to a memory region that can be accessed simultaneously by multiple processing units. It can be implemented using dual-port RAM, shared SRAM, or memory-mapped I / O. Its purpose is to eliminate the overhead of traditional communication protocols and achieve low-latency data exchange between modules. The CAN bus can be understood as a reliable serial communication protocol. It can be implemented using a CAN controller chip in conjunction with a CAN transceiver. Its purpose is to provide a communication link with high anti-interference capability and ensure reliable transmission of control commands in complex electromagnetic environments.

[0125] In this embodiment, the digital signal processing module and the control module interact in real time through a shared memory area to achieve rapid response to control commands and high-speed data caching. The control module communicates with the host computer via a CAN bus to achieve control command parsing, status monitoring of each module, and adaptive control. This design, through the coordinated application of the shared memory area and the CAN bus communication mechanism, effectively solves the processing delay and quantization error problems caused by the lack of efficient collaboration between modules, thereby significantly improving the system's data processing accuracy and real-time response capability under complex lighting, atmospheric scattering, and strong noise environments. Specifically, the shared memory area, as the data exchange hub between the digital signal processing module and the control module, avoids the protocol overhead and waiting time in traditional communication methods, ensuring that control commands are responded to within microseconds. At the same time, the CAN bus communication adopts a standardized data frame format and error detection mechanism, ensuring the reliability of host computer command parsing and the continuity of status monitoring, enabling the system to dynamically adjust operating parameters to adapt to environmental changes. Through the above technical solutions, the system achieves seamless connection of the signal processing link in the calibration experiment, effectively suppressing random interference caused by asynchronous operations between modules, and providing a highly stable data processing foundation for laser altimeter satellite calibration equipment.

[0126] Furthermore, the present invention also provides an adaptive data processing method for a laser altimeter satellite calibration device, comprising:

[0127] S1. The weak electrical signal output by the photodetector is amplified and filtered to obtain the processed analog signal;

[0128] S2. Convert the processed analog signal into a digital signal and dynamically adjust the sampling rate and channel selection;

[0129] S3. Adaptively identify and determine the validity of the input digital signal to extract the valid laser pulse signal;

[0130] S4. Transmit the effective laser pulse signal to the host computer, and dynamically adjust the operating parameters and control logic of each module according to the feedback information returned by the host computer.

[0131] In this embodiment, the entire adaptive data processing process is as follows:

[0132] First, the system receives analog signals from the photodetector. These signals undergo high-fidelity amplification, noise suppression, and safety protection through the amplification, filtering, and protection circuits of the analog signal processing module. The amplified signal is then converted from analog to digital by a high-precision ADC module to generate a high-resolution digital signal. Simultaneously, the sampling rate is dynamically adjusted according to environmental conditions and task requirements, and parallel acquisition can be achieved through a multi-channel sampling switch, improving the system's fault tolerance.

[0133] Secondly, the digital signal processing module receives digital sampling signals from the ADC acquisition module and intelligently identifies and adaptively extracts valid laser digital information. Specifically: First, the data input and buffering unit receives the digital sampling sequence and uses a double-buffered structure to ensure real-time separation of sampling and processing; then, the background modeling and noise estimation unit performs sliding statistical analysis on the input data, calculates the background mean and fluctuation range, and establishes an adaptive dynamic background model to distinguish stable backgrounds from abnormal fluctuations in real time; next, the dynamic threshold detection unit sets a detection threshold based on the signal-to-noise ratio and the background model, and judges and marks candidate pulses point by point in the sampling sequence; then, the pulse feature recognition and validity determination unit extracts the feature parameters of the candidate pulses (such as peak amplitude, pulse width, rising edge, falling edge, signal-to-noise ratio, etc.), and performs intelligent judgment through a random forest classifier, outputting a confidence score C. When C is greater than the set threshold C0, the confidence score is determined. th If C is less than or equal to a set threshold C, it is directly determined as a valid laser digital signal. th Then, the physical model performs a secondary judgment to improve the system's anti-interference capability in complex environments. Finally, the result output and data encapsulation unit encodes, encapsulates, and transmits the effective digital signals from the judgment, outputting key parameters such as pulse amplitude, arrival time, duration, and confidence level, providing accurate input for subsequent calibration calculations and energy analysis.

[0134] Finally, based on the digital signal processing results and instruction parsing results, the control module executes the corresponding control logic and sends control signals to the execution unit through the I / O interface to adjust and operate external devices. Simultaneously, the processed digital signals, control parameters, and key status information are stored to support system monitoring, maintenance, and troubleshooting. Furthermore, operating parameters can be dynamically adjusted based on feedback information to ensure the stability and accuracy of signal acquisition, processing, and control.

[0135] Compared with existing technologies, the digital signal processing module of this invention introduces a dual-judgment mechanism of artificial intelligence algorithms and physical features into the data processing chain of laser altimeter satellite calibration equipment for the first time. This module introduces an effective adaptive digital information extraction model, achieving intelligent identification of laser pulses through multi-level analysis of the output signal of the ADC acquisition module. The system first establishes a dynamic background model to estimate the noise environment in real time, and then sets an adaptive detection threshold based on signal statistical characteristics. On this basis, morphological features of candidate pulses are extracted, including peak amplitude, pulse width, rising edge, falling edge, signal-to-noise ratio, and waveform symmetry, and multi-dimensional feature fusion and classification are achieved through a random forest algorithm. When the intelligent classification confidence is lower than the threshold, a physical threshold model is further used for secondary verification to balance sensitivity and accuracy. Through this dual-layer intelligent judgment mechanism, the system can accurately extract real laser signals under complex lighting and atmospheric conditions, achieving high-precision and highly robust digital signal processing.

[0136] In summary, the adaptive data processing system and method for laser altimeter satellite calibration equipment provided by this invention can achieve high-precision, high-reliability, and real-time signal processing and control in ground calibration experiments, providing a reliable data foundation for on-orbit performance verification of satellite laser altimeters.

[0137] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An adaptive data processing system for a laser altimeter satellite calibration facility, characterized by, The application relates to a weak laser pulse signal acquisition system, which comprises the following parts: an analog signal processing module, which is connected to a photodetector and is used for amplifying and filtering a weak electric signal output by the photodetector to obtain a processed analog signal; an ADC acquisition module, which is connected to an output end of the analog signal processing module and is used for converting the processed analog signal into a digital signal and has dynamic sampling rate control and multi-channel sampling capacity; a digital signal processing module, which is connected to an output end of the ADC acquisition module and is used for adaptively identifying and judging the input digital signal to extract an effective laser pulse signal; the digital signal processing module comprises: a data input and buffer unit, which is connected to the ADC acquisition module and is used for receiving a digital sampling sequence from the ADC acquisition module and buffering and separating the digital sampling sequence by adopting a double-buffer structure; a background modeling and noise estimation unit, which is connected to the data input and buffer unit and is used for performing real-time statistics on the buffered and separated data, calculating a mean value and a standard deviation in a sliding window, and establishing a dynamic background model according to the mean value and the standard deviation; a dynamic threshold detection and candidate pulse identification unit, which is connected to the background modeling and noise estimation unit and is used for setting a detection threshold according to the dynamic background model and identifying a candidate pulse according to the detection threshold; a pulse feature identification and effectiveness judgment unit, which is connected to the dynamic threshold detection and candidate pulse identification unit and is used for extracting features of the candidate pulse and judging the effectiveness of the candidate pulse to output an effective laser digital signal; a result output and data packaging unit, which is connected to the pulse feature identification and effectiveness judgment unit and is used for encoding and packaging the effective laser digital signal to output key parameters; the key parameters include a pulse amplitude, an arrival time, a duration width and a confidence degree; a control module, which is connected to the ADC acquisition module, the digital signal processing module and an upper computer respectively and is used for executing control logic, data management and communication with the upper computer to realize adaptive control and state detection of the system.

2. The system of claim 1, wherein, The analog signal processing module comprises: an amplification circuit, which is connected to an output end of the photodetector and is used for amplifying the weak electric signal by adopting a high-bandwidth and low-noise operational amplifier; a filter circuit, which is connected to an output end of the amplification circuit and is used for removing noise and interference of the amplified signal by adopting a multi-stage filter to obtain a pure analog signal; a protection circuit, which is arranged between the photodetector and the amplification circuit and is used for overvoltage, overcurrent and short-circuit protection of the input weak electric signal.

3. The system of claim 1, wherein, The ADC acquisition module comprises: a high-precision ADC chip, which is connected to an output end of the analog signal processing module and is used for converting the processed analog signal into a digital signal with a resolution not lower than 12 bits; a sampling rate controller, which is connected to the high-precision ADC chip and is used for dynamically adjusting a sampling rate according to environmental conditions and signal types; a multi-channel sampling switcher, which is arranged between a plurality of sensor output ends and an input end of the high-precision ADC chip and is used for realizing selection and parallel acquisition of multi-channel signals.

4. The system of claim 1, wherein, The dynamic background model established by the background modeling and noise estimation unit is: ； wherein, is a background mean value within a sliding window, is a standard deviation within the sliding window, is a first adaptive coefficient; wherein the first adaptive coefficient is dynamically adjusted according to a signal-to-noise ratio, an ambient light intensity, a solar irradiance, and a cloud scattering intensity. The detection threshold set by the dynamic threshold detection and candidate pulse recognition unit is: ； wherein, and are the background mean and standard deviation within the sliding window, respectively, is a second adaptive coefficient; wherein the second adaptive coefficient is dynamically adjusted according to the signal-to-noise ratio and the real-time noise level.

5. The system of claim 1, wherein, The pulse feature recognition and effectiveness determination unit comprises: a feature extraction subunit for performing feature extraction on the candidate pulse to form a pulse feature vector; the pulse feature vector comprises a peak amplitude, a pulse width, a rising edge slope, a falling edge slope, a waveform symmetry feature, and a signal-to-noise ratio; a random forest classification subunit for inputting the pulse feature vector into a pre-trained random forest classification model for intelligent classification and outputting a confidence level; and taking a first target pulse with a confidence level greater than a preset threshold as an effective laser digital signal; a physical model determination module for performing secondary determination on a second target pulse with a confidence level less than or equal to a set threshold based on a physical feature and a preset empirical threshold; and taking the second target pulse as an effective laser digital signal when the second target pulse meets a determination condition; wherein the determination condition is: ； and, ； and, ； wherein, is the amplitude of the sampling point, and are the background mean and standard deviation within the sliding window, respectively, is the first adaptive coefficient, is the pulse width, is the signal-to-noise ratio, , , are all preset empirical threshold values.

6. The system of claim 1, wherein, The digital signal processing module and the control module interact in real time through a shared storage area to realize fast response of control instructions and high-speed data caching; The control module is in communication connection with the upper computer through a CAN bus to realize control instruction analysis, module state monitoring, and adaptive control.

7. An adaptive data processing method for a laser altimeter satellite calibration facility, characterized by, Comprise: S1, amplifying and filtering the weak electrical signal output by the photodetector to obtain a processed analog signal; S2, converting the processed analog signal into a digital signal and dynamically adjusting the sampling rate and channel selection; S3, adaptively recognizing and determining the effectiveness of the input digital signal to extract an effective laser pulse signal; S4, transmitting the effective laser pulse signal to the upper computer and dynamically adjusting the operating parameters and control logic of each module according to the feedback information returned by the upper computer; The S3 comprises: receiving a digital sampling sequence and buffering and separating the digital sampling sequence; performing real-time statistics on the buffered and separated data, calculating the mean and standard deviation in the sliding window, and establishing a dynamic background model according to the mean and standard deviation; setting a detection threshold according to the dynamic background model and recognizing a candidate pulse according to the detection threshold; performing feature extraction and effectiveness determination on the candidate pulse to output an effective laser digital signal; encoding and packaging the effective laser digital signal to output key parameters; the key parameters include pulse amplitude, arrival time, duration width, and confidence level.

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