A GNSS receiver hardware module automatic detection method and system

Abstract

This invention provides an automated testing method and system for GNSS receiver hardware modules, relating to the field of electronic equipment testing technology. The method includes: parsing a preprocessed signal dataset and extracting signal feature boundaries based on a convex hull algorithm, calculating key performance indicators such as signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity; comparing these key performance indicators with preset performance thresholds, automatically determining whether the GNSS receiver hardware module is qualified based on the comparison results, and generating a performance judgment result; and automatically generating a structured test report based on the performance judgment result, and outputting the report to a designated terminal or storage location. This invention achieves an efficient and fully automated solution for testing various key performance indicators of GNSS receiver hardware modules by simulating GNSS signal excitation, response signal acquisition, signal processing algorithms, and automated performance judgment criteria.

Description

Technical Field

[0001] This invention relates to the field of electronic equipment testing technology, and in particular to an automated testing method and system for GNSS receiver hardware modules. Background Technology

[0002] Global Navigation Satellite System (GNSS) is the core of Positioning, Navigation and Timing (PNT) services. With the completion of the BeiDou-3 system and the popularization of multi-mode GNSS (BeiDou / GPS, etc.), receivers are developing towards miniaturization, high sensitivity and multi-frequency points, which puts forward higher requirements for the efficiency, accuracy and standardization of their performance testing.

[0003] Current GNSS receiver testing primarily relies on manual methods and single-unit instruments, which may lead to the following problems:

[0004] The testing process involves manual intervention in hardware connection, parameter configuration, and result judgment at each stage, without any automated collaboration. For example, during batch testing, configuring parameters for a single module is time-consuming, and the rate of rework due to human input errors is high. The total time for a single test exceeds 40 minutes, and the daily testing volume is less than 100 units. Furthermore, differences in personnel operating habits affect the consistency of results.

[0005] Using conventional algorithms such as Fourier transform and moving average to process the original signal has a weak ability to suppress non-stationary noise, which may lead to feature extraction deviations. For example, a module was judged as unqualified if the bit error rate exceeded the threshold in the laboratory test, but the actual bit error rate met the standard in the field test. The signal-to-noise ratio estimation error under burst noise can reach 3~5dB, affecting the accuracy of sensitivity detection.

[0006] There is no unified testing standard in the industry. Different manufacturers have significant differences in parameter configuration rules, indicator calculation models, and thresholds. For example, the receiving sensitivity threshold is divided into -130dBm and -140dBm, and the results of similar products may be difficult to compare. Moreover, manually generated test reports may lack timestamps and test identification, making subsequent quality traceability difficult. Tests are mostly carried out in ideal laboratory environments, i.e., static, interference-free, and single-frequency, which may not be able to reproduce real-world scenarios such as weak outdoor signals below -160dBm, multi-frequency interference, and high-dynamic motion. This may result in some modules meeting laboratory standards but experiencing positioning interruptions or signal loss in actual applications. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an automated testing method and system for GNSS receiver hardware modules. By simulating GNSS signal excitation, response signal acquisition, signal processing algorithms, and automated performance judgment criteria, a solution is achieved to efficiently, objectively, and fully automatically test various key performance indicators of GNSS receiver hardware modules.

[0008] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0009] Firstly, an automated detection method based on a GNSS receiver hardware module, the method comprising:

[0010] Step 1: Establish a connection with the GNSS receiver hardware module through a preset communication protocol, and configure a set of test parameters to complete the initialization of the detection environment;

[0011] Step 2: Based on the established connection and configured test parameters, generate an analog GNSS signal containing multiple frequency points and multiple modulation methods, and input the signal into the GNSS receiver hardware module;

[0012] Step 3: In response to the simulated GNSS signal, the response signal output by the GNSS receiver hardware module is acquired to obtain the original response dataset containing intermediate frequency data, navigation message and status parameters; and the original response dataset is processed by a shearing transform algorithm to enhance the signal and suppress noise, generating a preprocessed signal dataset.

[0013] Step 4: Analyze the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate the key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity.

[0014] Step 5: Compare the key performance indicators with the preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate the performance judgment result.

[0015] Step 6: Based on the performance assessment results, automatically generate a structured test report and output the report to a specified terminal or storage location.

[0016] Secondly, an automated detection system based on GNSS receiver hardware modules includes:

[0017] The initialization module is used to establish a connection with the GNSS receiver hardware module through a preset communication protocol and configure a set of test parameters to complete the initialization of the detection environment.

[0018] The signal generation module is used to generate an analog GNSS signal containing multiple frequency points and multiple modulation methods based on the established connection and configured test parameters, and input the signal to the GNSS receiver hardware module;

[0019] The preprocessing module is used to respond to simulated GNSS signals by acquiring the response signals output by the GNSS receiver hardware module to obtain the raw response dataset containing intermediate frequency data, navigation messages, and state parameters; and to perform signal enhancement and noise suppression processing on the raw response dataset using a shearing transform algorithm to generate a preprocessed signal dataset.

[0020] The index calculation module is used to parse the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity.

[0021] The result determination module is used to compare key performance indicators with preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate performance determination results.

[0022] The report output module is used to automatically generate a structured test report based on the performance assessment results and output the report to a specified terminal or storage location.

[0023] Thirdly, a computing device includes:

[0024] One or more processors;

[0025] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0026] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0027] The above-described solution of the present invention has at least the following beneficial effects:

[0028] From environmental initialization and analog signal generation to report output, no manual intervention is required, reducing manpower. Signal features are objectively extracted using the convex hull algorithm, and results are judged based on threshold quantization comparison, avoiding human error and ensuring consistent test results. Test parameters are configured to match equipment capabilities, generating analog signals that conform to satellite system standards, ensuring a unified testing environment for different batches and models of receivers, and providing comparable results. Calculated core indicators such as acquisition time and tracking accuracy, as well as individual and overall judgment rules, are all based on preset standards. Support for wired or wireless dual-interface connections, adapting to various GNSS hardware including wireless transmission modules and choke coil antennas. Multi-frequency, multi-modulation signals cover the satellite systems the receiver needs to be compatible with, eliminating the need for individual hardware debugging. Noise reduction and signal enhancement through shearing transform filters environmental noise, providing better resolution for analysis. High-quality data reduces calculation errors caused by high noise in raw data; the convex hull algorithm locates stable signal regions, accurately calculates acquisition time and tracking accuracy, and is more consistent with the true characteristics of the signal than traditional feature extraction methods, resulting in more accurate indicator results; the structured report includes timestamps, test sequence identifiers, and complete information on parameters, indicators, and judgments, enabling rapid tracing of the testing process of a receiver and adapting to production quality backtracking and customer acceptance inspection needs; the report can be output to a designated terminal or preset storage location without manual processing, and can be directly used for production screening and R&D improvement, achieving seamless integration of testing and application. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating an automated detection method for GNSS receiver hardware modules provided by an embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of an automated detection system based on a GNSS receiver hardware module provided by an embodiment of the present invention. Detailed Implementation

[0031] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0032] like Figure 1 As shown, an embodiment of the present invention proposes an automated detection method based on a GNSS receiver hardware module, the method comprising the following steps:

[0033] Step 1: Establish a connection with the GNSS receiver hardware module through a preset communication protocol, and configure a set of test parameters to complete the initialization of the detection environment;

[0034] Step 2: Based on the established connection and configured test parameters, generate an analog GNSS signal containing multiple frequency points and multiple modulation methods, and input the signal into the GNSS receiver hardware module;

[0035] Step 3: In response to the simulated GNSS signal, the response signal output by the GNSS receiver hardware module is acquired to obtain the original response dataset containing intermediate frequency data, navigation message and status parameters; and the original response dataset is processed by a shearing transform algorithm to enhance the signal and suppress noise, generating a preprocessed signal dataset.

[0036] Step 4: Analyze the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate the key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity.

[0037] Step 5: Compare the key performance indicators with the preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate the performance judgment result.

[0038] Step 6: Based on the performance assessment results, automatically generate a structured test report and output the report to a specified terminal or storage location.

[0039] In this embodiment of the invention, a precise connection is established with the GNSS receiver hardware module through a preset communication protocol, avoiding detection interruptions caused by unstable connections; unified configuration of test parameters completes environmental initialization, avoiding errors caused by inconsistent parameter settings in different detection scenarios, and ensuring the standardization of the detection environment for receivers of the same or different batches; simulated GNSS signals are generated based on preset test parameters, and signal strength, frequency deviation, modulation accuracy, etc., can be precisely controlled. Compared with relying on real satellite signals, it can provide a more stable standard excitation signal for the receiver, ensuring that the subsequently collected response data can truly reflect the receiver hardware performance; the required simulated signals can be quickly generated and injected, shortening the preparation time for a single detection, and signal parameters can be adjusted as needed to flexibly verify the receiver's performance in extreme scenarios; the raw response dataset containing intermediate frequency data, navigation messages, and status parameters is collected, completely covering the output information of the GNSS receiver hardware module, providing comprehensive data support for subsequent performance index calculations; signal enhancement and noise suppression are achieved through a shearing transform algorithm, which can filter environmental noise in the raw data and highlight the effective signal characteristics. Based on the convex hull algorithm, signal feature boundaries are extracted, which can accurately locate key signal features. The calculated signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity have no performance dimensions that are missed, and can comprehensively verify whether the overall performance of the GNSS receiver meets the standards. The quantitative comparison logic based on key indicators and preset thresholds avoids judgment bias caused by differences in human experience, and ensures that all GNSS receivers are judged according to a unified standard. The structured report can present the detection parameters, raw data summary, performance indicators, and pass / fail judgment results in a logically organized manner, which can facilitate users to quickly obtain core information and reduce the cost of information interpretation.

[0040] In a preferred embodiment of the present invention, step 1 above includes:

[0041] Step 11: Establish a physical connection with the GNSS receiver hardware module through the selected wired or wireless communication interface. This includes: based on the hardware configuration of the GNSS receiver hardware module under test, enumerating the interface types labeled on the module, such as wired interfaces like RS232 / USB-C and wireless interfaces like 4G / LoRa; considering the testing scenario requirements, for example, wired interfaces are preferred for fixed laboratory testing, while wireless interfaces are preferred for outdoor or mobile testing, eliminating interface types incompatible with the scenario; by comparing the transmission rates of the interfaces, wired interfaces must meet a requirement of ≥115200bps to adapt to subsequent parameter transmission requirements, and wireless interfaces must meet a bit error rate ≤10. -6 To ensure the reliability of data transmission, the final wired or wireless communication interface is determined.

[0042] After the interface is selected, the connection detection process is initiated: For wired interfaces, a preset level signal, such as 5V high level and 0V low level, is sent to the interface, and the level response from the interface is continuously monitored. If the feedback signal is consistent with the sent signal for 5 consecutive times, the wired physical connection is determined to be established. For wireless interfaces, such as 4G / LoRa, a connection request frame containing the detection system identifier is sent to the wireless transmission module of the GNSS receiver hardware module. After the receiving module returns the response frame, the signal strength value RSSI in the frame is parsed. If RSSI ≥ -85dBm, which meets the minimum signal strength requirement for outdoor wireless transmission, and the system identifier in the response frame is consistent with the sending identifier, the wireless physical connection is determined to be established.

[0043] Step 12: Based on the established physical connection, send an initialization command to the GNSS receiver hardware module and receive the device identifier and capability information returned by it. Specifically, this includes: determining the frame structure of the initialization command according to the preset communication protocol, including a command type field, a detection system number field, and a check field; wherein, the command type field is fixed with a value of 0x01, which represents the initialization command; the detection system number field is assigned a unique identifier for the detection system, such as TEST-2024-001; the check field is generated by performing a CRC-16 algorithm, that is, performing a CRC operation on the binary data of the command type field and the system number field; concatenating the above fields in the order of command type, system number, and check to form a complete initialization command.

[0044] After receiving the response frame returned by the GNSS receiver hardware module, the first step is to verify the frame check field. Using the same CRC-16 algorithm as the command generation, all data in the response frame except the check field is processed and compared with the check field. If they match, the information is considered valid. Next, the device identification field is extracted. For example, GNSS-INT-001 indicates an integrated receiver, and GNSS-SPL-002 indicates a separate receiver. This is compared with a preset device identification database to determine the specific model of the hardware module. Finally, the capability information field is extracted, including supported satellite systems (BeiDou / GPS / GLONASS), maximum signal processing bandwidth, and receivable power range. This data is converted into structured data, such as: Satellite System: BeiDou, GPS; Maximum Bandwidth: 20MHz; Power Range: -140dBm~-80dBm, providing a basis for subsequent parameter configuration.

[0045] Step 13: Based on the device identification and capability information, configure a set of test parameters that match the GNSS receiver hardware module. These test parameters include the signal center frequency, modulation type, signal power level, and data sampling rate. Specifically, this includes: First, based on the supported satellite systems analyzed in Step 12, such as BeiDou and GPS, determine the standard frequency points for the corresponding satellite systems, i.e., BeiDou B1 frequency point 1561.098MHz and GPS L1 frequency point 1575.42MHz; Second, extract the maximum signal processing bandwidth from the device capability information, such as 20MHz, and verify whether the standard frequency point is within the device's bandwidth coverage range. For example, 1561.098MHz is valid if it falls within the 1550MHz~1570MHz bandwidth; Finally, if the device supports multiple frequency points, combine the testing requirements, such as selecting two frequency points to verify dual-satellite dual-frequency performance, or selecting one frequency point to verify single-frequency performance, to determine the final signal center frequency.

[0046] Based on the device capability information parsed in step 12, including the supported modulation methods, and combined with the signal modulation standards of the corresponding satellite systems (i.e., BeiDou B1 uses BPSK modulation and GPS L1 uses BPSK modulation), modulation types that the device does not support are excluded. For example, if the device only supports BPSK, QPSK is excluded. If the device supports multiple modulation types, such as BPSK and QPSK, the final modulation type is determined according to the testing requirements, such as selecting BPSK consistent with the actual application for bit error rate testing.

[0047] The signal power level configuration calculation process is as follows: First, extract the receivable power range from the device capability information, such as -140dBm to -80dBm; second, combine the performance indicators to be tested, such as selecting a power value close to the lower limit for testing receiving sensitivity, and selecting an intermediate power value for testing normal operating performance, to determine the initial power value; finally, verify whether the initial power value is within the device power range, such as -138dBm being valid if it is within -140dBm to -80dBm, and if it exceeds the range, adjust it to the nearest valid value within the range to determine the final signal power level.

[0048] According to the Nyquist sampling theorem, the sampling rate must be ≥ 2 times the signal bandwidth. First, determine the signal bandwidth corresponding to the configured signal center frequency. For example, the BeiDou B1 signal bandwidth is 2MHz. Calculate the minimum sampling rate: 2 × 2MHz = 4MHz. Second, extract the maximum sampling rate from the equipment capability information, such as 10MHz, and verify whether the minimum sampling rate is ≤ the maximum sampling rate. If 4MHz ≤ 10MHz, it is valid. Finally, to ensure sampling accuracy, select twice the minimum sampling rate, such as 8MHz, as the final data sampling rate. If the equipment's maximum sampling rate is insufficient, select the maximum sampling rate.

[0049] Step 14: Send the test parameters to the GNSS receiver hardware module and receive the acknowledgment signal to complete the initialization of the test environment. This includes: First, converting the signal center frequency, modulation type, signal power level, and data sampling rate configured in Step 13 into the binary data format specified by the protocol, such as converting the frequency 1561.098MHz into hexadecimal 0x5D9F3E8 and the modulation type BPSK into 0x01; Second, splicing the data according to the frame structure of frequency, modulation type, power level, sampling rate, and checksum, where the checksum field uses the CRC-32 algorithm, which is generated by operating on the binary data of the first four fields; Finally, using a frame-by-frame sending method, with each frame data length ≤128 bytes to avoid packet loss in wireless transmission, waiting for the frame acknowledgment signal (ACK) returned by the module after each frame is sent. If no ACK is received within 100ms, retransmission is performed, up to a maximum of 3 times, until all parameter frames have been sent.

[0050] After receiving the parameter confirmation signal returned by the GNSS receiver hardware module, which contains the received parameter data, the system first parses the parameter data in the confirmation signal and compares it one by one with the sent test parameters. For example, the signal center frequency deviation must be ≤±1Hz, the power level deviation must be ≤±0.5dBm, and the modulation type and sampling rate must be completely consistent. Second, the number of consistent parameters is counted. If all parameters are consistent, the parameter sending is considered successful. Finally, an initialization completion flag, such as INIT-OK, is generated, the initialization completion time is recorded, and the detection environment initialization is completed. If there are any parameter inconsistencies, the system returns to step 13 to reconfigure the parameters and send them again until the parameters are confirmed to be consistent.

[0051] In this embodiment of the invention, the selected wired or wireless dual interface is compatible with GNSS hardware of different forms, eliminating the need to design a separate connection method for each hardware. The wired interface can meet the low-latency connection requirements of high-precision detection scenarios, while the wireless interface can adapt to mobile or outdoor scenarios, avoiding connection limitations caused by wired constraints and ensuring reliable establishment of physical connections in different detection scenarios. By receiving device identification and capability information, the core attributes of the current detection hardware can be clearly identified. Key information can be automatically obtained through command interaction, reducing human identification errors and improving the automation level of the detection process. Matching test parameters are configured according to device information to ensure that the test parameters match the hardware capabilities, avoiding hardware damage or invalid detection data caused by parameters exceeding the hardware's support range, thus ensuring detection safety and data validity. By receiving parameter confirmation signals from the hardware module, it can be verified that the test parameters have been successfully sent and recognized by the hardware, avoiding subsequent analog signal injection and data acquisition deviations caused by parameters not taking effect, and ensuring the integrity and reliability of the detection environment initialization.

[0052] In a preferred embodiment of the present invention, step 2 above includes:

[0053] Step 21: Based on the test parameters, generate the corresponding navigation message bitstream by calling the pre-stored satellite ephemeris data. Specifically, this includes: First, extracting the satellite system type corresponding to the signal center frequency in the test parameters configured in Step 13 above. For example, when the signal center frequency is 1561.098MHz, it matches the BeiDou B1 system; when the frequency is 1575.42MHz, it matches the GPS L1 system. Second, from the pre-stored ephemeris database, which contains historical ephemeris data of systems such as BeiDou and GPS, and covers orbital parameters of different satellite numbers, filter out the ephemeris data corresponding to the satellite system, including core information such as the satellite orbit semi-major axis, eccentricity, orbital inclination, and clock correction parameters. Finally, verify the validity of the filtered ephemeris data, that is, determine whether the timestamp of the ephemeris data is within ±7 days of the current detection time, to ensure that the ephemeris parameters are close to the actual satellite operating status, and to avoid navigation message distortion due to ephemeris expiration, and determine the target ephemeris data to be called.

[0054] Based on the matching satellite system navigation message format standards, such as the BeiDou B1 system following the "BeiDou Satellite Navigation System Space Signal Interface Control Document" and the GPS L1 system following the IS-GPS-200 standard, the orbital parameters and clock correction parameters in the ephemeris data are first filled into the corresponding fields according to the message frame structure, such as the superframe, main frame, and subframe of the BeiDou navigation message. Second, a message verification field is added, and the filled message data is verified to generate a verification code, which is then added to the verification field. Finally, the complete message frame, including the parameter field and the verification field, is converted into a binary bit stream, such as converting the semi-major axis value of 10,000 km into a 32-bit binary number, thus completing the generation of the navigation message bit stream.

[0055] Step 22: Based on the signal center frequency and modulation type in the test parameters, perform carrier modulation and spread spectrum processing on the navigation message bit stream to form a baseband digital signal. Specifically, this includes: First, based on the signal center frequency configured in step 13, determine the frequency accuracy requirements of the carrier signal; second, generate a sinusoidal carrier signal of the corresponding frequency using a frequency synthesizer, such as a frequency synthesis circuit based on a phase-locked loop; monitor the frequency stability of the carrier signal in real time, using a frequency meter to measure and record the frequency value every 100ms; if the frequency deviation of 5 consecutive records is ≤±0.1Hz, the carrier signal is deemed qualified; finally, output a stable carrier signal to provide a reference for subsequent modulation processing.

[0056] According to the modulation type configured in step 13, such as BPSK or QPSK, firstly, the 0 and 1 logic levels in the navigation message bit stream are converted into corresponding voltage signals, such as 0 corresponding to -1V and 1 corresponding to +1V; secondly, if the modulation type is BPSK, the converted voltage signal is multiplied by the carrier signal to achieve phase modulation where 1 corresponds to in-phase carrier and 0 corresponds to out-of-phase carrier; if the modulation type is QPSK, the navigation message bit stream is grouped, with the grouping rule being that every 2 binary bits are divided into a group; then, each group of binary bits is processed separately... The phase signal should be mapped to four different phase states, with the following correspondences: a bit group consisting of 00 corresponds to a 0-degree phase, a bit group consisting of 01 corresponds to a 90-degree phase, a bit group consisting of 10 corresponds to a 180-degree phase, and a bit group consisting of 11 corresponds to a 270-degree phase. After the phase state mapping is completed, the mapped phase signal is multiplied by the carrier signal to complete the phase modulation process. Finally, high-frequency noise in the modulated signal is filtered out, and the signal is passed through a low-pass filter with a cutoff frequency of 1.2 times the signal bandwidth to output the modulated intermediate frequency signal.

[0057] Based on the spreading code standard of the corresponding satellite system, such as the C / A code used in the GPS L1 system and the pseudo-random code used in the BeiDou B1 system, the spreading code matching the satellite system is first retrieved from the pre-stored spreading code library. For example, the C / A code has a length of 1023 bits and a code rate of 1.023MHz. Second, the modulated intermediate frequency signal is multiplied with the spreading code to achieve signal spectrum expansion. The spreading gain equals the spreading code length. For example, a 1023-bit spreading code corresponds to approximately 30dB of gain, enhancing the signal's anti-interference capability. Finally, the spread signal is sampled and quantized at a sampling rate of 2 × spreading code rate, such as 2.046MHz, and converted into a digital signal format, such as a 16-bit binary number, forming a baseband digital signal.

[0058] Step 23 involves converting the baseband digital signal into an analog signal via a digital-to-analog converter (DAC), and adjusting its amplitude according to the signal power level in the test parameters to generate the final multi-frequency, multi-modulation analog GNSS signal. Specifically, this includes: First, determining the DAC parameter configuration: based on the data sampling rate configured in Step 13 (e.g., 8MHz), the sampling rate must satisfy the Nyquist criterion of ≥2 × the highest frequency of the baseband digital signal to ensure no aliasing distortion. The DAC's sampling clock frequency is set to the data sampling rate. Second, the baseband digital signal is input into the DAC. The DAC converts each group of digital signals into a corresponding analog voltage signal, such as 0V for digital value 0 and 5V for digital value 65535. Finally, a low-pass filter is used, with a cutoff frequency = data sampling rate / 2 (4MHz), to filter the stepped analog signal output from the DAC, smoothing the signal waveform and outputting a continuous analog baseband signal.

[0059] Measure the initial power of the analog baseband signal using a power meter, with the measurement point located at the output of the low-pass filter. Record the initial power value P0, such as -100dBm. Extract the target signal power level P configured in step 13. t For example, when testing receiver sensitivity, P t =-140dBm, P during normal performance testing t =-110dBm, calculate the power adjustment difference ΔP=P t -P0, such as when Pt=-140dBm, ΔP=-40dBm; finally, select the corresponding signal conditioning module according to ΔP, such as a fixed attenuator when ΔP is negative, and a low-noise amplifier when ΔP is positive. Input the analog baseband signal into the conditioning module, monitor the conditioned signal power in real time, and the power meter continuously measures and records every 50ms until the measured power value matches P. t If the deviation is ≤ ±0.5dBm, the power adjustment is completed; if there are multiple frequency signals, such as Beidou B1 and GPS L1, the above power adjustment process is performed on the analog signal of each frequency point to ensure that the power of each frequency point meets the target value and generate the final multi-frequency multi-modulation analog GNSS signal.

[0060] Step 24: Through the established physical connection, input the analog GNSS signal to the GNSS receiver hardware module. Specifically, this includes: confirming the type of physical connection established in step 11: if it is a wired connection, select an impedance-matched transmission cable, such as a 50Ω coaxial cable, to avoid signal reflection loss, and directly connect the analog GNSS signal to the signal input interface of the receiver hardware module through the cable; if it is a wireless connection, input the analog GNSS signal to the wireless transmission module, such as the 4G module operating frequency band of 1.8GHz and the LoRa module operating frequency band of 433MHz. The module converts the analog signal into a radio frequency signal that conforms to the corresponding wireless standard, such as the 4G module modulating the radio frequency signal according to the LTE protocol, and setting the wireless transmission parameters, such as the 4G module's transmit power ≤23dBm and the LoRa module's spreading factor SF=12.

[0061] A signal reception preparation command is sent to the GNSS receiver hardware module. After the receiver module returns a ready response, simulated GNSS signal transmission is initiated. The signal status during transmission is monitored in real time: for wired connections, the signal waveform at both ends of the transmission cable is monitored using an oscilloscope to ensure that the waveform has no significant distortion, such as amplitude attenuation ≤1dB and phase offset ≤5°; for wireless connections, the received signal strength (RSSI) is monitored through the wireless receiving unit of the receiver hardware module to ensure that RSSI ≥ -85dBm. Finally, a signal reception confirmation command is received from the GNSS receiver hardware module. The command contains the frequency and power information of the received signal. The information in the confirmation command is parsed. If the received frequency matches the test parameters and the received power deviates from the target power by ≤±1dBm, the signal input is considered successful. If no confirmation command is received or the information is inconsistent, the transmission path is rechecked, such as whether the wired cable is loose or the wireless module has dropped the connection. The signal transmission process is re-executed until the receiver confirms successful reception, completing the input of the simulated GNSS signal.

[0062] In this embodiment of the invention, navigation messages are generated based on pre-stored ephemeris data, including real parameters such as satellite orbit and clock correction, ensuring that the messages closely resemble the characteristics of actual satellite signals and providing a real data foundation for subsequent testing. Directly calling pre-stored data avoids real-time ephemeris calculation, reduces system resource consumption, and quickly generates message bitstreams adapted to test parameters, shortening signal generation preparation time. Processing according to the frequency and modulation type of test parameters ensures that the signal format conforms to satellite system standards, guaranteeing receiver recognition and parsing. Spread spectrum processing improves signal noise and interference immunity, simulating anti-interference scenarios for signal transmission in real environments, and more comprehensively verifying the receiver's signal processing capabilities. The generated baseband digital signal facilitates subsequent digital-to-analog conversion and power adjustment, achieving precise digital control of the signal generation process. Digital-to-analog conversion transforms the digital signal into an analog signal that the receiver can receive, completing the conversion from digital processing to physical signals and ensuring that the hardware can actually receive test signals. Adjusting the amplitude according to power level parameters can simulate signals of different intensities, meeting the test requirements for performance indicators such as receiver sensitivity. Utilizing established wired or wireless connections for signal transmission reduces transmission distortion and ensures that the signal received by the receiver is consistent with the generated signal.

[0063] In a preferred embodiment of the present invention, step 3 above includes:

[0064] Step 31: In response to the analog GNSS signal, the response signal output by the GNSS receiver hardware module is acquired in real time through the data acquisition module to obtain the raw response dataset containing intermediate frequency data, navigation message, and status parameters. Specifically, this includes: setting the sampling clock frequency of the data acquisition module according to the data sampling rate configured in step 13; configuring the input channel of the acquisition module according to the output interface type of the GNSS receiver hardware module, with intermediate frequency data connected to the analog acquisition channel and navigation message and status parameters connected to the digital acquisition channel; and setting the data storage format of the acquisition module, such as using 16-bit binary format for intermediate frequency data, ASCII code format for navigation message, and 32-bit floating-point format for status parameters.

[0065] The real-time acquisition and integrity verification calculation process of the response signal is as follows: After starting the acquisition module, the response signal output by the GNSS receiver hardware module is received synchronously: For intermediate frequency data, continuous acquisition is performed at the set sampling rate, storing one 16-bit data per sampling period, and the acquisition duration is consistent with the simulation signal injection duration in step 2, such as 10 minutes; for navigation messages, acquisition is performed frame by frame according to the message frame structure, such as 300 bits per frame for BeiDou navigation messages, and the start timestamp of each frame is recorded; for status parameters, such as the number of satellites tracked by the receiver and the signal-to-noise ratio, acquisition is performed at a frequency of 1 second / time. Periodic data acquisition; during the acquisition process, real-time verification of data integrity: for intermediate frequency data, check whether the interval of continuous sampling data is equal to the sampling period, and if the deviation is ≤1μs, it is considered continuous; for navigation messages, verify the integrity of each frame of data through the frame tail CRC check code, and if the verification fails, mark the frame as invalid and reacquire; for status parameters, check whether the parameter values ​​are within a reasonable range, such as the number of tracked satellites ≤12 and the signal-to-noise ratio ≥0dB, and remove outliers that are outside the range; after the acquisition is completed, the three types of data are associated and integrated according to the timestamp to form the original response dataset.

[0066] Step 32 involves framing the original response dataset to obtain a series of continuous time data frames. Specifically, this includes: first, extracting the sampling rate of the intermediate frequency (IF) data and the frame period of the navigation message from the original response dataset; then, using the navigation message frame period as a benchmark, calculating the amount of IF data contained in a single frame, i.e., 8MHz × 1 second = 8 × 10⁻⁶. 6 One sampling point; secondly, considering the memory capacity of the data processing system, the frame length is determined to be 8×10 corresponding to the amount of intermediate frequency data, 1 navigation message frame, and 1 set of state parameters, which is 1 navigation message frame period. 6 One intermediate frequency sampling point, 300-bit navigation message, and one set of status parameters are used to avoid processing delays caused by excessive data volume in a single frame.

[0067] The original dataset is divided according to the determined frame length: taking the start timestamp of the first navigation message as the reference, the intermediate frequency data within the corresponding 1 second, the navigation message of that frame, and a set of status parameters collected at the same time are extracted to form the first time data frame; subsequently, all the original data are divided sequentially according to the rule of extracting 1 frame every 1 second to form continuous time data frames. For example, 10 minutes of data can be divided into 600 frames; a synchronization identifier is added to the header of each data frame, such as FRAME-XX, where XX is the frame sequence number, which starts from 001 and increments by the timestamp, i.e., the frame start time, to ensure that the frames are continuous in time order and to avoid frame order disorder during subsequent processing.

[0068] Step 33: The shearing transform algorithm is used to decompose each time data frame in the time-frequency domain, calculate the coefficients of each sub-band signal, and set the coefficients below the noise threshold to zero by setting a threshold to achieve noise suppression, thus obtaining the sub-band coefficients after threshold processing. Specifically, for a single time data frame, firstly, the number of decomposition layers for the shearing transform is selected. Based on the signal bandwidth of the intermediate frequency data, such as an intermediate frequency signal bandwidth of 2MHz, it is decomposed into 4 layers, with each layer corresponding to a sub-band bandwidth of 0.5MHz, covering the full signal bandwidth. Secondly, the shearing transform is performed according to the selected number of decomposition layers: the intermediate frequency data of the data frame is used as input, and iterative calculation is used to decompose it into 4 high-frequency sub-band coefficients and 1 low-frequency sub-band coefficient. The navigation message and status parameters are not involved in the transformation and are stored separately. Each sub-band coefficient corresponds to a frequency range, such as the low-frequency sub-band corresponding to 0-0.5MHz, and the high-frequency sub-band corresponding to 0.5-1MHz, 1-1.5MHz, and 1.5-2MHz respectively. Finally, the amplitude and phase information of each sub-band coefficient are recorded to form a coefficient matrix.

[0069] Select sub-band segments in the data frame that have no valid signals, and statistically analyze the mean amplitude (denoted as μ) and standard deviation (denoted as σ) of the sub-band coefficients within these segments. Set the noise threshold to μ+3σ to ensure that more than 99.7% of the noise coefficients are below the threshold, thus reducing false positives for valid signals. Iterate through the coefficient matrix of each sub-band, setting coefficients with amplitudes below the noise threshold to zero. For example, coefficients with amplitudes of 0.2V < threshold 0.3V in a certain high-frequency sub-band are uniformly set to 0, while retaining valid signal coefficients with amplitudes above the threshold. Finally, apply the same noise threshold logic to the navigation message and status parameters. For example, set interference bits with amplitudes below the threshold in the navigation message to zero. This completes the noise suppression of the entire frame of data, resulting in the sub-band coefficients after threshold processing and the purified navigation message and status parameters.

[0070] Step 34 involves reconstructing the sub-band coefficients after thresholding using an inverse shear transform to obtain the denoised signal frame. Specifically, this includes: using the thresholded sub-band coefficients obtained in step 33 as input, performing an inverse shear transform in order from high frequency to low frequency: first, multiplying the four high-frequency sub-band coefficients by their corresponding inverse transform basis functions to obtain the high-frequency reconstructed signal; second, superimposing the high-frequency reconstructed signal with the low-frequency sub-band coefficients, and then filtering using a low-pass filter with the same bandwidth as during decomposition (cutoff frequency 2MHz) to eliminate aliasing interference during superposition; finally, obtaining the reconstructed intermediate frequency (IF) data, which has the same sampling rate and length as the IF data in the original data frame, such as 8×10⁻⁶. 6 One sampling point.

[0071] The reconstructed intermediate frequency (IF) data is integrated with the purified navigation message and status parameters from step 33 according to the structure of IF data, navigation message, and status parameters. Synchronization identifiers and timestamps of the original data frames are added to form denoised signal frames. The reconstruction quality is then verified: the signal-to-noise ratio (SNR) of the denoised IF data is calculated and compared with the SNR before denoising. If the SNR before denoising is 10dB and the SNR after denoising is ≥15dB, it is considered qualified. The CRC check pass rate of the navigation message is checked. If it is ≥99%, it is considered qualified. It is confirmed that there are no abnormal values ​​in the status parameters.

[0072] Step 35: Merge all denoised signal frames in chronological order to generate a preprocessed signal dataset. This includes: First, extracting the header timestamps of all denoised signal frames and sorting them in ascending order of timestamps (e.g., frame number 001 corresponds to time 00:00:00, frame number 002 corresponds to 00:00:01), and so on, removing abnormal frames with duplicate or disordered timestamps (e.g., retaining the first frame if timestamp 00:00:02 appears twice). Second, merging the data frame by frame according to the sorting result: continuously splicing the intermediate frequency (IF) data of each frame in the sampling order, with the end of the first IF data frame followed by the beginning of the second IF data frame without gaps; arranging the navigation messages continuously in frame order, retaining the timestamp of each frame for correlation; and organizing the status parameters into a parameter list in the order of acquisition time. Finally, generating an index for the merged dataset, recording the correspondence between the starting position of the IF data, the navigation message frame number, and the acquisition time of the status parameters, to facilitate quick data location during subsequent analysis.

[0073] Intermediate frequency data is saved as a binary file with the suffix .bin, and navigation messages are saved as text files with the suffix .txt. Each frame occupies one line and includes the frame number, timestamp, and message content. Status parameters are saved as an Excel spreadsheet with columns named timestamp, number of tracked satellites, signal-to-noise ratio, and signal power. At the same time, a dataset description document is generated, recording information such as acquisition time, sampling rate, denoising parameters, and data volume.

[0074] In this embodiment of the invention, the collected intermediate frequency data, navigation messages, and status parameters provide a comprehensive data foundation for subsequent software analysis and performance index calculation. Large volumes of raw data are split into continuous small frames to avoid excessive system resource consumption and processing delays caused by processing too much data at once. After framing, denoising and reconstruction can be performed separately for each time data frame, avoiding the problem of local noise affecting the overall data during processing and improving the accuracy of signal processing. By decomposing sub-frequency bands through shearing transform and setting noise thresholds, environmental noise in the raw data can be filtered out, while preventing effective signal coefficients from being misjudged as noise, ensuring that the core features of the signal are not lost, and providing high-quality data for subsequent performance index calculation. The processed sub-frequency band coefficients are reconstructed into signal frames through inverse transform, ensuring that the denoised signal still retains the temporal characteristics and structure of the original signal. The reconstructed signal frames can be directly identified by subsequent analysis stages without additional format conversion, improving the overall continuity of the detection process. The denoised signal frames are merged in chronological order to restore the temporal continuity of the data, ensuring that subsequent analysis is based on complete time-series data and avoiding index calculation errors caused by data breaks.

[0075] In a preferred embodiment of the present invention, step 4 above includes:

[0076] Step 41 involves performing time-domain and frequency-domain analysis on the preprocessed signal dataset to extract an initial feature sequence containing signal amplitude, phase, and signal-to-noise ratio (SNR). Specifically, this includes: First, following the time frame order of the preprocessed dataset generated in Step 35 (e.g., 1 second per frame), adapting to the frame segmentation logic of Step 32, performing sliding window analysis on the intermediate frequency (IF) data for each frame. The window length is set to 10ms, with a step size of 5ms to ensure the continuity of time-domain features. The mean amplitude and variance of the IF data within each window are calculated. The mean amplitude is obtained by summing all sampling points within the window and dividing by the number of sampling points, retaining two decimal places. The variance is calculated by summing the squared deviations of each sampling point from the mean amplitude and then dividing by the number of sampling points. Simultaneously, by comparing the phase values ​​of adjacent sampling points and taking the absolute value of the difference, the maximum and minimum phase differences within the window are statistically analyzed to extract phase stability features. Second, by associating the state parameters from Step 35, such as the signal-to-noise ratio (SNR), the mean amplitude, variance, extreme phase differences, and SNR of each frame are arranged in chronological order to form a time-domain feature sequence.

[0077] Perform a Fast Fourier Transform (FFT) on each frame of intermediate frequency data, with the number of transform points set to 2. 16Adapt to the 8MHz sampling rate configured in step 13 to ensure a frequency domain resolution ≥ 0.125kHz: Calculate the transformed frequency domain amplitude spectrum, locate the main peak frequency, i.e. the signal center frequency, and compare it with the signal center frequency configured in step 13. The deviation should be ≤ ±0.5kHz to verify the signal frequency domain integrity; Statistically calculate the frequency domain energy ratio within ±1MHz of the main peak frequency. If the energy ratio is ≥ 90%, it is determined to be a concentrated signal.

[0078] Finally, the main peak frequency and frequency domain energy ratio are added to the time domain feature sequence to form an initial feature sequence containing signal amplitude, phase, signal-to-noise ratio and frequency domain characteristics.

[0079] Step 42: Map the initial feature sequence to a two-dimensional feature space to obtain the corresponding feature point set; calculate the minimum convex hull boundary of the feature point set based on the convex hull algorithm, and determine the signal transition boundary and stable feature region based on the calculated minimum convex hull boundary. Specifically, this includes: determining the coordinate axes of the two-dimensional feature space: the horizontal axis is set as the average signal amplitude (V), and the vertical axis is set as the signal-to-noise ratio (SNR) (dB), adapting the core parameters that best reflect the signal stability in the initial feature sequence; traversing the initial feature sequence, and assigning the average amplitude and SNR of each time point to one feature point in the two-dimensional space. For example, if the average amplitude is 0.8V and the SNR is 15dB at a certain time point, the corresponding feature point is (0.8, 15). All feature points form a feature point set, ensuring that the point set covers the entire stage of the signal from startup to stability. For example, the feature points from 0 to 10 seconds after the signal input in step 24 must be included.

[0080] The Graham scan method is used to execute the convex hull algorithm: traverse the feature point set, select the point with the smallest signal-to-noise ratio on the y-axis as the starting point; using the starting point as the reference, calculate the angle between the lines connecting the remaining feature points and the starting point, and sort them in ascending order of the angle; connect the sorted feature points in sequence, and use the cross product to determine and remove concave points. Points with a cross product ≤ 0 are determined to be concave points and need to be removed, finally forming a closed minimum convex hull boundary.

[0081] Regions are divided based on convex hull boundaries: the density of all feature points within the convex hull boundary is counted, and the area of ​​the convex hull is divided into 100 grids on average. The number of feature points in each grid is counted, and regions with a density of ≥8 points / grid are determined as stable feature regions. Regions with a density of <2 points / grid within the convex hull boundary, and located outside the stable feature regions, are determined as transition boundaries, corresponding to the transition stage from unstable to stable signals.

[0082] Step 43: Based on the stable feature region, calculate the signal acquisition time, which is the time interval from the start of signal input to the first time the signal amplitude feature enters and remains within the stable feature region. Specifically, this includes: determining the time starting point, taking the moment in step 24 when the simulated GNSS signal is input to the receiver hardware module as t0, recorded as 0ms, and synchronously recording the timestamp of each feature point in the initial feature sequence, such as t1=10ms, t2=20ms, consistent with the sliding window step size in step 41.

[0083] Traverse the feature point set and monitor whether the feature points fall into a stable feature region in time order: when a feature point, such as at time t... x When the feature first falls into a stable feature region after 500ms, continuous verification is initiated to monitor the subsequent two feature points, i.e., t. x+1 =510ms, t x+2 =520ms; If the next two feature points are both located within the stable feature region, that is, if the three consecutive feature points satisfy the mean amplitude and signal-to-noise ratio within the stable region, then it is determined that the signal has entered the region stably for the first time, and this moment is recorded as t1, which is 500ms; Calculate the signal acquisition time T1=t1-t0=500ms, and the result is accurate to 10ms.

[0084] Step 44: Based on the signal amplitude feature point set collected after entering the stable feature region, calculate the tracking accuracy according to the density of the distribution of the feature point set within the convex hull boundary. The denser the feature point distribution, the higher the tracking accuracy. Specifically, this includes: filtering feature points, extracting all feature points after t1=500ms in step 43, i.e., feature points after the signal stabilizes, a total of N points, such as N=550 points, corresponding to a 5.5-second stabilization period, to ensure that only feature points in the stable signal phase are counted, excluding interference points in the transition phase.

[0085] The density calculation and tracking accuracy determination process is as follows: The stable feature region is divided into 50×50 uniform grids, with the grid side length being 1 / 50 of the horizontal / vertical axis length of the stable region to ensure density statistical accuracy. The number of feature points in each grid is counted, and the average density ρ = N / (50×50) is calculated. For example, when N = 550, ρ = 0.22 points / grid. Density thresholds are set, with preset values ​​of ρ1 = 0.15 points / grid and ρ2 = 0.3 points / grid. If ρ ≥ ρ2, the tracking accuracy is determined to be excellent, indicating extremely dense feature point distribution and stable tracking. If ρ1 ≤ ρ < ρ2, the tracking accuracy is determined to be good, indicating relatively dense distribution and relatively stable tracking. If ρ < ρ1, the tracking accuracy is determined to be poor, indicating sparse distribution and unstable tracking.

[0086] Step 45: Parse the navigation messages in the original response dataset to obtain the received code stream. Compare the received code stream with the expected navigation message bit stream and define a decision threshold based on the convex hull boundary. Calculate the bit error rate based on the decision threshold. Specifically, this includes: extracting the navigation messages from the original response dataset (the navigation messages collected in step 31 above have not undergone clipping transformation to ensure message integrity); parsing according to the message format of the corresponding satellite system, such as splitting the BeiDou B1 message into superframe, main frame, and subframe structure, with subframe 1 containing clock parameters and subframes 2-3 containing ephemeris parameters); extracting the effective data segments of the message, removing the frame header and frame tail check codes, and converting it into a binary received code stream S with a length of Mbit, such as M=30000bit.

[0087] Obtain the expected navigation message bit stream S0. The navigation message bit stream generated in step 21 is ensured to be consistent with the length and content of the received code stream S. Based on the convex hull boundary defined in step 42, a decision threshold is defined. The minimum value V0 of the average amplitude within the convex hull boundary is taken, such as V0=0.5V. When the average amplitude of a bit in the received code stream is ≥V0, it is judged as 1, otherwise it is 0, to avoid bit misjudgment caused by noise. Compare S and S0 bit by bit and count the number of bit errors E, such as E=15 bits. Calculate the bit error rate BER=E / M×100%, such as BER=0.05%. The result is retained to 4 decimal places. The decision threshold reduces the bit error rate deviation caused by non-hardware performance.

[0088] Step 46: Based on the signal acquisition time and tracking accuracy results, gradually reduce the signal power level of the simulated GNSS signal and repeat the acquisition and tracking process. The lowest signal power that the receiver can normally demodulate the navigation message is used as the receiving sensitivity. Specifically, this includes: determining the initial power and adjustment step size: the initial power is the signal power level P0 configured in step 13, such as P0=-110dBm, and the adjustment step size ΔP=-2dBm.

[0089] Adjust the power of the simulated GNSS signal according to P=P0+n×ΔP, n=1, 2, ..., such as P=-112dBm when n=1, P=-114dBm when n=2; after each adjustment, repeat step 2 signal generation and injection, step 43 acquisition time calculation, and step 45 message demodulation verification: if the receiver can still acquire the signal normally, the acquisition time is ≤2 seconds and the navigation message demodulation is successful, and BER≤0.1%, then continue to reduce the power; if after two consecutive adjustments the receiver cannot acquire the signal or demodulation fails, and BER>0.1%, stop the test.

[0090] Record the power value P from the last successful normal acquisition and demodulation. m , such as P m =-142dBm, P mAs the receiver's receiving sensitivity, this value is ensured to be the minimum signal power that can be demodulated normally, while the repeated testing mechanism can avoid the random errors of a single test.

[0091] Step 47: Summarize the calculated key performance indicators of signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity. Specifically, this includes a summary by indicator name, calculation result, judgment standard, and corresponding file requirements: Signal acquisition time: T1 = 500ms, judgment standard ≤ 1 second is acceptable; If the average density value of feature points obtained during the tracking accuracy calculation is less than the preset density threshold corresponding to the excellent level (0.3 grids), such as a density value of 0.22 grids, it needs to be adjusted based on the actual density value calculated from the actual detection. For example, when the actual calculated average density value of feature points is 0.35 grids, this density value is greater than or equal to the density threshold of 0.3 grids corresponding to the excellent level, and the tracking accuracy is judged as excellent; Tracking accuracy level greater than or equal to good level is judged as acceptable; Bit error rate: BER = 0.05%, judgment standard ≤ 0.1% is acceptable; Receiver sensitivity: P m =-142dBm, the judgment standard is ≤-140dBm as qualified, which is suitable for weak signal scenarios; the summarized indicators are stored in a structured format, such as a table, including indicator ID, value, unit, and qualified status.

[0092] In this embodiment of the invention, the extracted signal amplitude, phase, and signal-to-noise ratio features can be directly used as inputs for convex hull algorithm analysis, avoiding deviations in subsequent boundary extraction due to feature loss and ensuring the accuracy of indicator calculations. Time-domain and frequency-domain analysis comprehensively captures the core characteristics of the signal, providing a basis for accurately judging the hardware signal processing capabilities. The minimum convex hull boundary calculated by the convex hull algorithm replaces the method of manual subjective judgment of signal boundaries, avoiding boundary judgment deviations caused by human error. The determination of transition boundaries and stable feature regions provides a clear judgment range for subsequent calculations of signal acquisition time and tracking accuracy, ensuring that indicator calculations have a clear basis. The acquisition time is objectively quantified by using the first entry of the signal amplitude into and sustained within the stable region as the judgment criterion. The acquisition time directly reflects the response speed of the GNSS receiver hardware module to the signal, providing a key basis for performance judgment. The accuracy is judged by the density of feature point distribution within the convex hull boundary, eliminating the need for complex error model calculations and improving the efficiency of indicator calculations. Defining the decision threshold using convex hull boundaries can filter out misjudgments caused by noise interference and reduce bit error rate deviations caused by non-hardware performance factors. Navigation message comparison and bit error rate calculation provide core indicators for evaluating hardware data transmission performance. By gradually reducing power and repeatedly acquiring and tracking, the minimum power required for normal demodulation of the receiver can be accurately found, avoiding threshold misjudgments caused by setting the power all at once. The summarized acquisition time, tracking accuracy, bit error rate, and receiver sensitivity comprehensively cover the core performance dimensions that need to be verified in automated detection.

[0093] In a preferred embodiment of the present invention, step 5 above includes:

[0094] Step 51: Obtain the key performance indicators of signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity. Specifically, this includes: extracting the key performance indicator summary file generated in step 47, such as a structured table, stored in the system's preset detection result directory, and accurately extracting the key performance indicators of signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity by field name: signal acquisition time, unit: ms, e.g., 500ms; tracking accuracy, level: excellent, good, poor, e.g., excellent; bit error rate, unit: %, e.g., 0.05%; receiver sensitivity, unit: dBm, e.g., -142dBm.

[0095] After extraction, an integrity check is performed: it checks whether the key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity all have valid values, and are not empty or zero. If a certain indicator is missing, such as receiver sensitivity not being recorded, the anomaly handling mechanism is triggered, and the indicator is automatically backtracked to step 46 to recalculate.

[0096] Step 52: Compare each key performance indicator with the corresponding performance thresholds pre-stored in the database to obtain the comparison results for each indicator. Specifically, this includes: accessing the system's preset performance threshold database, which is categorized and stored according to receiver model. For example, Beidou integrated receivers and GPS separate receivers correspond to different threshold tables. Read the four types of indicator thresholds corresponding to the current test model: signal acquisition time threshold, such as ≤1000ms; tracking accuracy threshold, such as good or above; bit error rate threshold, such as ≤0.1%; and receiver sensitivity threshold, such as ≤-140dBm.

[0097] The comparison is performed one-to-one according to the index type and threshold type: Signal acquisition time, the extracted value (500ms) is compared with the threshold (1000ms), and 500ms ≤ 1000ms is determined; Tracking accuracy, the grade (excellent) is compared with the threshold (good and above), and excellent ≥ good is determined; Bit error rate, the extracted value (0.05%) is compared with the threshold (0.1%), and 0.05% ≤ 0.1% is determined; Receiver sensitivity, the extracted value (-142dBm) is compared with the threshold (-140dBm), and -142dBm ≤ -140dBm is determined. Note: The smaller the dBm value, the higher the sensitivity.

[0098] Step 53: Based on the comparison results of various indicators, generate preliminary judgment results for the corresponding individual performance. Specifically, this includes: based on the compliance or non-compliance results from Step 52, calling the preset individual judgment rule library: if the indicator comparison result is compliant, the corresponding individual is preliminarily judged as qualified, such as a signal acquisition time of 500ms meeting the threshold and being judged as qualified; if the comparison result is non-compliant, it is judged as unqualified, such as assuming a receiver bit error rate of 0.15% > the threshold of 0.1%, then it is judged as unqualified; for grade-type indicators such as tracking accuracy, the judgment is further refined: if the grade is excellent, it is marked as qualified (excellent); if the grade is good, it is marked as qualified (good); if the grade is poor, it is marked as unqualified, ensuring that the judgment result reflects both compliance and performance quality.

[0099] The four individual judgment results are stored in the format of indicator name, judgment result, and comparison basis, such as signal acquisition time, qualified, 500ms≤1000ms, to generate a preliminary judgment table for individual performance.

[0100] Step 54: Based on the preliminary judgment results of all individual performance indicators, and according to the predetermined qualification judgment rules, automatically generate the overall performance judgment result of the GNSS receiver hardware module. Specifically, this includes: retrieving the preset overall qualification judgment rules from the system rule base, classifying them according to application scenarios. For example, the general scenario rule is that if all four indicators are qualified, the overall system is qualified; the outdoor weak signal scenario rule is that the receiving sensitivity and tracking accuracy must be qualified, and at least one of the other two must be qualified.

[0101] Check each of the four results in the preliminary performance evaluation table: if all four are qualified, such as signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity, then the overall qualification is triggered; if at least one is unqualified, such as bit error rate, then the overall failure is triggered and the unqualified item is recorded, such as bit error rate 0.15% > threshold 0.1%; for cases of overall failure, generate an additional analysis of the reasons for failure, such as the core indicator bit error rate not meeting the standard, which may affect the reliability of data transmission and provide direction for subsequent hardware optimization.

[0102] The overall judgment result (pass or fail), details of individual judgments, and reasons for failure are stored in a preset format, such as PDF, and pushed to the testing terminal. This ensures that the results can be directly used in production acceptance, customer delivery, and other scenarios, achieving full automation of the testing, judgment, and application process.

[0103] In this embodiment of the invention, four core indicators—signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity—are accurately acquired to avoid incomplete comparisons due to missing indicators, providing complete data support for subsequent standardized judgments. The acquired indicators are directly derived from the summary results in step 47, requiring no additional data collection or conversion, achieving seamless integration of indicator calculation and judgment comparison, and improving process efficiency. Comparisons are based on pre-stored thresholds in the database, replacing the method of manually setting thresholds based on experience, ensuring consistent comparison standards for different batches and models of receivers. The comparison logic can be automatically executed by software, eliminating the need for manual verification item by item. This shortens comparison time; individual assessment of each indicator directly pinpoints the specific performance shortcomings of the receiver hardware module, avoiding situations where the overall performance is unqualified but the problem cannot be located; preliminary assessment results for each item can intuitively reflect the specific level of each performance category, making it easy for testing personnel and customers to quickly understand the individual performance of the hardware module; comprehensive assessment based on predetermined rules avoids overall misjudgment due to minor non-core indicator deficiencies, ensuring that the results align with actual application scenarios; predetermined rules are pre-embedded in the testing system, eliminating the need for manual intervention and ensuring consistent judgment logic across different testing personnel and at different testing times, thus improving the credibility of the overall judgment results.

[0104] In a preferred embodiment of the present invention, step 6 above includes:

[0105] Step 61: Based on the performance judgment result, call the preset report template, specifically including: First, read the overall performance judgment result generated in step 54, i.e., qualified or unqualified, and match the corresponding template from the system's preset report template library: If the judgment result is qualified, retrieve the qualified version template, the core module includes a test parameter summary table, a key performance indicator compliance table, and an overall qualified conclusion; if the judgment result is unqualified, retrieve the unqualified version template, and on the basis of the qualified version module, add an unqualified indicator details table, a reason analysis and suggestion column.

[0106] Perform field integrity verification on the retrieved template: check whether it contains test parameter fields, namely signal center frequency, modulation type, power level, key performance index fields, namely acquisition time, tracking accuracy, bit error rate, sensitivity, and judgment result fields, namely single judgment, overall judgment, and other core modules. If there are missing fields, such as the power level field not being included, the template repair mechanism is automatically triggered to call the spare fields from the template library to complete it.

[0107] Step 62: Fill the test parameters, key performance indicators, and performance judgment results into the corresponding fields of the report template according to the preset format to obtain the filled report. Specifically, this includes: clarifying the data source of each field: the test parameter field data comes from the test parameter configuration record table in step 13, such as signal center frequency 1561.098MHz, modulation type BPSK, signal power level -110dBm; the key performance indicator field data comes from the indicator summary table in step 47, such as acquisition time 500ms, tracking accuracy excellent, bit error rate 0.05%, sensitivity -142dBm; the judgment result field data comes from the performance judgment report in step 54, such as all individual judgments are qualified, and the overall judgment is qualified, ensuring that the data source is traceable and avoiding data mismatch.

[0108] According to the template's preset format, such as test parameters presented in tabular form, and indicators presented in a list format including indicator name, value, unit, threshold, and whether it meets the standard, fill in the data: For numerical data, such as capture time, retain the same precision as the original record, keep 500ms as an integer, and 0.05% as two decimal places; For graded data, such as tracking accuracy being excellent, fill in according to the template's preset grade labeling rules, with excellent marked in green and good marked in blue; For judgment results, add comparison criteria after the corresponding field, such as capture time being qualified: 500ms≤1000ms.

[0109] After the data is filled, the system automatically compares the filled data with the original data source for consistency. For example, it compares whether the filled sensitivity of -142dBm is consistent with the -142dBm recorded in step 46. If there is a deviation, such as -140dBm being mistakenly filled, an error message is triggered and the deviation field is located. The data needs to be filled again until it is consistent, ensuring that the reported data is accurate.

[0110] Step 63: Add a timestamp and test sequence identifier to the filled report to generate a structured test report. Specifically, this includes: obtaining the current time from the real-time clock module of the test system, accurate to the second, in the format YYYY-MM-DDHH:MM:SS, such as 2024-05-20 14:30:45; appending the timestamp to the test completion time field on the report cover; and recording the basis for the timestamp generation in the report generation log at the end of the report, i.e., originating from the system clock and synchronized with the test terminal time, to ensure that the time record is traceable.

[0111] The test sequence identifier is generated according to the preset rules: the identifier format is TEST-Date-Daily Detection Sequence Number, where the date is the date of completion of the test, such as 20240520, and the daily detection sequence number is the sequence number of the Nth test on the day, starting from 001 and incrementing, such as the 5th test on the day, which is 005. The final identifier is TEST-20240520-005.

[0112] After generation, the system queries the detection sequence database to verify whether the identifier already exists. If it does not exist, it is directly appended to the test number field on the report cover. If it exists, the sequence number is incremented, such as from 005 to 006, and re-verified until a unique identifier is generated, ensuring that each report corresponds to a unique detection event.

[0113] The populated report, timestamp, and unique test sequence identifier are integrated and formatted in a pre-defined structured format, such as PDF, including a searchable text layer, fixed page numbers, and a table of contents, to generate the final structured test report for the GNSS receiver hardware module. The report also automatically generates a summary containing core information such as the test number, the model of the tested object, the overall judgment result, and the average value of key indicators, facilitating quick preview.

[0114] Step 64: Output the structured test report to the user-specified terminal or preset storage location to complete the automated test process. Specifically, this includes: reading the user's output preferences set in the test system: if the user prefers to output to a specified terminal, obtain the terminal's IP address or device identifier, such as the tester's computer IP: 192.168.1.100, or the mobile terminal's Bluetooth identifier, and establish a communication connection between the test system and the terminal. For wired connections, port connectivity needs to be verified; for wireless connections, signal strength needs to be verified to be ≥-80dBm. If the user prefers to output to a preset storage location, locate the storage location and verify whether the storage space is sufficient. Space at least twice the report size needs to be reserved; for example, if the report is 10MB, at least 20MB needs to be reserved.

[0115] Terminal output: The structured report is sent to a designated terminal via file transfer protocols such as FTP and Bluetooth. After the transfer is completed, the receiving terminal returns a file reception confirmation signal, which includes the file size and checksum. The sent file size is compared with the received file size. If the deviation is ≤1KB, the report is considered complete. Storage location output: The report is copied to a preset location. After copying, the MD5 checksum of the file is calculated and compared with the original checksum when the report was generated. If they match, the storage is considered complete.

[0116] After the output is completed, the system automatically records the output log, including the output time, target terminal or storage location, report number, and whether the output result is successful or failed. It also sends a report output completion prompt to the testing personnel, such as a system pop-up or SMS notification. If the output fails, such as when the terminal is disconnected or the storage location is unavailable, a retry mechanism is triggered, with a maximum of 3 retries. If a retry fails, the reason for the failure is recorded and an alarm is triggered to ensure a complete closed loop of the automated testing process.

[0117] In this embodiment of the invention, the pre-set template ensures a consistent report format for different test batches and receiver models, avoiding difficulties in information reading due to format confusion; direct template access eliminates the need for temporary format design, reducing repetitive work; targeted field filling avoids omission of key data during manual entry, ensuring the integrity of the report information; the pre-set format clearly defines the field positions corresponding to the data, avoiding incorrect or mixed data entry, ensuring the logical clarity of the report content; the timestamp records the time of test completion, and the test sequence identifier uniquely corresponds to a single test, facilitating subsequent tracing of the test time and batch information of a specific receiver; the date and sequence number in the identifier can be used as a classification basis for report archiving, making it convenient for testing personnel to quickly retrieve target reports and improving report management efficiency; report output is the final step in automated testing, pushing the results to the user terminal or storing them in a preset location, completing the entire process of signal generation, index calculation, judgment, and reporting; designated terminal output allows testing personnel to view results immediately, while preset storage locations facilitate long-term archiving.

[0118] like Figure 2 As shown, embodiments of the present invention also provide an automated detection system based on a GNSS receiver hardware module, comprising:

[0119] The initialization module is used to establish a connection with the GNSS receiver hardware module through a preset communication protocol and configure a set of test parameters to complete the initialization of the detection environment.

[0120] The signal generation module is used to generate an analog GNSS signal containing multiple frequency points and multiple modulation methods based on the established connection and configured test parameters, and input the signal to the GNSS receiver hardware module;

[0121] The preprocessing module is used to respond to simulated GNSS signals by acquiring the response signals output by the GNSS receiver hardware module to obtain the raw response dataset containing intermediate frequency data, navigation messages, and state parameters; and to perform signal enhancement and noise suppression processing on the raw response dataset using a shearing transform algorithm to generate a preprocessed signal dataset.

[0122] The index calculation module is used to parse the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity.

[0123] The result determination module is used to compare key performance indicators with preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate performance determination results.

[0124] The report output module is used to automatically generate a structured test report based on the performance assessment results and output the report to a specified terminal or storage location.

[0125] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0126] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automated detection method based on GNSS receiver hardware modules, characterized in that, The method includes: Step 1: Establish a connection with the GNSS receiver hardware module through a preset communication protocol, and configure a set of test parameters to complete the initialization of the detection environment; Step 2: Based on the established connection and configured test parameters, generate an analog GNSS signal containing multiple frequency points and multiple modulation methods, and input the signal into the GNSS receiver hardware module; Step 3: In response to the simulated GNSS signal, the response signal output by the GNSS receiver hardware module is acquired to obtain the original response dataset containing intermediate frequency data, navigation message and status parameters; and the original response dataset is processed by a shearing transform algorithm to enhance the signal and suppress noise, generating a preprocessed signal dataset. Step 4: Analyze the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate the key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity. Step 5: Compare the key performance indicators with the preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate the performance judgment result. Step 6: Based on the performance assessment results, automatically generate a structured test report and output the report to a specified terminal or storage location.

2. The automated detection method based on GNSS receiver hardware module according to claim 1, characterized in that, Step 1 includes: Establish a physical connection with the GNSS receiver hardware module through a selected wired or wireless communication interface; Based on the established physical connection, an initialization command is sent to the GNSS receiver hardware module, and the device identifier and capability information returned by the module are received. Based on the equipment identification and capability information, configure a set of test parameters that match the GNSS receiver hardware module. The test parameters include the signal center frequency, modulation type, signal power level, and data sampling rate. The test parameters are sent to the GNSS receiver hardware module, and an acknowledgment signal is received to complete the initialization of the test environment.

3. The automated detection method based on GNSS receiver hardware modules according to claim 2, characterized in that, Step 2 includes: Based on the test parameters, the corresponding navigation message bit stream is generated by calling the pre-stored satellite ephemeris data; Based on the signal center frequency and modulation type in the test parameters, the navigation message bit stream is subjected to carrier modulation and spread spectrum processing to form a baseband digital signal. The baseband digital signal is converted into an analog signal by a digital-to-analog converter, and its amplitude is adjusted according to the signal power level in the test parameters to generate the final multi-frequency, multi-modulation analog GNSS signal. The analog GNSS signal is input to the GNSS receiver hardware module through the established physical connection.

4. The automated detection method based on GNSS receiver hardware modules according to claim 3, characterized in that, Step 3 includes: In response to analog GNSS signals, the data acquisition module collects the response signals output by the GNSS receiver hardware module in real time to obtain the raw response dataset containing intermediate frequency data, navigation messages and status parameters. The original response dataset is divided into frames to obtain a series of continuous time data frames. The time-frequency domain decomposition of each time data frame is performed by the shear transform algorithm, the coefficients of each sub-band signal are calculated, and the part of the coefficient below the noise threshold is set to zero by setting a threshold to achieve noise suppression, and the coefficients of each sub-band after threshold processing are obtained. The sub-band coefficients after thresholding are reconstructed by inverse shearing transform to obtain the denoised signal frame. All denoised signal frames are merged in chronological order to generate a preprocessed signal dataset.

5. The automated detection method based on GNSS receiver hardware modules according to claim 4, characterized in that, Step 4 includes: Time-domain and frequency-domain analyses were performed on the preprocessed signal dataset to extract initial feature sequences containing signal amplitude, phase, and signal-to-noise ratio. The initial feature sequence is mapped to a two-dimensional feature space to obtain the corresponding feature point set; the minimum convex hull boundary of the feature point set is calculated based on the convex hull algorithm, and the transition boundary and stable feature region of the signal are determined according to the calculated minimum convex hull boundary.

6. The automated detection method based on GNSS receiver hardware modules according to claim 5, characterized in that, Step 4 also includes: Based on the stable feature region, the signal acquisition time is calculated, which is the time interval from the start of signal input to the first time the signal amplitude feature enters and remains within the stable feature region. Based on the set of signal amplitude feature points collected after entering the stable feature region, the tracking accuracy is calculated according to the density of the distribution of the feature point set within the convex hull boundary. The denser the distribution of feature points, the higher the tracking accuracy. The navigation messages in the original response dataset are parsed to obtain the received code stream. The received code stream is compared with the expected navigation message bit stream, and a decision threshold is defined based on the convex hull boundary. The bit error rate is calculated based on the decision threshold. Based on the signal acquisition time and tracking accuracy results, the signal power level of the analog GNSS signal is gradually reduced and the acquisition and tracking process is repeated, with the lowest signal power that the receiver can normally demodulate the navigation message as the receiving sensitivity. The key performance indicators of signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity are summarized.

7. The automated detection method based on GNSS receiver hardware modules according to claim 6, characterized in that, Step 5 includes: Obtain the key performance indicators of the signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity; Each key performance indicator is compared one by one with the corresponding performance thresholds pre-stored in the database to obtain the comparison results of each indicator. Based on the comparison results of various indicators, a preliminary judgment result for the corresponding individual performance is generated; Based on the preliminary results of all individual performance indicators and in accordance with the predetermined pass / fail criteria, the overall performance assessment result of the GNSS receiver hardware module is automatically generated.

8. The automated detection method based on GNSS receiver hardware modules according to claim 7, characterized in that, Step 6 includes: Based on the performance assessment results, a preset report template is invoked; The test parameters, key performance indicators, and performance judgment results are filled into the corresponding fields of the report template according to the preset format to obtain the filled report; Add timestamps and test sequence identifiers to the populated report to generate a structured test report; The structured test report is output to the user-specified terminal or a preset storage location to complete the automated test process.

9. An automated detection system based on a GNSS receiver hardware module, the system implementing the method as described in any one of claims 1 to 8, characterized in that, include: The initialization module is used to establish a connection with the GNSS receiver hardware module through a preset communication protocol and configure a set of test parameters to complete the initialization of the detection environment. The signal generation module is used to generate an analog GNSS signal containing multiple frequency points and multiple modulation methods based on the established connection and configured test parameters, and input the signal to the GNSS receiver hardware module; The preprocessing module is used to respond to analog GNSS signals by acquiring the response signals output by the GNSS receiver hardware module and obtaining a raw response dataset containing intermediate frequency data, navigation messages and status parameters. The original response dataset is then processed using a shear transform algorithm to enhance the signal and suppress noise, resulting in a preprocessed signal dataset. The indicator calculation module is used to parse the preprocessed signal dataset and extract the signal feature boundaries based on the convex hull algorithm, and calculate key performance indicators such as signal acquisition time, tracking accuracy, bit error rate and receiver sensitivity. The result determination module is used to compare key performance indicators with preset performance thresholds, automatically determine whether the GNSS receiver hardware module is qualified based on the comparison results, and generate performance determination results. The report output module is used to automatically generate a structured test report based on the performance assessment results and output the report to a specified terminal or storage location.

10. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 8.

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