Low-latitude high-frequency radar ionosphere monitoring data product generation method and system

Abstract

The invention provides a low-latitude high-frequency radar ionosphere monitoring data product generation method and system, and the method comprises the steps: receiving an echo signal of a low-latitude high-frequency radar ionosphere, carrying out the orthogonal demodulation and digital sampling of the echo signal, and generating original in-phase orthogonal data; performing beam forming on the original in-phase orthogonal data according to receiving channels of a main array and an auxiliary array to obtain two paths of beam in-phase orthogonal records; forming time delay combination data according to the in-phase orthogonal records of the two beams, and performing phase fitting on the time delay combination data to generate a fitting autocorrelation function; according to the fitting autocorrelation function, drawing a scattering echo fast view of the low-latitude high-frequency radar ionosphere; according to the method, a complete product system is constructed by unifying space-time and file specifications, the data quality and cross-platform sharing efficiency can be improved, and high-quality support is provided for space environment research.

Description

Technical Field

[0001] This invention relates to the field of ground-based monitoring technology for the space environment, specifically to a method and system for generating low-latitude high-frequency radar ionospheric monitoring data products. Background Technology

[0002] Currently, the ionosphere, as an important component of the Earth's atmosphere, has a significant impact on human activities such as radio communication, navigation and positioning, and satellite tracking and control due to its dynamic changes (such as the formation, drift, and dissipation of inhomogeneities). In low-latitude regions, the ionosphere is affected by multiple factors, including the geomagnetic field configuration, solar radiation, and atmospheric fluctuations, making its changes even more complex. Therefore, high-precision, long-term monitoring of the ionosphere is crucial.

[0003] Current monitoring of the low-latitude ionosphere mainly relies on equipment such as high-frequency radar and VHF radar, but existing technologies have the following shortcomings: 1. Incomplete data product system: Most monitoring equipment only outputs raw observation data (such as IQ signals) or single intermediate data (such as power spectrum), lacking a hierarchical system from raw data (L0) to inversion parameters (L2) and then to visualization products (L2Q). This fails to meet the differentiated needs of different users (such as equipment maintenance personnel, researchers, and engineering application personnel), and the product definition is unclear, making it difficult to serve as a unified basis for cross-system collaboration. 2. Non-standard data processing procedures: In existing processing methods, there is a lack of standardized solutions for the combination logic of non-equally spaced pulses and the identification of "bad delay" (range gate cross interference, transmission masking interference) in the calculation of autocorrelation function, resulting in large differences in the results of the same observation data after being processed by different methods; at the same time, the fusion method of east-west time-division sounding data is not unified, and it is impossible to realize the full-domain visualization of ionospheric dynamics. 3. Weak data quality control: Existing technologies mostly rely on single hardware calibration (such as channel amplitude and phase correction), lacking a full-link quality control scheme that includes hardware inspection, data verification, and multi-device comparison, and there are no clear quality control indicators (such as the proportion of qualified data), making it difficult to assess data reliability. Some abnormal data (such as invalid echoes caused by antenna failure) are mixed with valid data, affecting subsequent applications. 4. Lack of spatiotemporal and document standardization: The time systems (such as local time and UTC time) and coordinate systems (such as radar station center coordinates and geographic coordinates) used by different monitoring devices are not consistent, and the file naming rules are chaotic, resulting in low efficiency of multi-site data splicing and cross-platform sharing, making it difficult to form a large-scale ionospheric monitoring dataset. In summary, existing technologies lack a standardized data product system covering all levels, standardized data processing procedures, end-to-end quality control schemes, and unified spatiotemporal and file management standards. This results in technical problems such as insufficient reliability of low-latitude ionospheric monitoring data, difficulty in meeting differentiated application needs, and low efficiency in cross-system sharing. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method for generating low-latitude, high-frequency radar ionospheric monitoring data products, comprising: The echo signal from the ionosphere of a low-latitude high-frequency radar is received, and the echo signal is orthogonally demodulated and digitally sampled to generate original in-phase orthogonal data. The original in-phase orthogonal data is beamformed according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records; Based on the in-phase orthogonal records of the two beams, time delay combination data is formed, and phase fitting is performed on the time delay combination data to generate a fitting autocorrelation function; Based on the fitted autocorrelation function, a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere is plotted.

[0005] Optionally, the step of forming time-delay combination data based on the two beam in-phase orthogonal records includes: Extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records respectively; The two sampled sequences are grouped according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampled sequence.

[0006] Optionally, the sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval.

[0007] Optionally, the step of performing phase fitting on the time-delay combination data to generate a fitted autocorrelation function includes: Based on a preset fitting algorithm, the effective time delay in the time delay combination data is phase extracted to obtain the phase information corresponding to each effective time delay; Phase cycle slips are identified based on the change characteristics of the phase information, and the phase cycle slips are eliminated by phase difference correction of adjacent effective time delays to obtain a continuous phase sequence; Based on the continuous phase sequence, a fitted autocorrelation function is generated.

[0008] Optionally, generating a fitted autocorrelation function based on the continuous phase sequence includes: The continuous phase sequence is fitted using the least squares linear fitting method to obtain the phase change characteristics; Based on the phase change characteristics, the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width are retrieved. The echo signal-to-noise ratio, Doppler velocity, Doppler spectral width, and effective time delay are correlated and integrated to generate a fitted autocorrelation function.

[0009] Optionally, the step of plotting a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function includes: Extract the fitted autocorrelation function for eastward detection and the fitted autocorrelation function for westward detection from the fitted autocorrelation function; The autocorrelation function fitted by the westward detection is reversed in terms of distance value and then arranged in chronological order with the autocorrelation function fitted by the eastward detection to obtain the time-sorted autocorrelation function. Based on the time-ordered fitted autocorrelation function, a fast view of the scattered echoes from the low-latitude high-frequency radar ionosphere is plotted.

[0010] Optionally, the step of plotting a fast view of the scattered echoes of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function sorted over time includes: Extract the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate from the time-sorted fitted autocorrelation function; Based on the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate, a sub-graph structure of the scattered echo fast view is constructed according to a preset layout rule; Color level mapping is performed on the sub-graph structure to obtain the image pixel color information; Based on the image pixel color information, a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere is drawn according to a preset pixel size.

[0011] Based on the same inventive concept, this invention also provides a low-latitude high-frequency radar ionospheric monitoring data product generation system, comprising: The signal modulation module is used to receive echo signals from the ionosphere of a low-latitude high-frequency radar, perform orthogonal demodulation and digital sampling on the echo signals, and generate original in-phase orthogonal data. The beamforming module is used to perform beamforming on the original in-phase orthogonal data according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records. The phase fitting module is used to form time delay combination data based on the in-phase orthogonal records of the two beams, and to perform phase fitting on the time delay combination data to generate a fitting autocorrelation function; The product drawing module is used to draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function.

[0012] Optionally, the phase fitting module includes: The sequence extraction submodule is used to extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records; The sequence grouping submodule is used to group the two obtained sampling sequences according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampling sequence.

[0013] Optionally, the sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval.

[0014] Optionally, the phase fitting module includes: The phase extraction submodule is used to extract the phase of the effective time delay in the time delay combination data based on a preset fitting algorithm, so as to obtain the phase information corresponding to each effective time delay. The information elimination submodule is used to identify phase cycle slips based on the change characteristics of the phase information, and eliminate the phase cycle slips by correcting the phase difference between adjacent effective delays to obtain a continuous phase sequence. The sequence fitting submodule is used to generate a fitted autocorrelation function based on the continuous phase sequence.

[0015] Optionally, the sequence fitting submodule includes: A phase fitting unit is used to fit the continuous phase sequence using the least squares linear fitting method to obtain phase change characteristics; The parameter inversion unit is used to invert the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width based on the phase change characteristics. The time delay integration unit is used to integrate the echo signal-to-noise ratio, Doppler velocity, Doppler spectral width and the effective time delay to generate a fitted autocorrelation function.

[0016] Optionally, the product drawing module includes: The parameter extraction submodule is used to extract the fitted autocorrelation function for eastward detection and the fitted autocorrelation function for westward detection from the fitted autocorrelation function; The parameter sorting submodule is used to reverse the sign of the distance value of the fitted autocorrelation function of the westward detection and arrange it in time order with the fitted autocorrelation function of the eastward detection to obtain the fitted autocorrelation function sorted by time. The product generation submodule is used to draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function sorted over time.

[0017] Optionally, the product generation submodule includes: The group extraction unit is used to extract the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate from the time-sorted fitted autocorrelation function. The structure generation unit is used to construct a sub-graph structure of the scattered echo fast view according to a preset layout rule based on the echo signal-to-noise ratio, Doppler velocity and Doppler spectral width parameters corresponding to each time node and each distance gate. A color level mapping unit is used to perform color level mapping on the sub-image structure to obtain image pixel color information; The quick view generation unit is used to draw a quick view of the scattered echo of the low-latitude high-frequency radar ionosphere according to the image pixel color information and a preset pixel size.

[0018] In another aspect, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for generating low-latitude high-frequency radar ionospheric monitoring data products as described above is implemented.

[0019] In another aspect, the present invention also provides a computer device readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the method for generating low-latitude high-frequency radar ionospheric monitoring data products as described above.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method and system for generating low-latitude high-frequency radar ionospheric monitoring data products, comprising: receiving echo signals from the low-latitude high-frequency radar ionosphere; performing orthogonal demodulation and digital sampling on the echo signals to generate raw in-phase orthogonal data; performing beamforming on the raw in-phase orthogonal data according to the receiving channels of the main array and the auxiliary array to obtain two-beam in-phase orthogonal records; forming time-delay combination data based on the two-beam in-phase orthogonal records, and performing phase fitting on the time-delay combination data to generate a fitted autocorrelation function; and drawing a fast view of the scattered echoes of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function. Parallel beamforming using dual arrays enhances signal directivity and detection accuracy, effectively increasing the signal strength of long-distance ionospheric echoes and reducing environmental noise interference. Combined with the ordered combination of non-equidistant pulses and a phase cycle slip elimination mechanism, it addresses the problems of inaccurate parameter inversion and ineffective handling of bad time delay interference in existing technologies, enabling standardized output of core data products. By drawing a fast view of the scattered echo based on the fitted autocorrelation function, it provides a visual representation of the dynamic changes across the entire ionosphere, solving the problems of fragmented and difficult-to-quickly analyze traditional time-division detection data. This provides an intuitive and efficient terminal product for engineering applications and scientific research, improving the convenience of data application. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a method for generating low-latitude high-frequency radar ionospheric monitoring data products provided by the present invention; Figure 2 A schematic diagram of the overall framework of a method for generating low-latitude high-frequency radar ionospheric monitoring data products provided by the present invention; Figure 3 A cross-interference comparison diagram of a method for generating low-latitude high-frequency radar ionospheric monitoring data products, provided for a specific embodiment of the present invention; Figure 4 A schematic diagram illustrating the overlap between the sampling time and the transmission pulse window during transmission masking interference in a method for generating low-latitude high-frequency radar ionospheric monitoring data products, provided for a specific embodiment of the present invention; Figure 5 This is a schematic diagram of the structural composition of a low-latitude high-frequency radar ionospheric monitoring data product generation system provided by the present invention.

[0022] Figure 6 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0023] This invention proposes a method, system, device, and medium for generating low-latitude high-frequency radar ionospheric monitoring data products. The specific embodiments of this invention will be further described in detail below with reference to the accompanying drawings.

[0024] Example 1: This invention provides a method for generating low-latitude high-frequency radar ionospheric monitoring data products, the flowchart of which is shown below. Figure 1 As shown, it includes: Step 1: Receive the echo signal from the ionosphere of a low-latitude high-frequency radar, perform orthogonal demodulation and digital sampling on the echo signal, and generate original in-phase orthogonal data; Step 2: Perform beamforming on the original in-phase orthogonal data according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records; Step 3: Based on the in-phase orthogonal records of the two beams, form time delay combination data, and perform phase fitting on the time delay combination data to generate a fitted autocorrelation function; Step 4: Based on the fitted autocorrelation function, draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere.

[0025] In one implementation, step 3 above, which involves forming time-delay combined data based on the two beam in-phase orthogonal records, may include: Extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records respectively; The two sampling sequences are grouped according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampling sequence.

[0026] For example, the sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval; In this implementation, the sampling sequence corresponding to the same set of non-equally spaced pulses is selectively extracted from the in-phase orthogonal records of the two beams, which can completely preserve the core information of the amplitude and phase of the echo signal at each detection distance. The preset unambiguous combination sequence designed based on the transmission timing of the non-equally spaced pulses and the detection resolution can effectively eliminate the superposition interference of different pulse echoes, avoid the pollution of effective data by clutter signals, and ensure data purity. The two grouped sampling sequences are arranged sequentially according to the preset time delay interval to form time delay combination data, which can realize the standardized organization of data, improve the coherence and efficiency of data processing, and at the same time ensure the orderly correlation of signals corresponding to different distance gates and different pulses.

[0027] In one implementation, the step of performing phase fitting on the time-delay combination data to generate a fitted autocorrelation function includes: Based on a preset fitting algorithm, the effective time delay in the time delay combination data is phase extracted to obtain the phase information corresponding to each effective time delay; Phase cycle slips are identified based on the change characteristics of the phase information, and the phase cycle slips are eliminated by phase difference correction of adjacent effective time delays to obtain a continuous phase sequence; Based on the continuous phase sequence, a fitted autocorrelation function is generated.

[0028] In this implementation, the process of generating a fitted autocorrelation function based on the continuous phase sequence may include: The continuous phase sequence is fitted using the least squares linear fitting method to obtain the phase change characteristics; Based on the phase change characteristics, the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width are retrieved. The echo signal-to-noise ratio, Doppler velocity, Doppler spectral width and effective time delay are correlated and integrated to generate a fitted autocorrelation function; In this implementation, phase cycle slips are identified and corrected by phase change characteristics, ensuring the continuity and integrity of the phase sequence. A least-squares linear fitting method is used to obtain reliable phase change characteristics. Based on these characteristics, the three core parameters of echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width are accurately retrieved. The retrieved parameters are then integrated with the effective time delay depth to generate a standardized fitting autocorrelation function. This effectively solves the problems of ineffective phase interference handling and insufficient accuracy in key parameter retrieval in existing technologies, thereby improving the reliability and practicality of the data products and providing high-quality, standardized core data support for scientific research and engineering applications in low-latitude ionospheric monitoring.

[0029] In one implementation, step 4 above, which involves drawing a fast view of the scattered echoes of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function, may include: Extract the fitted autocorrelation function for eastward detection and the fitted autocorrelation function for westward detection from the fitted autocorrelation function; The autocorrelation function fitted by the westward detection is reversed in terms of distance value and then arranged in chronological order with the autocorrelation function fitted by the eastward detection to obtain the time-sorted autocorrelation function. Based on the time-ordered fitted autocorrelation function, a fast view of the scattered echoes from the low-latitude high-frequency radar ionosphere is plotted.

[0030] In this implementation, the process of drawing a fast view of the scattered echoes of the low-latitude high-frequency radar ionosphere based on the time-ordered fitted autocorrelation function may include: Extract the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate from the time-sorted fitted autocorrelation function; Based on the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate, a sub-graph structure of the scattered echo fast view is constructed according to a preset layout rule; Color level mapping is performed on the sub-graph structure to obtain the image pixel color information; Based on the image pixel color information, a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere is drawn according to a preset pixel size. In the above implementation method, eastward and westward detection data are accurately extracted from the fitted autocorrelation function. The spatial reference of the eastward and westward data is unified by reversing the sign of the westward distance value. Then, the data is arranged in chronological order to form a complete time-series dataset, which can effectively solve the problem of fragmentation of traditional time-division detection data. Furthermore, the core parameters of echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width corresponding to each time node and range gate are extracted. A special sub-graph structure is constructed according to the preset layout rules. Combined with color level mapping, the abstract parameter values ​​are transformed into intuitive pixel color information. Finally, a standardized scattering echo quick view is drawn according to the preset pixel size. This not only realizes the full-domain visualization of the dynamic changes of the ionosphere, but also improves the readability and application convenience of the data through parameter classification display and standardized format output, providing intuitive and efficient visualization data support for scientific research analysis and engineering judgment.

[0031] In summary, this invention addresses the technical problems of incomplete data product systems, non-standardized processing procedures, weak quality control, and lack of spatiotemporal and document standardization in existing low-latitude ionospheric monitoring technologies. It proposes a method for generating low-latitude high-frequency radar ionospheric monitoring data products, the overall framework of which is illustrated in the diagram below. Figure 2 As shown, echo signals are acquired by transmitting non-equally spaced pulse trains to generate raw IQ data. Two-beam IQ records are obtained through beamforming of the main and auxiliary arrays. After grouping into time-delay combination data according to unambiguous combination sequences, autocorrelation calculation is performed. The raw autocorrelation function is obtained by combining signal accumulation. Bad time-delay data corresponding to range gate crosstalk and transmission shielding interference are marked and skipped. Least-squares linear fitting of phase changes and elimination of cycle slips are used to invert core parameters such as echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width to generate a fitted autocorrelation function. The westward detection range values ​​are reversed and fused with eastward data in chronological order to create a fast view of ionospheric scattering echoes containing three sub-maps. Simultaneously, end-to-end quality control is performed, including hardware checks, data verification, and multi-device comparisons. Finally, a standardized data product system covering L0-L2Q levels is constructed, achieving precise and standardized data processing, thus ensuring data reliability and unifying spatiotemporal and file management standards.

[0032] Example 2: The present invention provides a method for generating ionospheric monitoring data products using a low-latitude high-frequency radar, illustrated by a specific embodiment. The parameter settings for the low-latitude high-frequency radar are as follows: It consists of an east- or west-facing radar antenna array (e.g., a main array with 20 receiving channels and an auxiliary array with 4 receiving channels), a transceiver system (including frequency synthesis, beamforming, and RF power amplifier modules, with a peak transmit power of 12kW), and a control computer (including timing control and digital receiving modules), and is deployed at station xxx. It supports three observation modes to meet different monitoring needs: (1) Beam scanning mode: scan in increments from wave position 1 to the maximum wave position, observation area 180-2000km, time resolution 2 minutes, distance resolution 45km, scanning range ±20°; (2) FastScanMode: Same beam scanning logic, but time resolution is improved to 1 minute; (3) Fixed beam mode: specifies a single beam position for continuous detection with a time resolution of 30 seconds, used for dynamic tracking of ionospheric inhomogeneities.

[0033] The spatiotemporal reference information is set according to the following parameters: Time Standard: All data product file names and contents are in Coordinated Universal Time (UTC) to ensure time alignment across multiple sites; Spatial reference: The geographic coordinate system is preferred. The distance is based on the radar station. The eastward / westward distance is calculated by multiplying the number of distance gates by 45km. The westward data is reversed during fusion. The Doppler velocity is defined as positive when closer to the radar and negative when farther away. The azimuth is 0° with true north as the starting point and increases clockwise.

[0034] The product creation process may specifically include the following steps: 1. Data Collection The radar transmits non-equally spaced pulse trains (encoded in single-pulse, Barker code, and multi-pulse modes). After receiving the echo signals, it performs quadrature demodulation and digital sampling to obtain the raw IQ data for each channel and each range gate (I is the in-phase signal, Q is the quadrature signal, with a phase difference of 90°). Multiple sets of IQ data (each set contains N pulses) are collected per unit time. For example, the radar transmits non-equally spaced pulse trains (pulse sequence: 0, 22, 26, 27, 42, 43, 50 μs) according to multi-pulse encoding. After receiving the echo signals, it performs quadrature demodulation and 15 MHz sampling to obtain 16 channels of raw IQ data (I / Q are both 32-bit integers, with a value range of ±4.2950e+09). One set of IQ data is generated every 116 ms, and a total of 620 sets of data are generated in 2 hours.

[0035] 2. Beamforming The raw IQ data is beamformed separately for the main array (20 channels) and the auxiliary array (4 channels) to obtain two beam IQ records (main array IQ and auxiliary array IQ) to ensure signal directivity and detection accuracy. For example, the software reads 16 channels of raw IQ data and performs beamforming according to the weighting coefficients of the main array (20 channels, 10 effective channels are actually used) and the auxiliary array (4 channels) (main array weight [0.1, 0.15, ..., 0.05], auxiliary array weight [0.25, 0.25, 0.25, 0.25]) to obtain two beam IQ records (main array IQ and auxiliary array IQ). The data format is a custom binary, and each data point occupies 8 bytes (4 bytes each for I and Q).

[0036] 3. Pulse Combination For the IQ data of the same group of non-equally spaced pulses, the sampling sequence after each transmitted pulse is extracted, and the data is grouped according to the unambiguous combination sequence to form time delay combination data of 1τ, 2τ, 3τ... (τ is the minimum time delay interval), so as to solve the signal superposition problem of non-equally spaced pulses.

[0037] 4. Autocorrelation calculation Autocorrelation is performed on the time delay combination data for each distance gate, using the following formula: ; in, for Sample voltage values ​​at all times. for The conjugate of the sampled voltage value at any given time. For delay The autocorrelation function at the location; For each group of beam IQ data, the sampling sequence after 7 transmitted pulses (each sequence contains 45 range gate data) is extracted, and grouped into unambiguous combination sequences (1τ=100μs, 2τ=200μs, ..., 30τ=3000μs) to obtain 30 time delay combination data; autocorrelation is calculated on the 30 time delay data of each range gate to obtain the single pulse train autocorrelation function.

[0038] 5. Signal accumulation For a single pulse train with M non-equally spaced pulses, the autocorrelation function is averaged to obtain the original autocorrelation function (L1 level product), as shown in the formula: ; This step can improve the signal-to-noise ratio and suppress random noise.

[0039] 6. Bad latency identification and handling Identify and label two types of bad delays: Range gate crosstalk: The echoes from different transmitted pulses superimposed at the same range gate, such as... Figure 3As shown, Current7Pulse Sequence, 2.4ms represents a 7-pulse sequence (a detection sequence consisting of 7 non-equally spaced pulses) of low-latitude high-frequency radar, and SuperDARN Pulse Sequence represents a SuperDARN pulse sequence. The horizontal axis represents time (in ms), and the vertical axis represents distance (in km). Transmission masking interference: The sampling time overlaps with the transmission pulse window, such as... Figure 4 As shown, the radar observation parameters marked with Samples forkatscan mpinc=8 logfr=1 nrang=80 nbink=1, with [ij], Rad -> blonkedsomples clearly marking the bad delay intervals corresponding to transmission masking interference such as

[00] , [112,112], [336,336], and [344,344]. The horizontal axis is time (samples) with a range of 0-400, which intuitively presents the distribution of bad delay in the time series. It also involves range gate cross-interference, showing the phenomenon of echo superposition of different transmission pulses at the same range gate. Together, they provide a visual basis for bad delay identification and skipping. During processing, bad latency data is skipped, and subsequent calculations are performed only on valid latency data; For example, if a single pulse train is emitted in 2 hours (such as pulse train 4), the autocorrelation function of the 620 single pulse trains is averaged to obtain the original autocorrelation function (RACF). To identify bad delays, range gate crosstalk is used. For example, if range gates 10-15 (corresponding to 540-810km) have crosstalk at a delay of 15τ-20τ, they are marked as bad delays. Transmit masking interference is also used. If there is a transmit pulse window at a delay of 0τ-2τ, it is marked as a bad delay. Bad delay data is skipped, and 25 valid delay data are retained for subsequent calculations. 7. Phase Fitting and Parameter Inversion For the original autocorrelation function to eliminate bad time delay, the phase change is fitted by least squares linear fitting, and cycle slips are eliminated in the process to obtain the true phase; the phase change rate obtained by fitting is the Doppler velocity, and the phase dispersion is the Doppler spectral width; at the same time, the logarithm of the zero-delay power and the background noise (average of 10 minimum autocorrelation values) are calculated to obtain the echo signal-to-noise ratio, and finally the fitted autocorrelation function (L2 level product) is generated. For the original autocorrelation function of the effective time delay, the phase change was fitted by least squares linear fitting. During the process, a phase cycle slip (180° jump) was found at distance 20 (900km). The cycle slip was eliminated by correcting the phase difference between adjacent time delays. The fitting yielded: Doppler speed: Eastward distance from gates 10-30 (540-1350km) is +50 to +150m / s (closer to radar); Westward distance from gates 10-30 is -40 to -120m / s (farther from radar). Doppler spectral width: 20-80 m / s in both east and west directions; Echo signal-to-noise ratio: The highest signal-to-noise ratio (30-60dB) is found at east-facing range gates 15-25 (810-1125km), while the signal-to-noise ratio at the edge range gates is <10dB. A fitted autocorrelation function (FACF) is generated, and the data format is SuperDARN FITACF. Each range gate stores three parameters (4-byte float type) for signal-to-noise ratio, Doppler velocity, and spectral width.

[0040] 8. East-West Data Fusion and Quick View Drawing Read in the eastward / westward fitted autocorrelation function data, reverse the sign of the westward distances and arrange them in chronological order; draw a quick view (L2Q level product) with a size of 1200×900 pixels, divided into 3 sub-plots (signal-to-noise ratio, Doppler velocity, spectral width), with the horizontal axis representing time (24 hours) and the vertical axis representing distance gates (numbers 4-45, corresponding to 180-2000km), and use color levels to distinguish the parameter sizes.

[0041] Four types of standardized data products are constructed, covering levels L0-L2Q, and their specific definitions are shown in Table 1 below: Table 1 Data Product Definition Table Product Grade Product Name English name Subclass coding Core Features Document Specifications L0 Beam IQ Recording Beam IQ Records BIQR Time resolution 116ms, distance resolution 45km, distance gates 45, value range ±4.2950e+09 Custom binary format, naming convention: OBSID_LHFRnn_BIQR_L0_02H_YYYYMMDDhhmmss_Vnn.nn_D.dat L1 Original autocorrelation function RawAutoCorrelationFunction RACF Time resolution 2min / 1min / 30s, distance gates 45, using SuperDARN RAWACF format. DATAMAP format, naming convention: OBSID_LHFRnn_RACF_L1_02H_YYYYMMDDhhmmss_Vnn.nn_D.RAWACF L2 Fitting autocorrelation function FittedAutoCorrelationFunction FACF Includes signal-to-noise ratio (0-96dB), Doppler velocity (±350m / s), and spectral width (0-200m / s), SuperDARN FITACF format. DATAMAP format, naming rule: OBSID_LHFRnn_FACF_L2_02H_YYYYMMDDhhmmss_Vnn.nn_D.FITACF L3 Ionospheric scattered echo quick view Quick look ofIonosphericScattering Echoes QISE 1200×900 pixels, 3 sub-images, time-segmented encoding DAY JPG format, APP15 segments store auxiliary information, naming rule: OBSID_LHFRnn_IPQP_L2Q_DAY_YYYYMMDDhhmmss_Vnn.nn.JPG

[0042] In Table 1, OBSID is a 5-digit observation station code, LHFRnn is the device number (starting from 01), D is the direction (E / W), and Vnn.nn is the version number.

[0043] When the FACF data for the east (D=E) and west (D=W) directions from 00:00 to 24:00 on June 1, 2024 is read in, the distance values ​​corresponding to the west-direction distance gates are reversed (e.g., distance gate 10 corresponds to 540km → -540km); after arranging the data in chronological order, a quick view is plotted: The image size is 1200×900 pixels, divided into three sub-images: top (signal-to-noise ratio), middle (Doppler velocity), and bottom (spectral width), each sub-image being 1200×280 pixels; Horizontal axis: 00:00-24:00 UTC, marked at 2-hour intervals; Vertical axis: distance from gates 4-45 (corresponding to 180-2000km, positive for east and negative for west); The auxiliary information is stored in the APP15 segment of the JPG file, including the product name "Ionospheric Scattered Echo Quick View", observation mode "ScanMode", detection frequency "15MHz", wave position "4", etc., and the file is named "DF001_LHFR01_IPQP_L2Q_DAY_20240601000000_V1.00.JPG" (DF001 is the 5-digit code of xxx station).

[0044] The quality inspection process is as follows: 1. Hardware quality control The antenna array's shape, structure, and component connections are manually inspected weekly to ensure that the antenna configuration is consistent. The amplitude and phase of the transmit and receive channels are automatically corrected daily to ensure the consistency of amplitude and phase of each channel. Real-time monitoring of peak transmission power (calculated based on the number of channels and power level), triggering an alarm when abnormalities occur; For example, at 8:00 AM on June 1st, a manual inspection of the antenna array revealed that the wiring of receiver unit 3 in the east-facing antenna array was loose. After tightening it again, the channel amplitude consistency was ≤5%, and the phase consistency was ≤3°. Automatic amplitude and phase correction was performed at 00:00 on June 1st: the amplitude deviation of the 10 channels of the main array was 1.2%-3.5%, and the phase deviation was 0.5°-2.1°, which met the requirements; Peak power monitoring: The peak power remained stable at 11.8-12.2kW within 2 hours, with no abnormal alarms.

[0045] 2. Data Validation Raw IQ data verification: Check the consistency of data length, pulse repetition count, encoding type, and number of distance gates; delete the file if it does not meet the requirements. Autocorrelation function verification: Verify the accuracy of delay combinational logic and bad delay marking, and mark abnormal data as "pending review".

[0046] Regarding the beam IQ recording: out of 620 data sets, 3 data sets had abnormal lengths (missing 10 range gates). After deletion, 617 sets of valid data were obtained, with a validity rate of 99.5% (≥95%, meeting the quality control indicators). Original autocorrelation function: Verify delay combinational logic, 25 out of 30 delays are valid, bad delay marking accuracy is 100%.

[0047] 3. Multi-device comparison verification Every month, the low-latitude high-frequency radar data is compared with the ionospheric echo images of the Fuke VHF radar and the Sanya VHF radar to analyze the spatiotemporal differences and verify the detection function and performance; a full system calibration is carried out once a year to ensure data accuracy. For example, from 12:00 to 14:00 on June 1st, the ionospheric echo data from a low-latitude high-frequency radar (15MHz) and a Fuchs VHF radar (50MHz) were compared: In the 810-1125km eastward region, the drift velocity of the inhomogeneous body detected by both methods differed by ≤10m / s, and the signal-to-noise ratio showed a consistent trend, verifying the reliability of the data.

[0048] 4. Quantification of quality control indicators Beam IQ recordings: ≥95% of the data can be used for analysis; Original / fitted autocorrelation function: The percentage of qualified data in the annual quality control is ≥80%; Quick View: Image format, size, and sub-image layout pass rate 100%, auxiliary information completeness 100%.

[0049] For example, the effective recording rate of beam IQ is ≥95% (99.5%). Original / fitted autocorrelation function: 88% ≥ 80% of data were valid on June 1st; Quick View: All 100 images met the requirements for format, size, and auxiliary information, achieving a 100% pass rate. All indicators meet the quality control requirements.

[0050] This specific embodiment illustrates the proposed method for generating low-latitude high-frequency radar ionospheric monitoring data products. Leveraging a hardware configuration of 20 main array and 4 auxiliary array receiving channels, along with three differentiated observation modes (beam scanning, fast beam scanning, and fixed beam), it flexibly adapts to diverse needs for daily monitoring and special event tracking. It achieves a minimum time resolution of 30 seconds and a range resolution of 45km, enabling precise coverage of an observation area of ​​180-2000km. Through orthogonal demodulation, beamforming, dual-mechanism identification of bad delays (range gate crosstalk and transmit shielding interference), and phase cycle slip elimination, it achieves an effective beam IQ recording ratio of 99.5% and a qualified ratio of 88% for the original / fitted autocorrelation function, while also minimizing deviations from the drift velocity of inhomogeneous bodies detected by Fuchs VHF radar. With a speed of ≤10m / s, the accuracy and reliability of data processing are significantly improved. By fusing westward distances with inverse signs and drawing a 1200×900 pixel quick view, the fragmentation problem of traditional time-division detection data can be solved, enabling dynamic full-domain visualization of the ionosphere. At the same time, by unifying the UTC time and geographic coordinate system and adopting standardized file naming rules that include key information such as site, equipment, and product type, combined with 2-hour / 1-day time segmentation coding, the efficiency of data retrieval and cross-platform sharing can be greatly improved. Ultimately, it provides high-quality, standardized data support covering L0-L2Q levels for scientific research and engineering applications such as monitoring low-latitude ionospheric inhomogeneities and spacecraft operation support, fully demonstrating the significant advantages of this invention in terms of product system integrity, data processing accuracy, quality control reliability, and data management efficiency.

[0051] Example 3: Based on the same inventive concept, this invention also provides a low-latitude high-frequency radar ionospheric monitoring data product generation system, the structural composition of which is shown in the schematic diagram below. Figure 5 As shown, it includes: The signal modulation module is used to receive echo signals from the ionosphere of a low-latitude high-frequency radar, perform orthogonal demodulation and digital sampling on the echo signals, and generate original in-phase orthogonal data. The beamforming module is used to perform beamforming on the original in-phase orthogonal data according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records. The phase fitting module is used to form time delay combination data based on the in-phase orthogonal records of the two beams, and to perform phase fitting on the time delay combination data to generate a fitting autocorrelation function; The product drawing module is used to draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function.

[0052] In one implementation, the phase fitting module may include: The sequence extraction submodule is used to extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records; The sequence grouping submodule is used to group the two obtained sampling sequences according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampling sequence.

[0053] For example, the sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval.

[0054] In one implementation, the phase fitting module may include: The phase extraction submodule is used to extract the phase of the effective time delay in the time delay combination data based on a preset fitting algorithm, so as to obtain the phase information corresponding to each effective time delay. The information elimination submodule is used to identify phase cycle slips based on the change characteristics of the phase information, and eliminate the phase cycle slips by correcting the phase difference between adjacent effective time delays to obtain a continuous phase sequence. The sequence fitting submodule is used to generate a fitted autocorrelation function based on the continuous phase sequence.

[0055] In this implementation, the sequence fitting submodule may include: A phase fitting unit is used to fit the continuous phase sequence using the least squares linear fitting method to obtain phase change characteristics; The parameter inversion unit is used to invert the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width based on the phase change characteristics. The time delay integration unit is used to integrate the echo signal-to-noise ratio, Doppler velocity, Doppler spectral width and the effective time delay to generate a fitted autocorrelation function.

[0056] In one implementation, the product drawing module may include: The parameter extraction submodule is used to extract the fitted autocorrelation function for eastward detection and the fitted autocorrelation function for westward detection from the fitted autocorrelation function; The parameter sorting submodule is used to reverse the sign of the distance value of the fitted autocorrelation function of the westward detection and arrange it in time order with the fitted autocorrelation function of the eastward detection to obtain the fitted autocorrelation function sorted by time. The product generation submodule is used to draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function sorted over time.

[0057] In this implementation, the product generation submodule may include: The group extraction unit is used to extract the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate from the time-sorted fitted autocorrelation function. The structure generation unit is used to construct a sub-graph structure of the scattered echo fast view according to a preset layout rule based on the echo signal-to-noise ratio, Doppler velocity and Doppler spectral width parameters corresponding to each time node and each distance gate. A color level mapping unit is used to perform color level mapping on the sub-image structure to obtain image pixel color information; The quick view generation unit is used to draw a quick view of the scattered echo of the low-latitude high-frequency radar ionosphere according to the image pixel color information and a preset pixel size.

[0058] Example 4: like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0059] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the method for generating low-latitude high-frequency radar ionospheric monitoring data products in the above embodiments.

[0060] Example 5: Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the low-latitude high-frequency radar ionospheric monitoring data product generation method in the above embodiments.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for generating low-latitude high-frequency radar ionospheric monitoring data products, characterized in that, include: The echo signal from the ionosphere of a low-latitude high-frequency radar is received, and the echo signal is orthogonally demodulated and digitally sampled to generate original in-phase orthogonal data. The original in-phase orthogonal data is beamformed according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records; Based on the in-phase orthogonal records of the two beams, time delay combination data is formed, and phase fitting is performed on the time delay combination data to generate a fitting autocorrelation function; Based on the fitted autocorrelation function, a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere is plotted.

2. The method as described in claim 1, characterized in that, The step of forming time delay combination data based on the two beam in-phase orthogonal records includes: Extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records respectively; The two sampled sequences are grouped according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampled sequence.

3. The method as described in claim 2, characterized in that, The sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval.

4. The method as described in claim 1, characterized in that, The step of performing phase fitting on the time-delay combination data to generate a fitted autocorrelation function includes: Based on a preset fitting algorithm, the effective time delay in the time delay combination data is phase extracted to obtain the phase information corresponding to each effective time delay; Phase cycle slips are identified based on the change characteristics of the phase information, and the phase cycle slips are eliminated by phase difference correction of adjacent effective time delays to obtain a continuous phase sequence; Based on the continuous phase sequence, a fitted autocorrelation function is generated.

5. The method as described in claim 4, characterized in that, The step of generating a fitted autocorrelation function based on the continuous phase sequence includes: The continuous phase sequence is fitted using the least squares linear fitting method to obtain the phase change characteristics; Based on the phase change characteristics, the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width are retrieved. The echo signal-to-noise ratio, Doppler velocity, Doppler spectral width, and effective time delay are correlated and integrated to generate a fitted autocorrelation function.

6. The method as described in claim 1, characterized in that, The step of plotting a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function includes: Extract the fitted autocorrelation function for eastward detection and the fitted autocorrelation function for westward detection from the fitted autocorrelation function; The autocorrelation function fitted by the westward detection is reversed in terms of distance value and then arranged in chronological order with the autocorrelation function fitted by the eastward detection to obtain the time-sorted autocorrelation function. Based on the time-ordered fitted autocorrelation function, a fast view of the scattered echoes from the low-latitude high-frequency radar ionosphere is plotted.

7. The method as described in claim 6, characterized in that, The step of plotting a fast view of the scattered echoes of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function sorted over time includes: Extract the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate from the time-sorted fitted autocorrelation function; Based on the echo signal-to-noise ratio, Doppler velocity, and Doppler spectral width parameters corresponding to each time node and each distance gate, a sub-graph structure of the scattered echo fast view is constructed according to a preset layout rule; Color level mapping is performed on the sub-graph structure to obtain the image pixel color information; Based on the image pixel color information, a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere is drawn according to a preset pixel size.

8. A system for generating low-latitude, high-frequency radar ionospheric monitoring data products, characterized in that, include: The signal modulation module is used to receive echo signals from the ionosphere of a low-latitude high-frequency radar, perform orthogonal demodulation and digital sampling on the echo signals, and generate original in-phase orthogonal data. The beamforming module is used to perform beamforming on the original in-phase orthogonal data according to the receiving channels of the main array and the auxiliary array to obtain two beam in-phase orthogonal records. The phase fitting module is used to form time delay combination data based on the in-phase orthogonal records of the two beams, and to perform phase fitting on the time delay combination data to generate a fitting autocorrelation function; The product drawing module is used to draw a fast view of the scattered echo of the low-latitude high-frequency radar ionosphere based on the fitted autocorrelation function.

9. The system as described in claim 8, characterized in that, The phase fitting module includes: The sequence extraction submodule is used to extract the sampling sequence corresponding to the same set of non-equally spaced pulses from the two beam in-phase orthogonal records; The sequence grouping submodule is used to group the two obtained sampling sequences according to a preset unambiguous combination sequence to obtain the time delay combination data corresponding to each sampling sequence.

10. The system as described in claim 9, characterized in that, The sampling sequence contains echo amplitude and phase information corresponding to each detection distance; The unambiguous combination sequence is designed based on the transmission timing and detection resolution of non-equally spaced pulses to eliminate the superposition interference of different pulse echoes; The time delay combination data are arranged sequentially according to a preset time delay interval.

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