Visibility prediction methods and related equipment for low-Earth orbit satellites without prior information

Abstract

This invention discloses a method and related equipment for predicting the visibility of low-Earth orbit (LEO) satellites without prior information. It relates to the field of signal processing technology for LEO satellites in GNSS (Global Navigation Satellite System). The method includes: acquiring the LEO satellite; tracking the LEO satellite when it is successfully acquired; monitoring and recording the tracking parameters of the LEO satellite while tracking is active; recording the continuous invisible time of the LEO satellite when tracking is lost; obtaining the current tracking parameters and comparing them with historical tracking parameters when the LEO satellite is reacquired and re-enters tracking mode to determine the consistency between the two tracking states; and estimating the remaining invisible time based on the current time, the recording time of the historical tracking parameters, and the continuous invisible time of the LEO satellite. This invention achieves effective prediction of the visibility of LEO satellites.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal processing of low earth orbit satellites in GNSS (Global Navigation Satellite System), and in particular to a low earth orbit satellite visibility prediction method without prior information and related equipment. BACKGROUND

[0002] The orbit height of low earth orbit satellites is low, the orbit period is short, and the visible time for ground receivers is short. The ground receiver must initiate acquisition of the low earth orbit satellite in time within the visible time. Generally, the low earth orbit satellite will broadcast ephemeris information, and the receiver can use the received ephemeris information to predict the visible time of the low earth orbit satellite in time. However, if the low earth orbit satellite does not broadcast any ephemeris information and the receiver does not have the condition to obtain external satellite information, the orbit prediction of the low earth orbit satellite needs other methods. Under such conditions without any prior information, the acquisition of the low earth orbit satellite must traverse all possible signal parameters, which is inefficient and high in power consumption. How to not only predict the visible time of the satellite but also effectively predict the invisible time of the satellite under such conditions, so as to guide the receiver to start acquisition at the correct time and avoid invalid search, has become a technical problem to be solved.

[0003] Currently, there are several patented technologies dedicated to solving the problem of low-orbit satellite visible time prediction under the condition of no real-time ephemeris. Specifically, in the invention patent with the publication number CN115406448A and the subject name "Orbit prediction method for space-based Internet of Things terminal under ephemeris non-update condition", the satellite entry and exit time prediction is realized by obtaining the TLE data at a preset time and calculating based on the fitted prediction model. However, this method requires pre-acquisition of external TLE data and has a large amount of calculation, and it is completely invalid without TLE data, which cannot adapt to isolated receiving scenarios. In the invention patent with the publication number CN105044745A and the subject name "Circular orbit low-orbit satellite over-the-top remaining visible time prediction method", the satellite elevation angle and trace angle are calculated in real time using the user-received navigation signal and position information to solve the remaining visible time, which is relatively simple but relies on the satellite broadcast of spatial position and time information. When low-orbit satellites do not broadcast position information, the prediction function cannot be enabled. In the invention patent with the publication number CN110646819A and the subject name "Low-orbit satellite ephemeris prediction device and application method", the predicted ephemeris parameters are output by constructing an equation set and calculating the total error of historical ephemeris, which can reduce the frequency of ephemeris data reception, but the implementation process is very complex and occupies a large amount of computing power, which cannot run on low-power receivers. The method of "CN110058271A" and the subject name "Satellite signal acquisition and tracking method, device and satellite signal receiver" use real-time geographic information and pre-trained discrimination models to narrow the satellite search range and reduce the cold start capture time, but this method needs to rely on pre-trained models, increasing the computational burden of hardware, and mainly optimizes the cold start scenario, without fully considering the general demand for dynamic prediction and learning of low-orbit satellite visibility during long-term continuous operation of the receiver. The first run is invalid without model data, and the prediction logic cannot be dynamically updated.

[0004] The above existing technologies have common limitations: they all need to rely on additional satellite information (such as TLE, satellite broadcast position information), external training models, or involve complex calculation processes. These dependencies or complexities increase the implementation cost and computational burden of ground receiver systems, which cannot meet the efficient prediction and low-power consumption requirements in scenarios without prior information, resource constraints, and long-term operation. More importantly, these methods fail to fully utilize the historical tracking state information of low-orbit satellites accumulated by the receiver itself during long-term operation to realize a self-contained, low-complexity visibility and invisibility prediction, especially lacking effective means to estimate the remaining invisible time, a key parameter that directly affects capture scheduling and power consumption.

[0005] Therefore, it is necessary to provide a new technical solution, which can realize effective prediction of low-orbit satellite visible time and accurate estimation of its remaining invisible time by using the historical tracking state of low-orbit satellite accumulated by the receiver itself under the condition that the low-orbit satellite does not broadcast any ephemeris information and the receiver does not access external satellite information, thereby solving the efficiency and power consumption problems caused by the necessity of traversal acquisition under the condition of no prior information. SUMMARY

[0006] The technical problem solved by the present application is to overcome the deficiencies of the prior art, and specifically provides a low-orbit satellite visibility prediction method without prior information under the condition of no ephemeris information, satellite operation data and other prior information, and related equipment, as follows. 1) In a first aspect, the present application provides a low-orbit satellite visibility prediction method without prior information, and the specific technical solution is as follows: Capturing the low-orbit satellite; When the low-orbit satellite is successfully captured, tracking the low-orbit satellite; Under the tracking state, monitoring and recording the tracking parameters of the low-orbit satellite; When the tracking of the low-orbit satellite is lost, recording the continuous invisible time of the low-orbit satellite; When the low-orbit satellite is captured again and enters the tracking state, obtaining the current tracking parameters, comparing the current tracking parameters with the historically recorded tracking parameters, and obtaining the comparison result; When the comparison result is consistent, the remaining invisible time of the low-orbit satellite is estimated according to the current time, the recording time of the historically recorded tracking parameters, and the continuous invisible time of the low-orbit satellite.

[0007] The low-orbit satellite visibility prediction method without prior information provided by the present application has the following beneficial effects: This method enables effective prediction of the remaining invisibility time of low-Earth orbit (LEO) satellites, even without the satellites broadcasting any ephemeris information or the receiver connecting to external satellite networks. It relies solely on the historical tracking status of LEO satellites accumulated during continuous operation. This method eliminates the need for additional satellite ephemeris, two lines of element data, satellite-broadcast position information, or pre-trained external discrimination models. By using historical tracking information based on Doppler frequency and signal strength to replace external ephemeris and TLE data, it eliminates dependence on external information at its source. The monitoring and recording logic is embedded in the existing acquisition and tracking process, requiring no new hardware modules and reducing implementation complexity. By embedding the monitoring and recording of LEO satellite tracking parameters and the recording of continuous invisibility time into the conventional acquisition and tracking process, and performing parameter comparison and remaining invisibility time estimation upon reacquisition, the prediction function is integrated with the existing receiver architecture without significant hardware or software adjustments. The prediction of remaining invisible time can be directly used to guide the receiver's acquisition task scheduling, preventing hardware acquisition tasks from initiating invalid searches during satellite invisible periods, thereby reducing the overall power consumption of the receiver. As the receiver's operating time increases, the accumulated historical tracking status data of low-Earth orbit satellites becomes increasingly rich, gradually improving the accuracy of estimating the orbital period of low-Earth orbit satellites and predicting the remaining invisible time.

[0008] Based on the above scheme, the low-orbit satellite visibility prediction method without prior information of the present invention can be further improved as follows.

[0009] Furthermore, when the comparison results are consistent, the remaining invisible time of the low-Earth orbit (LEO) satellite is estimated based on the current time, the recording time of the tracking parameters in the historical records, and the continuous invisible time of the LEO satellite. This includes: when the comparison results are consistent, subtracting the recording duration of the tracking parameters in the historical records and the continuous invisible time of the LEO satellite from the current time to determine the visible time that the LEO satellite has passed within the current visible period; when the LEO satellite tracking is lost, subtracting the continuous invisible time in the historical records from the current time to determine the invisible time that the LEO satellite has passed within the current invisible period; and estimating the remaining invisible time of the LEO satellite based on the passed invisible time.

[0010] The beneficial effects of adopting the above-mentioned further scheme are as follows: By utilizing the continuous invisible time of the low-Earth orbit (LEO) satellite and matching historical records with the current time, the elapsed invisible time of the LEO satellite within the current invisible period can be determined first. This determined elapsed invisible time provides a direct and reliable input data basis for estimating the remaining invisible time of the LEO satellite. Estimation based on the elapsed invisible time makes the prediction of the remaining invisible time of the LEO satellite more accurate and reduces the uncertainty in the prediction process. This improves the reliability and practicality of the entire visibility prediction method and provides a clear decision-making basis for receiver sleep duration management and timing scheduling of the next acquisition attempt when the LEO satellite is in an invisible state.

[0011] Furthermore, it also includes: estimating the remaining visibility time of low-Earth orbit satellites based on the visibility time already passed; and updating the visibility flags of low-Earth orbit satellites according to the remaining visibility time of low-Earth orbit satellites.

[0012] The beneficial effects of adopting the above-described further scheme are as follows: By utilizing the elapsed visibility time, the remaining visibility time of low-Earth orbit (LEO) satellites is estimated, providing a precise time basis for updating the visibility flags of LEO satellites. Updating the visibility flags of LEO satellites based on their remaining visibility time allows the estimation results to be translated into explicit status instructions that can be directly queried by the hardware acquisition task. By querying the updated visibility flags, the hardware acquisition task can accurately determine the appropriate time to initiate acquisition of a specific LEO satellite, thereby avoiding invalid acquisition attempts when the satellite is invisible or when there is ample remaining visibility time. This process reduces the frequency of receiver-initiated hardware acquisition tasks, reduces unnecessary workload on the signal processing unit, and helps reduce the overall power consumption of the receiver.

[0013] Furthermore, the tracking parameters include Doppler frequency and signal strength.

[0014] The advantages of adopting the above-mentioned further scheme are as follows: selecting the Doppler frequency and signal strength of the low-Earth orbit (LEO) satellite as tracking parameters can accurately characterize the relative motion state and signal propagation conditions of the satellite. The Doppler frequency of the LEO satellite directly reflects the radial velocity change of the satellite, while the signal strength indicates the attenuation and quality of the signal propagation path. Both parameters can be obtained directly and reliably in a conventional tracking loop without additional hardware support. Their time-varying patterns together constitute the unique and repeatable orbital motion characteristics of each LEO satellite, providing a stable and highly discriminative basis for subsequent comparison of current tracking parameters with historical tracking parameters, thus ensuring the reliability of the comparison results and laying a data foundation for accurately estimating the remaining visible time of the LEO satellite.

[0015] 2) Secondly, the present invention also provides a low-Earth orbit satellite visibility prediction system without prior information, the specific technical solution of which is as follows: It includes a capture module, a tracking module, a monitoring module, a recording module, a comparison module, and an estimation module; The acquisition module is used to: acquire low-Earth orbit satellites; The tracking module is used to track low-Earth orbit (LEO) satellites when they are successfully acquired. The monitoring module is used to monitor and record the tracking parameters of low-Earth orbit satellites during tracking. The recording module is used to record the continuous period of time when tracking of a low-Earth orbit satellite is lost. The comparison module is used to: when a low-orbit satellite is recaptured and enters the tracking state, obtain the current tracking parameters, compare the current tracking parameters with the historical tracking parameters, and obtain the comparison results; The estimation module is used to estimate the remaining invisible time of the low-Earth orbit satellite when the comparison results are consistent, based on the current time, the recording time of the tracking parameters in the historical records, and the continuous invisible time of the low-Earth orbit satellite.

[0016] Based on the above scheme, the low-orbit satellite visibility prediction system without prior information of the present invention can be further improved as follows.

[0017] Furthermore, the estimation module is specifically used for: when the comparison results are consistent, determining the visible time that the low-orbit satellite has passed within the current visible period by subtracting the recording duration of the historical tracking parameters and the continuous invisible time of the low-orbit satellite based on the current time; when the low-orbit satellite tracking is lost, determining the invisible time that the low-orbit satellite has passed within the current invisible period by subtracting the continuous invisible time of the historical records based on the current time; and estimating the remaining invisible time of the low-orbit satellite based on the passed invisible time.

[0018] Furthermore, it also includes an update module; the estimation module is also used to: estimate the remaining visibility time of the low-Earth orbit satellite based on the visibility time that has been passed; the update module is used to: update the visibility flag of the low-Earth orbit satellite according to the remaining visibility time of the low-Earth orbit satellite.

[0019] Furthermore, the tracking parameters include Doppler frequency and signal strength.

[0020] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so that the electronic device implements any of the above-mentioned methods for predicting the visibility of low-Earth orbit satellites without prior information.

[0021] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any of the above-mentioned methods for predicting the visibility of low-Earth orbit satellites without prior information.

[0022] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is one of the flowcharts illustrating a method for predicting the visibility of low-Earth orbit satellites without prior information, according to an embodiment of the present invention. Figure 2 This is a second flowchart illustrating a method for predicting the visibility of low-Earth orbit satellites without prior information, according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a low-orbit satellite visibility prediction system without prior information, according to an embodiment of the present invention. Detailed Implementation

[0024] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0025] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0026] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for predicting the visibility of low-Earth orbit satellites without prior information, comprising the following steps: S1. Acquiring low-Earth orbit satellites, the specific implementation process is as follows: S10. After the receiver starts up and completes initialization, its RF front-end continuously receives radio signals in space. These signals are collected by the antenna and amplified by a low-noise amplifier to increase the signal level and suppress noise. The amplified signal then enters the downconverter. The downconverter converts the RF signal into an intermediate frequency (IF) analog signal. This process typically shifts the signal's center frequency to near zero to facilitate subsequent digital signal processing. For example, for a navigation signal with a carrier frequency of 1575.42 MHz, downconversion yields an analog signal with a center frequency of 0 MHz (i.e., zero IF). This IF analog signal is then sampled by an analog-to-digital converter (ADC), converting the continuous analog signal into a discrete digital sequence to obtain a digital IF signal sequence. .in, This is the index for discrete-time sampling points. The digital intermediate frequency signal sequence is a mixture containing possible target low-Earth orbit satellite signals, other satellite signals, noise, and interference.

[0027] S11. The digital signal processing unit initiates the acquisition process for the low-Earth orbit satellite. The goal of this process is to confirm the existence of the low-Earth orbit satellite signal without any prior information, and to make a preliminary estimate of its carrier Doppler frequency. Phase with pseudo-random code To achieve this goal, the receiver constructs a two-dimensional search grid consisting of a frequency dimension and a code phase dimension, systematically traversing the space of possible signal parameters. The frequency search range is set to... ,For example This range is determined based on the maximum possible Doppler frequency shift caused by the maximum possible relative radial velocity between the low-Earth orbit satellite and the ground receiver. The frequency search step is... ,For example The choice of step value requires a trade-off between acquisition accuracy and search speed. Code phase search covers the entire period of the pseudo-random code. (In terms of chip count). The code phase search step is... For example, corresponding The number of sampling points per chip. By traversing the entire two-dimensional grid, the potential locations of signals can be detected without omission.

[0028] S12. The local carrier replica is generated by a numerically controlled oscillator, and its discrete-time expression is: ,in, It is the local carrier search frequency set by the current search unit, corresponding to the current frequency point in the two-dimensional search grid. It is the sampling time interval of the analog-to-digital converter, that is, the time difference between adjacent sampling points. This is the initial phase of the numerically controlled oscillator. Simultaneously, the receiver internally generates a local pseudo-random code sequence. This sequence has the same structure as the pseudo-random code broadcast by the target low-orbit satellite and is used for subsequent correlation operations to identify specific satellite signals.

[0029] S13, The received digital intermediate frequency signal sequence Mixing with a locally generated carrier copy achieves carrier stripping, yielding baseband in-phase and quadrature components: , ,in, This indicates that the in-phase baseband component obtained after mixing is obtained by multiplying the received signal with a local carrier copy in cosine form. This indicates that the quadrature baseband component obtained after mixing is obtained by multiplying the received signal with a sinusoidal copy of the local carrier. Indicates the sampling time interval.

[0030] Then, the obtained baseband signal is compared with a signal that has undergone a specific phase shift. The local pseudo-random code sequence is correlated and accumulated to calculate the complex correlation value: in, It is the imaginary unit. This is the coherent integration length, i.e., the number of sampling points participating in one correlation accumulation, and the corresponding coherent integration time is... Its value is usually Milliseconds to several milliseconds. Indicates a specific search frequency and search code phase The calculated complex correlation value comprehensively reflects the degree of matching between the received signal and the local copy in terms of amplitude and phase. Indicates phase offset as The local pseudo-random code sequence.

[0031] S14. For each cell in the two-dimensional search grid Calculate its correlation power value, which is the square of the magnitude of the complex correlation value: ,in, Indicates the complex correlation value The real part, Indicates the complex correlation value The imaginary part of the power matrix. The receiver traverses the entire two-dimensional search grid to obtain a power matrix. Then, the maximum value is found in this power matrix. and its corresponding position parameters: ,in, Indicates the maximum power value The corresponding optimal Doppler frequency estimate, Indicates the maximum power value The corresponding optimal code phase estimate.

[0032] S15, The maximum power value obtained With a preset detection threshold A comparison is then made. Furthermore, to effectively distinguish between true signal peaks and accidental peaks caused by noise fluctuations, it is also necessary to determine... Whether it is significantly higher than the average power value in its surrounding local region (e.g., several adjacent frequency units and code phase units). The decision criterion can be expressed as: ,in, yes The average power value in the surrounding local area It is the standard deviation of the power value in that local area. It is a constant coefficient set according to the system's false alarm probability requirements, for example... or If both of the above conditions are met simultaneously, the target low-Earth orbit satellite signal is considered successfully acquired, and preliminary estimated parameters are output: Doppler frequency. and code phase Upon successful acquisition, the signal processing channel immediately switches to tracking mode for more precise parameter locking and navigation data demodulation. In this mode, the numerically controlled oscillator output frequency in the carrier tracking loop... This will be used to accurately compensate for the Doppler frequency shift of the signal. Since down-conversion has shifted the signal center frequency to zero, the subsequent Doppler frequency shift caused by the relative motion between the satellite and the receiver, as well as the receiver's local oscillator offset, will directly manifest as the control frequency of the digitally controlled oscillator. If the acquisition criteria are not met, the receiver continues searching for the next low-Earth orbit satellite or the next set of search parameters.

[0033] S2. Once a low-Earth orbit satellite is successfully acquired, it is tracked. The specific implementation process is as follows: S20. After successfully acquiring the low-Earth orbit (LEO) satellite signal, the LEO satellite immediately switches from acquisition mode to tracking mode. The purpose of tracking mode is to maintain precise synchronization with the acquired LEO satellite signal and continuously and accurately estimate two key parameters of the signal: the carrier Doppler frequency and the pseudo-random code phase. This process is achieved through two parallel feedback control loops: a carrier tracking loop and a code tracking loop. The carrier tracking loop is responsible for stripping the carrier from the signal, down-converting the signal to zero frequency; the code tracking loop is responsible for aligning the local pseudo-random code with the pseudo-random code in the received signal to despread the navigation data.

[0034] S21. The coarsely estimated Doppler frequency obtained in the previous step. As an initial value, a numerically controlled oscillator is used to generate an accurate local carrier copy. This local carrier copy is compared with the received intermediate frequency signal. Mixing is performed to generate two baseband signals: one in phase and one quadrature. To precisely control the carrier frequency and phase, the carrier tracking loop typically employs a phase-locked loop (PLL) or frequency-locked loop (FLL) structure. The loop phase detector or FLL generates an error control signal by calculating the phase or frequency error of the baseband signal. The error signal, after being smoothed by a loop filter, is fed back to the numerically controlled oscillator to dynamically adjust its output frequency. This ensures that the local carrier is synchronized with the carrier of the input signal, eliminating the effects of Doppler shift and receiver clock error.

[0035] S22, The coarsely estimated code phase obtained in the previous step. Starting with a code generator, a local pseudo-random code sequence is produced. The code tracking loop typically employs a delay-locked loop (LLO) structure, with an early-late gate correlator at its core. This correlator generates three local code copies: an immediate code, a lead code, and a lag code. The lead code and lag code each have a fixed, equal-sized, opposite-direction small time offset relative to the immediate code, for example... and Each of the three local code copies is correlated and integrated with the zero-frequency signal after carrier stripping.

[0036] S23. By comparing the correlation power values ​​of the leading and lagging channels, an error signal can be generated. This error signal reflects the deviation between the phase of the received signal's pseudo-random code and the phase of the local instantaneous code. A commonly used discriminator algorithm is: ,in, This indicates the relevant power value of the lead channel. This represents the correlation power value of the lag channel. When the local instantaneous code is perfectly aligned with the received signal code, the correlation power of the leading and lagging channels is equal, and the error signal... It is zero. If there is a deviation, The value will be positive or negative, and its magnitude is proportional to the amount of deviation.

[0037] S24, Code Phase Error Signal After filtering by an independent loop filter, the filtered control signal drives the clock frequency or phase of the code generator, thereby adjusting the generation rate and phase of the local pseudo-random code sequence, so that the instantaneous code copy accurately follows the changes in the phase of the received signal code. An auxiliary relationship exists between the code ring and the carrier ring; the accurate Doppler estimation provided by the carrier ring can help predict the rate of change of the code phase. This design is called a carrier-assisted code ring, which can improve the tracking performance of the code ring in dynamic environments.

[0038] S25. After the tracking loop stabilizes, the in-phase and quadrature signals output from the instantaneous channel. and It becomes pure and stable. Navigation data bits can be purified and stabilized by adjusting the in-phase components. Demodulation is achieved through integral clearing and sign decision. Simultaneously, the adjusted numerically controlled oscillator frequency output by the carrier tracking loop... The current signal Doppler frequency is a precise estimate, while the instantaneous code phase maintained by the code tracking loop is a precise measurement of the current signal propagation time. These parameters are continuously output, providing input for subsequent positioning calculations and tracking status monitoring in this invention.

[0039] The tracking state is the continuous signal processing state that a satellite navigation receiver enters after successfully acquiring a satellite signal. The purpose of tracking is to maintain precise and dynamic synchronization between the receiver's internally generated local signal copy (including the carrier and pseudo-random code) and the input signal from the satellite through closed-loop feedback control. This process compensates for Doppler shift, receiver clock drift, and signal propagation delay caused by the relative motion between the satellite and the receiver. Tracking is typically achieved by two cooperating loops: a carrier tracking loop for precisely matching the carrier frequency and phase of the signal; and a code tracking loop for precisely aligning the pseudo-random code phase of the signal. A stable tracking state allows the receiver to reliably demodulate the navigation data bits broadcast by the satellite and extract precise measurements for calculating distance and velocity, namely pseudorange and carrier phase.

[0040] S3. In tracking mode, monitor and record the tracking parameters of the low-Earth orbit satellite, including Doppler frequency and signal strength. The specific implementation process is as follows: S30. After the low-Earth orbit satellite signal enters a stable tracking state, the receiver's signal processing channel will continuously output accurate estimated parameters generated by the carrier tracking loop and code tracking loop. The monitoring function is activated as an independent software task or hardware logic module at fixed time intervals. This is triggered (e.g., every 5 or 10 seconds) to perform a parameter acquisition and recording operation. The algorithm can be configured based on the available computing resources and storage space of the receiver, providing flexibility.

[0041] S31. The monitoring task reads the Doppler frequency value of the low-Earth orbit satellite from the carrier tracking loop. This Doppler frequency value is directly derived from the instantaneous control frequency of the numerically controlled oscillator that controls the local carrier replica. .because It already includes the Doppler frequency shift caused by compensating for the relative motion between the satellite and the receiver, as well as the receiver's local oscillator offset; therefore, it is a direct measurement of the signal carrier frequency deviation at the current moment. Recorded Doppler frequencies of low-Earth orbit satellites. It can be obtained through the following relationship: . The unit is Hertz (Hz), and its positive or negative sign indicates the direction of relative motion.

[0042] S32. The monitoring task simultaneously calculates and records the signal strength of the low-Earth orbit satellite. Signal strength is typically measured using the carrier-to-noise ratio (C / N0) or correlated power value, which can be obtained from the instantaneous correlator output of the code tracking loop. At each monitoring moment, the in-phase and quadrature component values ​​after instantaneous channel coherence integration are read and recorded as follows: and Signal strength of low-Earth orbit satellites This can be obtained by calculating the square of the relevant amplitude: .

[0043] In order to convert it into a more general carrier-to-noise ratio form It is necessary to know the coherent integration time. And estimate the noise power spectral density The estimation formula is: ,in, This can be achieved by using a channel where no signal is present or by using a channel where no signal is present. It is estimated using long-term statistics of the channel. The carrier-to-noise ratio is measured in dB-Hz. It can be directly stored during recording. Or calculated The value serves as an indicator of signal strength for low-Earth orbit satellites.

[0044] S33. In addition to the two direct parameters, the Doppler frequency and signal strength of the low-Earth orbit satellites, the monitoring mission also records a derived parameter: continuous visibility time. This is the accumulated time from when the low-Earth orbit satellite was successfully acquired and put into tracking mode until the current monitoring moment. Each time a monitoring mission is triggered... Simply add a time interval to the previously recorded values. .

[0045] S34, All monitored parameters along with precise timestamps It is packaged into a data structure. This data structure must contain at least the following field: record timestamp. Doppler frequency of low-orbit satellites Signal strength of low-orbit satellites (or ), and continuous visible time This data packet is written to the receiver's non-volatile memory or an allocated circular buffer, forming a historical trace record.

[0046] S35. After completing this recording, the monitoring task enters a dormant state, awaiting the next recording cycle. This periodic monitoring and recording process continues throughout the entire visible period of the low-Earth orbit satellite (i.e., the period during which tracking is maintained), generating a series of time-series snapshots of tracking parameters. This historical data provides the foundation for subsequent predictions of the satellite's remaining visible and invisible times.

[0047] The Doppler frequency of a low-Earth orbit (LEO) satellite refers to the offset between the carrier frequency of the satellite signal actually received by the receiver and the carrier frequency of the signal transmitted by the satellite, caused by the relative radial motion between the LEO satellite and the ground receiver. The magnitude of this frequency offset is proportional to the instantaneous radial velocity of the satellite relative to the receiver, and its sign (positive or negative) indicates whether the satellite is approaching or moving away from the receiver. In the receiver's tracking loop, the LEO satellite's Doppler frequency is a key dynamic parameter that is estimated and compensated for in real time. Accurate measurement of the LEO satellite's Doppler frequency is not only used to maintain signal tracking but also to deduce the satellite's radial velocity information, forming the basis for velocity measurement functions in satellite navigation.

[0048] In this context, the signal strength of a low-Earth orbit (LEO) satellite refers to the power level of the radio signal received by a ground receiver from a specific LEO satellite. In navigation receivers, it is typically represented as the power of the baseband signal after carrier stripping and code despreading. Due to path loss, atmospheric attenuation, blockage, and multipath effects during signal propagation, the signal strength of an LEO satellite is a time-varying quantity. Internally, the signal strength of an LEO satellite is usually obtained by calculating the square of the amplitude of the correlator output signal and is often converted to a carrier-to-noise ratio (CNR). Monitoring the signal strength of LEO satellites helps determine signal tracking quality, detect signal blockage or interruption events, and in this invention, its variation pattern can serve as an auxiliary criterion for identifying the periodic characteristics of a satellite's orbit.

[0049] S4. When tracking of a low-Earth orbit satellite is lost, record the continuous period of time the low-Earth orbit satellite is not visible. The specific implementation process is as follows: S40. The receiver's signal tracking channel continuously monitors the tracking status of the low-Earth orbit (LEO) satellite. The effectiveness of the tracking status is jointly determined by the lock-on status of the carrier tracking loop and the code tracking loop. Within each processing cycle, the tracking logic checks a set of predetermined lock-on criteria. These criteria are primarily based on the reasonableness of the LEO satellite's signal strength and Doppler frequency. For example, when the LEO satellite's signal strength value, i.e., the carrier-to-noise ratio... The estimated value remains below a preset threshold. (For example, 25dB-Hz; actual tests will be conducted for each satellite signal, and a suitable value will be selected to approach the tracking limit.) This will be achieved for a certain duration. (This is typically set to the hundreds of milliseconds level to avoid sudden signal anomalies due to environmental factors. During this time, the loop is still adjusting the carrier frequency and code phase. Considering the existence of loop filtering, the adjustment of the carrier frequency and code phase will not be significant. If the signal recovers during this period, the loop can still re-track the signal.) This triggers a lock-loss suspicion. This judgment can be expressed as: ,in, Indicates at time The measured carrier-to-noise ratio, It is the minimum signal strength threshold required to maintain reliable tracking.

[0050] S41. Besides the signal strength of low-Earth orbit satellites, sudden changes in Doppler frequency are also an important auxiliary criterion for loss of lock, and the frequency error output by the carrier tracking loop... Abnormal values ​​output by the loop discriminator can be used for judgment. If the absolute value of the frequency error... Exceeding a maximum range expected by a dynamic model ,Right now If the tracking signal deviates from the true signal for several consecutive filtering cycles, it indicates that the tracking may have strayed from the actual signal. When one or more of the above-mentioned loss-of-lock criteria are met, the lock status flag of the tracking channel will be cleared, indicating that the tracking of the current low-Earth orbit satellite has been lost.

[0051] S42. Once a tracking loss event is confirmed, record the exact moment the tracking loss occurred. This moment It is defined as the end time of the processing cycle that satisfies the unlock criterion and triggers a state flag change. The timestamp representing the end of the current visible period for the low-orbit satellite is saved.

[0052] S43. Maintain a timer or an accumulation variable. , where superscript This indicates that it is aimed at the first Records from a low-Earth orbit satellite. From Initially, each time the system clock advances by one basic timing unit... (For example, every 1 second), this accumulated variable will be updated once: ,in, Indicates the current time. Indicates the time. The accumulated continuous invisible time. This process continues independently of signal capture attempts.

[0053] S44. The invisible time recorder needs to periodically write the accumulated continuous invisible time to non-volatile memory or a database associated with historical tracking records. Write operations can be performed at fixed intervals, such as every 10 seconds or every minute, or triggered when other events related to the LEO satellite occur (such as initiating a new acquisition attempt). Each record contains the following information: the identifier of the LEO satellite. Recording time and the cumulative continuous invisible time up to that moment. .

[0054] S45, When the receiver successfully reacquires the same low-Earth orbit satellite (identified as...) When the system transitions to a stable tracking state, the invisible time recorder receives an event notification. At this point, the recorder will make a final recording, saving the final continuous invisible time. Then reset the accumulator timer corresponding to the low-orbit satellite. The value is zero, and it enters a dormant state, waiting for the next tracking loss event to occur. The final recorded value is... This indicates the complete, continuous period of time during which the low-orbit satellite is not visible, from the end of the previous visible period to the beginning of the current visible period.

[0055] The continuous invisibility time of a low-Earth orbit (LEO) satellite refers to the entire time interval from the moment a ground receiver confirms the loss of signal tracking for a specific LEO satellite until the satellite's signal is successfully reacquired and stably tracked by the receiver. This time represents the duration during which the LEO satellite is radio unreachable from the ground receiver's location. The continuous invisibility time of an LEO satellite is determined by its orbital motion, primarily including the time the satellite is on the other side of the Earth or obscured by the Earth, and the periods when the satellite is above the horizon but the receiver cannot effectively receive the signal due to atmospheric effects or signal blockage. Accurately recording the continuous invisibility time of LEO satellites is crucial input data for predicting the satellite's orbital period and the next visible time in this invention.

[0056] S5. When the low-orbit satellite is recaptured and enters tracking mode, the current tracking parameters are obtained and compared with the historical tracking parameters to obtain the comparison result. The specific implementation process is as follows: S50. When the receiver successfully reacquires a low-Earth orbit (LEO) satellite and its carrier tracking loop and code tracking loop enter a stable locked state, a specific "parameter comparison and matching" task is triggered. The triggering condition for this task is: for a given LEO satellite identifier, the status flag of its tracking channel changes from "acquisition" or "lost lock" to "stable tracking," and this change is maintained for more than a preset stable time. (For example, 2 seconds). At this point, the system determines that the low-orbit satellite has re-entered a reliable visibility period and can initiate the comparison process with historical data.

[0057] S51. Read the latest filtered tracking parameters from the channel that is currently in a stable tracking state. This includes the Doppler frequency of the low-Earth orbit satellite at the current moment. Signal strength of low-Earth orbit satellites Doppler frequency of low-Earth orbit satellites The signal strength of low-Earth orbit satellites is derived directly from the control frequency of the carrier loop numerically controlled oscillator. With carrier-to-noise ratio The form obtained from the output of the instantaneous correlator. and The calculation shows that: ,in, It is the coherent integration time. This is the estimated noise power spectral density. For simplicity, let's denote it as... At the same time, record the current precise timestamp. .

[0058] S52. Based on the current low-Earth orbit satellite's identifier, query the satellite's historical tracking record file in non-volatile memory or a memory database. The goal of the search is to find historical data points within the last (or some historical) visible period that may correspond to the current moment in terms of the satellite's orbital motion phase. This is because the low-Earth orbit satellite's orbital period... For unknown but roughly known ranges (e.g., 1.5 to 2 hours), the search will cover the period from the current moment. Backtracking forward through all historical records within a time window of one to several possible period lengths. Assume a set of historical records is retrieved, where the first... Each historical record contains a historical timestamp. Doppler frequencies of historical low-orbit satellites Signal strength of historical low-Earth orbit satellites .

[0059] S53. Compare the Doppler frequencies of low-Earth orbit satellites. This comparison is not simply about absolute values, but rather about comparing trends and relative values. Because the Doppler frequency exhibits a roughly sinusoidal change from a negative maximum to a positive maximum (or vice versa) during satellite overhead, and because other satellite orbits also contribute to a fluctuating sinusoidal change in Doppler frequency, it is necessary to correlate it with historical data, considering time alignment. One method is to calculate the difference between the current frequency and historical frequencies at possible phase alignment points, assuming periodicity. Define the Doppler frequency comparison difference. for: .

[0060] However, a more effective comparison is to select a continuous time series from historical records and compare it with a shorter series that begins recording now, performing correlation or curve fitting similarity comparisons. For example, calculating the normalized cross-correlation coefficient between the Doppler frequency variation curve of the current time period and a curve from a historical period. . The closer it is to 1, the more consistent the change patterns of the two are.

[0061] S54. Compare the signal strength of low-Earth orbit satellites. Signal strength is greatly affected by the propagation path, and direct comparison of absolute values ​​has limited significance. The focus of the comparison is on the contour or trend of signal strength changes over time. Similarly, a method similar to that used with Doppler frequencies can be used to calculate the similarity between short sequences of current signal strength and historical signal strength sequences at possible alignment points, such as calculating the cross-correlation coefficient of trends. At the same time, it can be checked whether the two are in a similar intensity range, for example, to determine... Whether it is true or not, among which, It is an allowable threshold for signal strength difference.

[0062] S55. Set two similarity thresholds. and If a historical record or segment is found that makes the Doppler frequency similarity index... And the similarity index of signal strength At the same time, absolute difference and If the data is within an acceptable physical range, the comparison result is considered consistent. If, after traversing all candidate historical records within the retrieval time window, no historical record meeting the consistency condition is found, the comparison result is considered inconsistent. A consistent comparison result means that the currently captured and tracked low-orbit satellite is the same satellite as the historical satellite, and its orbital motion is periodic, with the current visible period matching the observation characteristics of a certain historical visible period.

[0063] S56. Output the comparison results. The output comparison results include the following fields: current low-Earth orbit satellite identifier, current timestamp. Matched historical time stamps (If consistent) Doppler frequency similarity value Signal strength similarity value and the final binary comparison result markers (For example, To indicate agreement, (Indicates inconsistency).

[0064] S6. When the comparison results are consistent, estimate the remaining invisible time of the low-Earth orbit satellite based on the current time, the recording time of the tracking parameters in the historical records, and the continuous invisible time of the low-Earth orbit satellite. Specifically: S60. When the comparison results are consistent, the visible time of the low-Earth orbit satellite is determined by subtracting the recording duration of the historical tracking parameters and the continuous invisible time of the low-Earth orbit satellite from the current time. The specific implementation process is as follows: When the parameter comparison and matching task outputs a comparison result flag. To ensure consistency, receive the output data packet from the comparison task. This data packet contains key input information: the current precise time. Recording times of successfully matched historical tracking parameters Simultaneously, based on the current identifiers of low-Earth orbit satellites, the complete, continuously invisible timeline corresponding to this recurring event is retrieved from non-volatile memory. In addition, a pre-calculated and stored parameter is retrieved from the historical statistics of this low-Earth orbit satellite: the historical average visible period length of this low-Earth orbit satellite. Historical average visible cycle length By analyzing the past of this low-orbit satellite The result is calculated from the first complete visible event. Each visible event begins with a successful capture and ends with a tracking loss. For the first... The next visible event, its visibility duration The calculation formula is ,in It is the first The start of the next visible cycle, i.e., the moment of successful capture; It is the first The end of the next visible period, i.e., the moment tracking is lost. Historical average visible period length. Is this it? Arithmetic mean of the duration of each visible event: To determine the visible time that a low-orbit satellite has passed within the current visible period, it is first necessary to access historical data. With the current moment Align with the same motion phase points within their respective visible periods. This is achieved by calculating the time elapsed within the historical visible period of each historical point. Assume the matched historical record belongs to the [missing information - likely a specific historical period]. There are several historically visible periods, and the start time of this period can be found by querying the historical database. Then the elapsed time of that historical record point within its own period. The calculation formula is: .in, Indicates the starting point of a historically visible cycle. up to the time this historical parameter was recorded The length of time elapsed serves as a phase bridge connecting historical cycles and the current cycle. Under the condition that the comparison results are consistent, the current moment can be considered... With historical moments They are in the same orbital motion phase within their respective visible periods. Therefore, the start time of the current visible period... Phase alignment can be used for estimation, as shown in the formula: This formula is based on a reasonable assumption: the time elapsed from the start of the visible period to the same phase point. The time elapsed in different periods is the same or highly similar. Finally, the visible time that a low-Earth orbit satellite has passed within the current visible period. Defined as the time starting from the current period up to the current moment The length of time elapsed. Based on the above estimation results, the calculation formula is as follows: .Will Substitute and get This means that, in this implementation, the visible time that a low-Earth orbit satellite has passed within the current visible period is... Numerically equal to the elapsed time of the historical record point within its own period. At this point, the visible time that the low-orbit satellite has passed within the current visible period has been determined.

[0065] S61. When low-Earth orbit satellite tracking is lost, based on the current time, subtract the historical continuous invisible time to determine the invisible time that the low-Earth orbit satellite has passed within the current invisible period. The specific implementation process is as follows: When tracking of a low-Earth orbit (LEO) satellite is lost, the receiver's signal tracking channel continuously monitors the tracking status of the LEO satellite. In each processing cycle, the tracking logic checks a set of predetermined loss-of-lock criteria, such as a signal strength consistently below a threshold or a carrier frequency error exceeding the expected range. When the loss-of-lock criteria are met, the lock status flag of the tracking channel is cleared, indicating a loss of tracking of the current LEO satellite, and the exact time of the tracking loss is recorded. .from Initially, the system activates or maintains a separate, continuous unreachable time accumulator for the low-Earth orbit (LEO) satellite. This accumulator is used to accumulate the duration for which the LEO satellite has been in a signal-unreachable state since the tracking was lost. The accumulator uses a fixed basic timing unit. Perform an update. Every time the system clock advances by one... The accumulator then increments the accumulated consecutive invisible time. This process continues until the low-Earth orbit satellite is successfully recaptured. When the satellite is subsequently recaptured and enters a stable tracking state, the system receives a successful capture event notification. At this point, the accumulator stops accumulating and records the final continuous invisible time. This value represents the complete continuous period of invisibility from the end of the previous visible period to the start of the current visible period. It is saved to non-volatile memory. This determines the amount of invisible time a low-Earth orbit (LEO) satellite has passed within the current invisible period, occurring when the LEO satellite is in a continuous invisible period and the remaining invisible time needs to be estimated. Assume the current time is... Furthermore, the low-orbit satellite has been missing since the last time it was lost. It has remained invisible ever since. Therefore, how much invisible time has the low-orbit satellite already passed within the current invisible period? That is, from the start of the invisible period up to the current moment The elapsed time. The calculation formula is: .in, This indicates the current time when the calculation is performed. This indicates the moment when the most recent tracking loss was confirmed, i.e., the start of the current invisible period. During system operation... The value can be obtained by querying the current time. And subtract the stored The value can be obtained in real time, or by directly reading and converting the current value of the aforementioned continuous invisible time accumulator.

[0066] S62. Based on the already passed invisible time, estimate the remaining invisible time of the low-Earth orbit satellite. The specific implementation process is as follows: The required data includes the historical average orbital period of low-Earth orbit satellites. The invisible time that low-orbit satellites have passed within the current invisible period. Historical average orbital period It is a pre-calculated and stored statistic that is approximately equal to the historical average length of the visible period. Compared to the historical average invisible time The sum of Historical average invisible time The remaining invisibility time of the low-Earth orbit satellite is calculated by averaging the consecutive invisibility times recorded over multiple complete orbital periods (each containing one visible event and one subsequent invisible event). Defined from the current calculation time The time interval until the expected end of the current invisible period (i.e., the start of the next visible period). The estimation is based on the historical average orbital period. It is assumed that the total length of the current orbital period is close to the historical average. Since a complete orbital period consists of one visible phase and one invisible phase, the expected total length of the current invisible period can be referenced to the historical average invisible time. Estimate the remaining invisible time for low-Earth orbit satellites. The basic formula is: .in, It is the historical average invisible time. This is the amount of time a low-Earth orbit satellite has been invisible within the current invisible period. The formula indicates that subtracting the already elapsed invisible time from the average invisible duration yields the estimated remaining invisible time. An equivalent estimation method uses the entire orbital period for calculation. This assumes that the start of the current orbital period is the start of the previous visible period. So, what is the expected start time of the next visible period? It can be estimated as The remaining invisible time for low-orbit satellites. Then it is With the current moment The difference. Combined with the already passed invisible time. Furthermore, considering the relationship between the visible and invisible periods, an estimation logic consistent with the above formula can be derived. To handle potential minor fluctuations in the orbital period itself and improve the robustness of the estimation, a standard deviation based on historical invisible time can be introduced. The tolerance. The final output of the remaining invisibility time of the low-Earth orbit satellite can be expressed as an interval estimate: .in, It is the standard deviation of continuous, unseen historical time, used to quantify its range of fluctuation; It is a coefficient selected based on the required confidence level, for example... In simplified applications, point estimates can be used directly. The remaining invisible time for low-orbit satellites.

[0067] Optionally, the above technical solution also includes: S70. Based on the already passed visible time, estimate the remaining visible time of the low-Earth orbit satellite. The specific implementation process is as follows: The required data includes the historical average visible period length of low-Earth orbit satellites. The visible time that low-orbit satellites have passed within the current visible period. Historical average visible cycle length The calculation method is described in step S60. The visible time that a low-orbit satellite has passed within the current visible period. The method for determining this is described in step S60. Remaining visible time for low-Earth orbit satellites. Defined from the current time The time interval until the expected end of the current visible period. Estimated based on the historical average visible period length. And it is assumed that the total length of the current visible period is close to the historical average. Therefore, the remaining visible time of low-Earth orbit satellites is estimated. The basic formula is: .in, It is the historical average length of the visible cycle. This is the visible time that a low-Earth orbit satellite has already traversed within the current visible period. This formula means that subtracting the elapsed visible time from the average visible period length yields the estimated remaining visible time. The relationship derived from S60... Substituting the values, we can obtain another equivalent formula: .in, This involves matching the elapsed time of a historical record point within its own historical period. To handle minor fluctuations that may exist within the visible period itself, a standard deviation based on the historical visible duration can be introduced. The tolerance. The final output of the remaining visible time of the low-Earth orbit satellite can be expressed as an interval estimate: or equivalent .in, It is the standard deviation of the historical visible duration; It is a coefficient selected based on the required confidence level, for example... In simplified applications, point estimates can be used directly. or The remaining visible time for low-Earth orbit satellites.

[0068] The visible time that a low-Earth orbit (LEO) satellite has passed within the current visible period refers to the accumulated time during which the satellite signal has been effectively locked and tracked by the receiver, from the start of the current visible event to the current calculated time. This time directly reflects how long the satellite has been visible to the receiver during this orbital transit. It is a cumulative value based on actual observations, typically measured in seconds or minutes. The visible time that a LEO satellite has passed within the current visible period is dynamic, increasing over time until the end of the current visible event. It is a fundamental input parameter for predicting the remaining duration of the current visible event.

[0069] The remaining visible time for a low-Earth orbit (LEO) satellite refers to the time interval remaining from the current calculation moment until the expected end of the current visible event for a tracked LEO satellite. It characterizes the duration for which the satellite signal is expected to be effectively received and processed by the receiver during the current orbital transit. The remaining visible time for an LEO satellite is an estimate based on historical statistical patterns (such as the average visible period length) and the current observation status (such as the visible time already passed). This estimate guides receiver resource allocation, for example, deciding when to stop tracking a disappearing satellite to save power, or pre-allocating acquisition resources for an upcoming satellite.

[0070] S71. Update the visibility flag of the low-Earth orbit satellite based on the remaining visibility time of the low-Earth orbit satellite. The specific implementation process is as follows: S710, the update process has two triggering conditions. The first condition is receiving a valid estimate of the remaining visible time from a low-Earth orbit satellite. and the corresponding low-orbit satellite identifiers The second condition is a periodic maintenance task performed internally by the receiver. This task scans all currently "visible" LEO satellite identifiers and checks whether their associated estimated remaining visibility time needs to be reassessed and triggers an identifier update. Regardless of the triggering condition, the target LEO satellite identifier must be obtained. And its latest remaining visible time information.

[0071] S711. Read a set of predefined threshold parameters from the configuration memory. These parameters are used to map continuous remaining visibility time estimates to discrete visibility flag states. Key thresholds include: ① the invisibility threshold. : This is typically set to a short time value (e.g., 60 seconds). When the estimated remaining visible time is less than or equal to this threshold, it means the satellite is about to leave the visible window. ② Capture Initiation Threshold This is typically set to a slightly longer time value (e.g., 300 seconds). When the remaining invisible time (estimated by subtracting the visible time from the orbital period) is less than this threshold, it means the satellite is about to enter the visible window. ③ Safety margin time A small positive time constant (e.g., 30 seconds) is used to avoid frequent flag switching at threshold boundaries, providing decision lag.

[0072] S712. Estimate the remaining visible time based on the obtained low-orbit satellite data. It executes a pre-defined state decision logic. This logic maintains a identifier for low-Earth orbit satellites. The internal state machine has states including "visible", "about to be invisible", "invisible", and "about to be visible". The decision rules are as follows: ① If the current status of a low-orbit satellite is "visible" or "about to become invisible", and the following conditions are met: If the target status is set to "about to become invisible", then the target status will be changed to "soon to be invisible". ② If the low-orbit satellite is currently marked as "visible", and the following conditions are met... If the target status remains "visible", then the target status should be kept "visible". ③ If the low-orbit satellite is currently marked as "invisible" or "about to become visible", it is necessary to combine this with the estimated remaining invisibility time of the low-orbit satellite. (can be accessed) Approximate estimation, where, The judgment is made based on the historical average orbital period. If it meets the following conditions... If the target status is set to "coming soon to be visible", then the target status will be set to "coming soon to be visible". ④ If the low-orbit satellite is currently marked as "invisible", and If so, the target state remains "invisible".

[0073] S713. Based on the target status determined in the previous step, generate the corresponding visibility flag value for low-orbit satellites. They are usually represented by numerical codes, for example: "00" means "invisible", "01" means "coming into view", "10" means "visible", and "11" means "coming into invisibility". This flag value is then used to... Writes are made to two locations. The first location is a globally shared memory flag area on the receiver, which allows for fast read access by other high-performance modules such as hardware acquisition tasks and tracking channel management. The second location is a status log file of the low-Earth orbit satellite in non-volatile memory, used for persistent storage and restoration of the status after a receiver restart. Write operations must be atomic or protected by mutexes to ensure data consistency.

[0074] S714, after updating the flag, sends an event notification to the receiver's task scheduler or hardware resource manager. The notification must include at least the low-Earth orbit satellite identifier. and the updated visibility flag This allows other subsystems to respond promptly to state changes. For example, when the task scheduler receives a "soon to be invisible" flag, it can begin to gradually release the computing resources allocated to that satellite tracking channel; or when it receives a "soon to be visible" flag, it can allocate a signal processing channel and computing resources in advance for the hardware acquisition task, preparing for acquisition.

[0075] S715. Cases where estimation information is incomplete or outdated need to be handled. For example, if a valid remaining visibility estimate for a low-Earth orbit satellite cannot be obtained, but the satellite's tracking channel is actually in a stable tracking state, its flag should be forcibly set to "visible." Additionally, for any satellite in the "about to be visible" state, an internal monitoring timer will be activated. If this state persists for far longer than... If no report of successful satellite acquisition is received after a certain period of time (e.g., twice the time), its flag should be reset to "invisible" and a prediction failure event should be recorded for subsequent algorithm optimization.

[0076] The visibility flag for low-Earth orbit (LEO) satellites is a state variable maintained internally by the receiver, specifically representing the predicted visibility status of a particular LEO satellite relative to the receiver in the current and near future. It is a discrete, finite enumeration value, not a continuous physical quantity. Common states include "invisible," "about to become visible," "visible," and "about to become invisible." The core function of the LEO satellite visibility flag is to serve as an efficient and abstract interface between the hardware acquisition task and the higher-level scheduling algorithm. The hardware acquisition task queries this flag periodically through polling or event listening to determine whether and when to initiate a resource-intensive acquisition process for a particular LEO satellite. By relying on this predictive flag, the receiver can avoid blindly and frequently traversing all satellites, instead concentrating acquisition resources on the satellites most likely to succeed, thereby significantly reducing system power consumption—a key beneficial effect of this invention.

[0077] Optionally, in the above technical solution, when the comparison results are inconsistent, the current tracking parameters are stored as a new historical record for the low-Earth orbit satellite, and the visibility flag of the low-Earth orbit satellite is updated to "visible." Simultaneously, the tracking and monitoring process for the low-Earth orbit satellite is maintained. The specific implementation process is as follows: 1) After the parameter comparison and matching task completes the comparison with all relevant historical records, if the final comparison result output indicates... If an inconsistency is found, an inconsistency handling process is triggered. Specifically, the complete output data packet from the comparison task is received, which contains the identifier of the current low-Earth orbit satellite. Current moment The Doppler frequency of current low-orbit satellites Current signal strength of low-Earth orbit satellites and the set of similarity values ​​for all candidate historical records. .

[0078] 2) Analyze the reasons for the inconsistency between the recorded data and the actual situation. There are two main possible causes: First, the currently captured and tracked low-Earth orbit (LEO) satellite is the correct target satellite, but its current orbital phase (i.e., its position within its visible period) does not match the phase in any stored historical records. This could occur when the satellite is captured for the first time, when the amount of historical data is insufficient, or when there is a small but observable change in the orbital parameters (requiring modification). Second, the currently captured signal may be a completely new LEO satellite signal that has not been previously recorded. For subsequent decision-making and learning, an inconsistency event log needs to be created. This log contains the following fields: event timestamp. (Right now Low Earth Orbit Satellite Identification Inconsistency type identifier (Initially set to "Pending"), and a statistical summary of all compared similarity values, such as the maximum similarity value. and .

[0079] 3) Current observation data needs to be incorporated into the historical database to enrich the knowledge base and generate a completely new historical tracking record. This record is similar in structure to a regular monitoring record, but has a special initial marker. The record content includes: ① Historical record timestamps. ② Doppler frequencies of historical low-Earth orbit satellites ③Signal strength of historical low-Earth orbit satellites ④ Visible period identifier Since no historical period can be matched, the system creates a new period identifier, for example, using an incrementing sequence number. ⑤ Record status flags Mark as "Initial Record" or "Not Associated".

[0080] 4) If this is the identifier of the low-Earth orbit satellite When the first historical tracking record is entered, or when existing historical records are insufficient to represent a complete cycle, the system initializes a set of statistical variables for this new cycle. This includes setting the start time of the currently visible cycle. and initialize the cumulative visible time for that period. Simultaneously, a visible time accumulator for this new cycle is started, with its update logic synchronized with the monitoring task, meaning it updates every recording interval. accumulator increases : This accumulated value will be used to calculate the actual visible duration at the end of this future cycle.

[0081] 5) Despite the discrepancy, the satellite is indeed in a stable tracking state, therefore it is undoubtedly "visible." A command will be sent to the receiver's resource management unit to identify this low-Earth orbit satellite. Corresponding visibility flag Explicitly setting it to "visible" ensures that the hardware acquisition task, upon querying, knows that this satellite no longer needs to be acquired again, thus avoiding repeated and unnecessary acquisition attempts and reducing hardware power consumption. The update operation can be represented as: .

[0082] 6) After completing the above recording and flag updates, the handling of this low-Earth orbit satellite will revert to the standard, periodic tracking status monitoring and recording procedure. This means that as long as tracking is not lost, monitoring will continue at intervals. Record its tracking parameters (Doppler frequency, signal strength, cumulative visibility time), and append these new records to the historical database under that LEO satellite identifier, while also associating them with the new visibility period identifier to which it belongs. This newly accumulated data will build a more complete orbital profile for the satellite, greatly increasing the likelihood of obtaining consistent results through parameter comparison when it reappears in the future.

[0083] 7) Periodically examine all historical records marked "unassociated" or "initial record." Once enough new historical records have accumulated for the same low-Earth orbit satellite, forming a complete visible cycle from acquisition to loss, the system can attempt to correlate these new records internally and calculate the characteristics of that cycle (such as the mean Doppler curve). Furthermore, it can attempt to compare the characteristics of the new cycle with those of earlier, equally unmatched historical cycles across cycles to discover previously unidentified matches, thereby correcting or enriching the historical database. This continuous learning capability allows the predictive power of this method to gradually improve as the receiver's operating time increases.

[0084] In low-Earth orbit (LEO) satellite visibility prediction methods without prior information, understanding the orbital characteristics of LEO satellites is crucial for improving prediction accuracy. Each LEO satellite orbits the Earth in a fixed orbital plane. Multiple LEO satellites may share the same fixed orbital plane; this fixed orbital plane design forms the foundation for large-scale navigation or communication constellation networks. Due to the Earth's rotation, the projection of the satellite's orbital plane relative to a fixed ground observation point causes a continuous drift. This drift causes the azimuth angle at which the satellite rises above the horizon, as observed by the ground receiver, to change continuously over time. Orbital perturbations, such as the Earth's non-spherical gravity and solar radiation pressure, only slightly correct the drift rate of the orbital plane but do not alter the long-term westward shift of the orbital plane. When a receiver acquires and tracks LEO satellites rising from different azimuth angles, the satellite's motion geometry relative to the receiver differs. This leads to variations in the Doppler frequency of the LEO satellite measured by the receiver over time, as well as differences in the length of the LEO satellite's visible time. This difference in observational characteristics caused by azimuth changes presents an additional challenge to low-orbit satellite visibility prediction without any prior information.

[0085] However, considering that multiple low-Earth orbit (LEO) satellites with different identifiers may be deployed within the same fixed orbital plane, and these LEO satellites will pass through the receiver's visible airspace sequentially at certain time intervals, historical tracking information of a previously observed LEO satellite within the same orbital plane can be collected and utilized to predict the visibility of a later-appearing LEO satellite with a different identifier. Specifically, in the step of "when a LEO satellite is recaptured and enters tracking mode, the current tracking parameters are obtained, and the current tracking parameters are compared with the historical tracking parameters to obtain the comparison result," the scope of historical record retrieval can be expanded. The system not only retrieves the historical records of the current LEO satellite's identifier itself but also retrieves the historical records of other LEO satellites that may belong to the same orbital plane as the current LEO satellite. Since satellites belonging to the same orbital plane have similar motion patterns, the historical tracking parameters of previous satellites (especially the Doppler frequency variation trend of LEO satellites) can provide valuable references for predicting the behavior of subsequent satellites. By comparing the Doppler frequency variation curve of the currently captured low-Earth orbit satellite with the historical Doppler frequency variation curve of other satellites in the same orbital plane, the probability of a successful match can be increased, thereby improving the accuracy of the estimation of the remaining visible or invisible time of the low-Earth orbit satellite.

[0086] Regarding specific methods for consistency comparison, since changes in the satellite's azimuth angle affect the observation curve, using complex mathematical models for curve fitting may be difficult to obtain accurate and universal expressions. Therefore, a more computationally efficient and robust cross-correlation algorithm can be considered to evaluate the similarity between the current tracking parameter sequence and historical tracking parameter sequences. For example, for Doppler frequency comparison of low-Earth orbit (LEO) satellites, the normalized cross-correlation function value can be calculated between the LEO satellite's Doppler frequency sequence within the current time period (e.g., several tens of seconds from the moment of successful acquisition) and the corresponding time period's LEO satellite Doppler frequency sequence in the candidate historical records. Let the current Doppler frequency sequence be... The historical Doppler frequency sequence is The sequence length is Then the normalized cross-correlation coefficient It can be calculated as: in, It is the current Doppler frequency sequence The mean, It is a historical Doppler frequency sequence The mean, It is the index of a time point in the sequence. The closer the value is to 1, the more similar the two curves are in shape and trend. By setting an appropriate similarity threshold... ,when When the comparison results are consistent along the Doppler frequency dimension, it can be determined that the results are consistent. This cross-correlation-based comparison method is not sensitive to the absolute amplitude of the signal, better reflects the similarity of the changing trend, and has relatively low algorithm complexity, which is beneficial for implementation on resource-constrained receiver platforms.

[0087] This application addresses the scenario where low-Earth orbit (LEO) satellites do not broadcast any ephemeris information, and the receiver does not connect online to obtain satellite position information. Specifically, the receiver uses only the historical tracking status of LEO satellites to predict their visibility time. After a cold start, the receiver continuously acquires all LEO satellites. The acquisition process involves the receiver traversing all possible parameters in a two-dimensional search space of frequency and code phase without prior information. It confirms signal presence by calculating the correlation power between the received signal and its local copy and detecting peak values, thus obtaining preliminary Doppler frequency and code phase estimates. Successfully acquired LEO satellites enter tracking mode. Once an LEO satellite is successfully acquired, the tracking process involves the receiver activating a carrier tracking loop and a code tracking loop. Through closed-loop feedback control, the local carrier and pseudo-random code precisely follow changes in the input signal, maintaining signal synchronization and outputting accurate tracking parameters.

[0088] During stable tracking of a low-Earth orbit (LEO) satellite, the receiver monitors and records the tracking parameters of the LEO satellite, including its Doppler frequency, signal strength, and continuous visibility time. Monitoring is performed at configurable fixed time intervals, and the parameters, along with timestamps, are saved as a historical tracking record. When satellite tracking is lost, the receiver records the continuous invisible time of the LEO satellite. Specifically, the receiver determines a loss of lock based on anomalies in signal strength and Doppler frequency, records the moment of loss, and starts a timer to accumulate the invisible duration until the satellite becomes visible and is successfully acquired again.

[0089] When the low-Earth orbit (LEO) satellite is recaptured and put into tracking mode, the current tracking parameters are acquired and compared with historical tracking parameters to obtain the comparison result. Specifically, the receiver retrieves the satellite's historical records and calculates the similarity between the current and historical parameters in terms of Doppler frequency variation trends and signal strength profiles. If the comparison result is consistent, the visible time already passed within the current visible period is determined based on the current time, the recording time of the historical tracking parameters, and the LEO satellite's continuous invisible time. Based on the passed visible time, the remaining visible time of the LEO satellite is estimated. Specifically, the estimation involves determining the phase offset using matched historical record points and combining this with the historical average visible period length to calculate the elapsed time and remaining time of the current period. If the comparison result is inconsistent, the current tracking parameters are stored as new historical records for the LEO satellite, and the satellite's visibility flag is updated to "visible," while maintaining the tracking and monitoring process.

[0090] Subsequently, the visibility flag of the low-Earth orbit (LEO) satellites is updated based on their remaining visibility time. Specifically, the visibility management module compares the estimated remaining visibility time with a preset threshold, updating the flag to "visible," "about to become invisible," "invisible," or "about to become visible," and writing this information to shared memory for the hardware acquisition task to query. This process allows the hardware acquisition task to initiate acquisition only for visible or soon-to-be-visible LEO satellites, reducing the frequency of receiver acquisition tasks and lowering hardware power consumption.

[0091] The above describes the tracking and monitoring process of a single low-Earth orbit (LEO) satellite, which can be easily extended to the tracking and monitoring of multiple LEO satellites. Each LEO satellite has its own independent historical tracking record database and visibility markers. The receiver performs the same acquisition, tracking, monitoring, recording, comparison, and prediction process on multiple satellites in parallel or time-division multiplexing mode. After the receiver has been running continuously for a period of time, a visible and invisible information model of all satellites can be gradually established, making the scheduling of hardware acquisition tasks more efficient.

[0092] Because of their low orbit and short period, low-Earth orbit (LEO) satellites complete one orbit around the Earth in a short time (e.g., about two hours). This results in a shorter visible time for ground receivers (e.g., about ten minutes). Therefore, receivers can collect tracking status data of LEO satellites within a complete orbital cycle much faster. Furthermore, the monitoring and recording frequency can be adjusted based on the receiver's available computing resources and storage space, such as recording every 5 or 10 seconds. This provides flexibility for implementing the algorithm on resource-constrained platforms. As the receiver's operating time increases, the accumulated historical tracking status data becomes richer, and the accuracy of predicting the visible time of LEO satellites gradually improves.

[0093] like Figure 2 As shown, the method of this embodiment includes: The first step is to power on the receiver, complete the hardware self-test, software system loading and parameter configuration, allocate storage space for the data structure of all low-Earth orbit satellites to be monitored, and initialize the visibility flag of each low-Earth orbit satellite to "invisible".

[0094] The second step involves entering the main loop, corresponding to "querying satellite visibility." The receiver, based on a pre-set satellite list or scheduling strategy, sequentially queries the visibility flag of each low-Earth orbit satellite. If the flag indicates the satellite is "invisible" or "about to become visible," the process leads to the "hardware acquisition task"; if the flag indicates "visible," acquisition is skipped, and the process proceeds directly to the subsequent tracking and monitoring phase.

[0095] The third step is to execute the hardware acquisition task. For the low-Earth orbit satellite to be acquired, the receiver, without prior information, performs a traversal search within the two-dimensional search space of frequency and code phase, determining the presence of a signal by calculating the correlation power and detecting peak values. Then, a "Acquisition successful?" check is performed. If acquisition fails, the process returns to the second step to check the visibility of the next satellite; if acquisition is successful, it proceeds to the next stage.

[0096] The fourth step is to enter tracking mode. For successfully acquired low-Earth orbit satellites, the receiver immediately initiates the carrier tracking loop and code tracking loop. Through closed-loop feedback control, it keeps the local signal copy precisely synchronized with the input signal and outputs accurate parameters such as the Doppler frequency of the low-Earth orbit satellite.

[0097] The fifth step is to monitor the tracking status. In a stable tracking state, the receiver periodically monitors and records tracking parameters at configurable fixed time intervals, including the Doppler frequency of the low-Earth orbit (LEO) satellite, the signal strength of the LEO satellite, and the cumulative continuous visibility time.

[0098] The sixth step involves continuously determining satellite visibility during monitoring. This determination is based on the tracking loop's lock status and signal quality. If the determination is "yes," the process continues with the monitoring and recording in step five. If tracking is lost, and the determination is "no," the process enters the "update" phase.

[0099] The seventh step is to perform the "update" operation. After tracking is lost, the system records the time of loss and begins or continues to accumulate the continuous invisible time of the low-Earth orbit satellite. Simultaneously, based on the data collected during this visibility cycle, the system may perform historical parameter comparisons and estimate the remaining visibility time, and update the visibility flag of the low-Earth orbit satellite accordingly (e.g., updating to "invisible" or "soon to be visible"). After the update is completed, the process returns to the second step, "query satellite visibility," thus forming a continuously running, learning, and optimizing closed-loop processing flow.

[0100] This invention addresses the problem that acquiring low-Earth orbit (LEO) satellites requires traversing all satellites without any prior information. It proposes a method for predicting LEO satellite visibility using only historical tracking information. This method is relatively simple to implement, requiring only the addition of periodic monitoring and recording of LEO satellite tracking status to the general acquisition and tracking process. It does not involve significant hardware or software adjustments and remains effective even for LEO satellites that do not broadcast any ephemeris information. The method's effectiveness lies in the fact that as the receiver continues to operate, the accumulated historical tracking status data of LEO satellites becomes increasingly rich, continuously improving the accuracy of parameter comparison and periodic matching, thereby gradually increasing the prediction accuracy of LEO satellite visibility time. Without any prior information about LEO satellites, this method continuously monitors the tracking status of LEO satellites, periodically recording the Doppler frequency, signal strength, and cumulative tracking duration over time; when tracking is lost, it records the continuous period of invisibility of the LEO satellite. When a low-Earth orbit (LEO) satellite is recaptured and matches historical records, the system can calculate the visible time the LEO satellite has passed within the current visibility period and estimate its remaining visible time and subsequent invisibility time. Based on these estimates, the system dynamically updates the visibility flags of the LEO satellites, setting them to "visible," "about to be visible," "about to be invisible," or "invisible." This method requires no additional dedicated hardware resources, primarily utilizing the receiver's existing signal processing channels and general-purpose storage units, without altering the existing software architecture. The frequency of monitoring and recording, as well as the storage scale of historical data, can be flexibly adjusted according to the receiver's available computing resources and storage space. For example, recording can be performed every 5 seconds, 10 seconds, or longer intervals, achieving configurability in terms of computing power and space usage for the LEO satellite visibility prediction function. Finally, the hardware capture task, by querying the updated LEO satellite visibility flags, can initiate capture only for LEO satellites marked "visible" or "about to be visible," avoiding blind full ephemeris traversal and significantly reducing the hardware capture frequency and overall power consumption.

[0101] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation. The scheme after adjusting the order is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0102] like Figure 3 As shown, an embodiment of the present invention provides a low-Earth orbit satellite visibility prediction system 200 without prior information, which includes an acquisition module 201, a tracking module 202, a monitoring module 203, a recording module 204, a comparison module 205, and an estimation module 206. The acquisition module 201 is used for: acquiring low-Earth orbit satellites; Tracking module 202 is used to track low-Earth orbit satellites when they are successfully acquired. The monitoring module 203 is used to: monitor and record the tracking parameters of the low-Earth orbit satellite in tracking mode; The recording module 204 is used to: record the continuous invisible time of a low-Earth orbit satellite when tracking of the low-Earth orbit satellite is lost; The comparison module 205 is used to: when a low-orbit satellite is recaptured and enters the tracking state, obtain the current tracking parameters, compare the current tracking parameters with the historical tracking parameters, and obtain the comparison result; The estimation module 206 is used to: when the comparison results are consistent, estimate the remaining invisible time of the low-orbit satellite based on the current time, the recording time of the tracking parameters in the historical records, and the continuous invisible time of the low-orbit satellite.

[0103] Optionally, in the above technical solution, the estimation module 206 is specifically used for: When the comparison results are consistent, the visible time that the low-orbit satellite has passed in the current visible period is determined by subtracting the recording duration of the historical tracking parameters and the continuous invisible time of the low-orbit satellite from the current time. When a low-Earth orbit satellite is lost, the invisible time that the low-Earth orbit satellite has passed within the current invisible period is determined by subtracting the continuous invisible time in the historical records from the current time. Estimate the remaining invisibility time for low-Earth orbit satellites based on the already passed invisibility time.

[0104] Optionally, the above technical solution also includes an update module; the estimation module 206 is further configured to: estimate the remaining visibility time of the low-orbit satellite based on the already passed visibility time; the update module is configured to: update the visibility flag of the low-orbit satellite according to the remaining visibility time of the low-orbit satellite.

[0105] Optionally, in the above technical solution, the tracking parameters include Doppler frequency and signal strength.

[0106] It should be noted that the beneficial effects of the low-Earth orbit satellite visibility prediction system 200 without prior information provided in the above embodiments are the same as those of the low-Earth orbit satellite visibility prediction method without prior information, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0107] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned methods for predicting the visibility of low-Earth orbit satellites without prior information.

[0108] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for predicting the visibility of low-Earth orbit satellites without prior information.

[0109] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0110] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the visibility of low-Earth orbit satellites without prior information, characterized in that, include: Capture low-Earth orbit satellites; Once the low-orbit satellite is successfully acquired, it will be tracked. During tracking, the tracking parameters of the low-Earth orbit satellite are monitored and recorded; When tracking of the low-orbit satellite is lost, record the continuous period of time the low-orbit satellite is not visible. When the low-orbit satellite is recaptured and enters the tracking state, the current tracking parameters are obtained and compared with the historical tracking parameters to obtain the comparison result. When the comparison results are consistent, the remaining invisible time of the low-orbit satellite is estimated based on the current time, the recording time of the tracking parameters in the historical record, and the continuous invisible time of the low-orbit satellite.

2. The method for predicting the visibility of low-Earth orbit satellites without prior information as described in claim 1, characterized in that, When the comparison results are consistent, the remaining invisibility time of the low-Earth orbit satellite is estimated based on the current time, the recording time of the tracking parameters in the historical records, and the continuous invisibility time of the low-Earth orbit satellite, including: When the comparison results are consistent, the visible time that the low-orbit satellite has passed in the current visible period is determined by subtracting the recording duration of the tracking parameters of the historical record and the continuous invisible time of the low-orbit satellite from the current time. When tracking of a low-Earth orbit satellite is lost, the invisible time that the low-Earth orbit satellite has passed within the current invisible period is determined by subtracting the continuous invisible time in the historical record from the current time. Based on the already passed invisible time, estimate the remaining invisible time of the low-orbit satellite.

3. The method for predicting the visibility of low-Earth orbit satellites without prior information as described in claim 2, characterized in that, Also includes: Based on the visible time already passed, estimate the remaining visible time of the low-Earth orbit satellite; The visibility flag of the low-Earth orbit satellite is updated based on the remaining visibility time of the low-Earth orbit satellite.

4. A method for predicting the visibility of low-Earth orbit satellites without prior information according to any one of claims 1 to 3, characterized in that, The tracking parameters include Doppler frequency and signal strength.

5. A low-Earth orbit satellite visibility prediction system without prior information, characterized in that, It includes a capture module, a tracking module, a monitoring module, a recording module, a comparison module, and an estimation module; The capture module is used to: capture low-orbit satellites; The tracking module is used to track the low-orbit satellite when it is successfully captured. The monitoring module is used to: monitor and record the tracking parameters of the low-orbit satellite in tracking mode; The recording module is used to: record the continuous invisible time of the low-orbit satellite when tracking of the low-orbit satellite is lost; The comparison module is used to: when the low-orbit satellite is recaptured and enters the tracking state, obtain the current tracking parameters, and compare the current tracking parameters with the historical tracking parameters to obtain the comparison result; The estimation module is used to: when the comparison results are consistent, estimate the remaining invisible time of the low-orbit satellite based on the current time, the recording time of the tracking parameters of the historical records, and the continuous invisible time of the low-orbit satellite.

6. The low-Earth orbit satellite visibility prediction system without prior information according to claim 5, characterized in that, The estimation module is specifically used for: When the comparison results are consistent, the visible time that the low-orbit satellite has passed in the current visible period is determined by subtracting the recording duration of the tracking parameters of the historical record and the continuous invisible time of the low-orbit satellite from the current time. When tracking of a low-Earth orbit satellite is lost, the invisible time that the low-Earth orbit satellite has passed within the current invisible period is determined by subtracting the continuous invisible time in the historical record from the current time. Based on the already passed invisible time, estimate the remaining invisible time of the low-orbit satellite.

7. A low-Earth orbit satellite visibility prediction system without prior information as described in claim 6, characterized in that, It also includes an update module; The estimation module is also used to: estimate the remaining visibility time of the low-orbit satellite based on the already passed visibility time; The update module is used to update the visibility flag of the low-Earth orbit satellite based on the remaining visibility time of the low-Earth orbit satellite.

8. A low-Earth orbit satellite visibility prediction system without prior information according to any one of claims 5 to 7, characterized in that, The tracking parameters include Doppler frequency and signal strength.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting the visibility of low-Earth orbit satellites without prior information as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the low-orbit satellite visibility prediction method without prior information as described in any one of claims 1 to 4.

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