Self-adaptive multi-constellation software-defined low-orbit satellite receiver and design method thereof

Abstract

The invention discloses a self-adaptive multi-constellation software-defined low-orbit satellite receiver design method, and relates to the field of satellite navigation, and the receiver can detect visible satellites and provide proper Doppler measurement for a positioning system based on extended Kalman filter (EKF). The design method mainly comprises the following steps: acquiring a downlink signal from an LEO satellite; detecting an overhead satellite by performing a Welch Power Spectral Density (PSD) analysis; tracking the Doppler frequency of each detected satellite by using a phase-locked loop (PLL) algorithm; a measurement switching filter is designed, and a correct measurement value is selected for the Doppler positioning architecture. The invention further discloses a self-adaptive multi-constellation software-defined low-orbit satellite receiver. The receiver can track the Doppler and the Doppler rate of the double-constellation satellite at the same time. Compared with a single constellation method, the method has the advantage that the positioning precision of a fixed receiver is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation, and in particular to an adaptive multi-constellation software-defined low-orbit satellite receiver and a design method thereof. Background Art

[0002] As the problem of limited signals of Global Navigation Satellite System (GNSS) in dense urban areas or indoor environments becomes increasingly prominent, navigation based on signal of opportunity (Nav-SOP) becomes an effective alternative. The integration of inertial navigation system (INS) and global positioning system (GPS) has been one of the most reliable navigation methods in recent decades. This method takes advantage of both the short-term accuracy of INS and the long-term robustness of GPS. However, some substantial shortcomings of the GPS / INS integration method have led researchers to focus on the Nav-SOP method instead. The main issues of GPS / INS integration include possible signal interference, sensitivity to interference, and inaccessibility in some dense urban and indoor environments.

[0003] Low Earth Orbit (LEO) satellites have gradually become an ideal source of opportunity signals due to their strong signal strength and diverse constellation layout; compared with geosynchronous orbit (GEO) or medium Earth orbit (MEO) satellites, LEO satellites provide wider signal coverage and higher positioning accuracy.

[0004] The existing technologies currently have the problem that the long-term coverage and visibility of low-Earth orbit satellites restrict navigation technology. Single-constellation LEO satellites have limitations in signal coverage and observable time. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an adaptive multi-constellation software-defined low-orbit satellite receiver and a design method thereof. Compared with single-constellation positioning, the present invention greatly improves the accuracy of the positioning results of fixed receivers. Through the combination of multiple constellations and the measurement switching mechanism, the signal limitation problem of traditional navigation systems in complex environments is overcome, and it is a reliable alternative to GNSS in restricted environments.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] In a first aspect, a design method for an adaptive multi-constellation software-defined low-orbit satellite receiver includes:

[0008] Get the downlink signal from LEO satellite;

[0009] The Welch power spectral density (PSD) analysis method is used to detect the downlink signal of the LEO satellite and obtain the detected satellite signal;

[0010] The phase-locked loop (PLL) algorithm is used to track the Doppler frequency shift of the detected satellite signal.

[0011] Selecting a Doppler measurement value using a measurement switching filter based on the Doppler frequency shift;

[0012] The selected Doppler measurements are input into the EKF positioning system for positioning calculation.

[0013] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver described in the present invention, the method of obtaining the downlink signal of the LEO satellite is as follows:

[0014] Set up multiple LEO satellite constellations to work together, predict the visible time of each LEO satellite based on the orbital parameters and current position of the LEO satellites in each LEO satellite constellation, and be ready to switch to the next visible LEO satellite when a LEO satellite is not visible;

[0015] The receiver acquires downlink signals from visible low earth orbit (LEO) satellites.

[0016] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver described in the present invention, the Welch power spectral density PSD analysis method is used to detect the downlink signal of the LEO satellite to obtain the detected satellite signal; the details are as follows:

[0017] The downlink signal of the LEO satellite passes through the low noise amplifier LNA, which amplifies the signal with the smallest signal-to-noise ratio (SNR) change. The amplified signal passes through the fourth power module to enhance the specific frequency components in the signal to obtain an enhanced signal. The enhanced signal enters the Welch power spectral density (PSD) analysis module to identify the satellite signal and determine its corresponding Doppler frequency.

[0018] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to the present invention, the specific process of detection is as follows:

[0019] Signal segmentation processing: The received LEO satellite downlink signal is divided into K segments, each segment includes M sampling points, and adjacent segments have partial overlap, and the number of overlapping points is S;

[0020] Power spectral density analysis: Apply the Hamming window function to each of the above segments to perform discrete Fourier transform DFT to calculate the segment spectrum FR m，k′ [v], calculated as:

[0021]

[0022] Among them, Rm [n] is the received signal of the mth constellation, w[n] is the Hamming window function, v represents the frequency, FR m，k′ [v] represents the spectrum of the k′th segment, n is the nth sampling point, e is the natural base, and j is the imaginary unit;

[0023] The power spectrum value of each segment is calculated according to the segmented spectrum, and the power spectrum of each segment is averaged to obtain the overall power spectrum density PSD;

[0024]

[0025]

[0026]

[0027] Among them, PP m，k′ [v] represents the power spectrum value of each segment, G is the normalized coefficient of the Hamming window, PSD m，k′ [v] represents the overall power spectral density, PSD m，k′ [v] Used to analyze the frequency components of the signal and identify the frequency peaks of the satellite signal;

[0028] Signal detection and threshold judgment: By setting a search window near the center frequency, the peak value of the overall power spectrum density PSD is detected to identify the satellite signal and determine its corresponding Doppler frequency.

[0029] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver described in the present invention, the Doppler frequency shift of the detected satellite signal is tracked by a tracking loop filter, and the tracking loop filter includes a coherent accumulator, a phase detector, a loop filter, a numerically controlled oscillator NCO and an integrator, wherein:

[0030] First, the input signal is mixed with a locally generated reference signal to obtain two signals: an I-channel signal and a Q-channel signal; wherein the input signal is a satellite signal, the I-channel signal is an in-phase component, and the Q-channel signal is an orthogonal component;

[0031] A coherent accumulator is used to accumulate the I-channel and Q-channel signals through the coherent accumulator, and output the accumulated I-channel and Q-channel signals to the phase detector;

[0032] The phase detector is used to calculate the phase error of the current received signal through the I and Q signals, and adjust the locally generated reference signal according to the phase error. The output of the phase detector is the phase error signal Δφ, which is output to the loop filter. The calculation formula of Δφ is as follows:

[0033]

[0034] Wherein, Q is the correlation value of Q-path signal, and I is the correlation value of I-path signal;

[0035] The loop filter is used to smooth the Δφ output by the phase detector and output the smoothed frequency correction value, which is used to adjust the frequency of the digital control oscillator;

[0036] The numerically controlled oscillator (NCO) is used to dynamically adjust the frequency of the local reference signal according to the frequency correction amount output by the loop filter to maintain phase synchronization with the input satellite signal. The local reference signal generated by the NCO is locked with the received signal, so that the receiver can stably track the changes in the Doppler frequency shift. The NCO tracks the frequency offset of the satellite signal in real time by adjusting the output frequency, ensuring that the system can accurately reflect the relative speed between the satellite and the receiver.

[0037] The integrator is used to accumulate the phase changes generated by the NCO and add the frequency change information into Doppler frequency shift data output; the output of the integrator is the Doppler frequency shift estimate at the current moment.

[0038] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver described in the present invention, a Doppler measurement value is selected by using a measurement switching filter according to the Doppler frequency shift; specifically as follows:

[0039] According to the number of visible satellites and signal type, the measurement switching filter automatically selects or generates the measurement matrix H(k+1). The measurement matrix H(k+1) is defined as:

[0040] H(k+1)=[h(k+1) c·I n×1 -c·I n×n ].

[0041] Where k is the time step, h(k+1) is the measurement vector of each satellite, c is the speed of light, and I n×1 is an n*1-dimensional unit vector, I n×n is the n*n dimensional identity matrix;

[0042] For the measurement vector h of the nth satellite n (k+1) is expressed as:

[0043]

[0044] Among them, p r is the receiver position, p t，n (k+1) and are the position and velocity of the nth satellite respectively, ensuring that the measurement matrix H(k+1) adapts to the current satellite configuration.

[0045] As a further optimization scheme of the design method of an adaptive multi-constellation software-defined low-orbit satellite receiver described in the present invention, the selected Doppler measurement value is input into the EKF positioning system for positioning calculation, as follows:

[0046] Defines the calculation of pseudorange rate measurement values. Based on the selected Doppler measurement value, the EKF positioning system calculates the pseudorange rate z n (k), the relationship between pseudorange rate and Doppler frequency is as follows:

[0047]

[0048] Among them, f d，n (k) is the Doppler frequency of the nth satellite, f c，n is the satellite's downlink center frequency, c is the speed of light;

[0049] Then, the pseudorange rate z of the nth satellite n (k) is defined as:

[0050]

[0051] Among them, p r is the receiver position, p t，n (k+1) is the position of the nth satellite, and are the clock drifts of the receiver and the satellite respectively. Assume that the receiver is stationary and the clock drifts of the LEO satellite and the receiver do not change with time. The pseudorange rate z n (k) is the measurement value of the EKF, which is used to update the position state of the receiver. The superscript T represents the transposition; p t，n (k) and are the time step k, the position and velocity of the nth satellite, ε t，n (k) is the noise term;

[0052] Define the state vector x(k) and the measurement vector z(k) as follows:

[0053]

[0054] z(k)=[z 1 (k) ... z n1 (k)] T

[0055] Where n is an integer greater than 1 and less than or equal to n1, z n (k) is the measurement vector, p r T It represents the target calculation coordinates;

[0056] The measurement matrix h(k+1) is used to connect the state space with the measurement space and contains the measurement information of each satellite. It is constructed as follows:

[0057] h(k+1)=[h 1 T (k+1)...h n1 T (k+1)] T

[0058]

[0059]

[0060]

[0061] in, is a variable, are the speed of the nth satellite at time step k+1, respectively;

[0062] The final form of the Jacobian row matrix is:

[0063]

[0064] State transfer and error covariance update, the state transfer matrix is ​​an (n+4)-dimensional identity matrix; the state vector x of time step k k (k) and the covariance matrix P k The time update equation of (k) is as follows:

[0065] x k (k+1)=x k (k), P k (k+1)=P k (k)+Q′

[0066] Where Q′ is the process noise covariance, x k (k+1),P k (k+1) are the updated state vector and covariance matrix respectively;

[0067] EKF is based on the prediction error covariance matrix P k (k+1) and the measurement noise covariance matrix R(k+1) calculate the Kalman gain K(k+1):

[0068] K(k+1)=P k (k+1)H T (k+1)[H(k+1)P k (k+1)H T (k+1)+R(k+1)] -1

[0069] State update, using the selected Doppler measurement value to update the state, the update equation is:

[0070] x k+1 (k+1)=x k (k+1)+K(k+1)[z(k+1)-H(k+1)x k (k+1)]

[0071] Where z(k+1) is the pseudorange rate measurement vector of the current time step.

[0072] In a second aspect, an adaptive multi-constellation software-defined low-orbit satellite receiver includes:

[0073] A signal receiving module is used to obtain the downlink signal of the LEO satellite;

[0074] The detection module includes a low noise amplifier LNA, a quartic module and a Welch power spectrum density PSD analysis module, wherein the downlink signal of the LEO satellite passes through the low noise amplifier LNA, the LNA amplifies the signal with the minimum signal-to-noise ratio SNR change, the amplified signal passes through the quartic module to enhance the specific frequency components in the signal to obtain an enhanced signal, and the enhanced signal enters the Welch power spectrum density PSD analysis module to identify the satellite signal and determine its corresponding Doppler frequency;

[0075] A tracking loop filter, used for tracking the Doppler frequency shift of the detected satellite signal using a phase-locked loop PLL algorithm;

[0076] A measurement switching filter is used to select Doppler measurement values ​​to be output to the EKF positioning system according to the Doppler frequency shift;

[0077] The EKF positioning system is used to calculate the positioning of the selected Doppler measurement values.

[0078] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver as described above when executing the computer program.

[0079] In a fourth aspect, a computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver as described above.

[0080] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0081] (1) The present invention designs a multi-constellation, adaptive receiver structure so that the system can be compatible with different types of LEO satellite signals;

[0082] (2) Using Doppler frequency shift as the main positioning parameter to effectively cope with GNSS restricted environments;

[0083] (3) The measurement switching filter and EKF algorithm are used to enable the system to perform measurement switching and dynamic adjustment according to the real-time satellite status, ensuring the positioning accuracy and real-time performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 This is a system structure diagram of the present invention.

[0085] Figure 2 It is a multi-constellation low-orbit satellite receiver system.

[0086] Figure 3 This is the working structure diagram of the phase-locked loop. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0088] The present invention designs a receiver capable of receiving multiple constellation signals (such as Orbcomm and Iridium-Next), which can significantly increase satellite visibility time, improve positioning accuracy and solve signal intermittent problems.

[0089] like Figure 1 As shown, a design method for an adaptive multi-constellation software-defined low-orbit satellite receiver includes the following steps:

[0090] Step 1, obtaining the downlink signal of the LEO satellite;

[0091] Step 2, using the Welch power spectral density (PSD) analysis method to detect the acquisition part of the satellite signal;

[0092] Step 3, performing Doppler frequency shift tracking on the detected LEO satellite through a phase locked loop (PLL) algorithm;

[0093] Step 4, using the measurement switching filter to select an appropriate Doppler measurement value;

[0094] Step 5: Input the selected Doppler measurement value into the EKF positioning system for positioning calculation.

[0095] The receiver is designed to receive downlink signals from different low earth orbit (LEO) satellites and can work with multiple LEO satellite constellations, including but not limited to Orbcomm, Iridium-Next, and Globalstar, ensuring that the receiver has available satellite signals most of the time. Positioning using LEO satellite signals depends on signal coverage and visibility duration; due to its orbital characteristics, each LEO satellite can only be seen by the receiver during a specific period of time, so when the satellite is not visible to the receiver, the receiver must switch to another satellite in another constellation or wait for the same satellite to come into view again; in the design of a multi-constellation software-defined receiver, this switching can be automatically completed through software control to ensure continuous signal reception and positioning capabilities; the receiver predicts the visible time of each satellite based on the orbital parameters and current position of each satellite, and is ready to switch to the next available satellite signal when the satellite is not visible; the above process improves the robustness of the receiver and allows continuous positioning and navigation in a dynamic environment.

[0096] In steps 1 and 2, each constellation has its own dedicated antenna; the received satellite signal is usually very weak and first passes through a low noise amplifier (LNA), which amplifies the signal with minimal signal-to-noise ratio (SNR) change; the amplified signal passes through a fourth power module to enhance specific frequency components in the signal; the signal enters the Welch power spectral density (PSD) analysis module to extract the spectral characteristics of different satellite signals from the received composite signal. The specific detection process is as follows:

[0097] Signal segmentation processing: The received satellite signal is divided into K segments, each segment contains M sampling points, and adjacent segments have partial overlap (the number of overlapping points is S); this segmentation processing method can improve the stability of signal detection while maintaining signal integrity;

[0098] Power spectral density analysis: Apply the Hamming window function to each of the above segments to perform discrete Fourier transform DFT to calculate the segment spectrum FR m，k′ [v], calculated as:

[0099]

[0100] Among them, R m [n] is the received signal of the mth constellation, w[n] is the Hamming window function, v represents the frequency, FR m，k′ [v] represents the spectrum of the k′th segment, n is the nth sampling point, e is the natural base, and j is the imaginary unit;

[0101] The power spectrum value of each segment is calculated according to the segmented spectrum, and the power spectrum of each segment is averaged to obtain the overall power spectrum density PSD;

[0102]

[0103]

[0104]

[0105] Among them, PP m，k′ [v] represents the power spectrum value of each segment, G is the normalized coefficient of the Hamming window, PSD m，k′ [v] represents the overall power spectral density, PSD m，k′ [v] Used to analyze the frequency components of the signal and identify the frequency peaks of the satellite signal;

[0106] Signal detection and threshold judgment: By setting a search window of ±1kHz near the center frequency, the power spectrum density (PSD) graph is peak-detected to identify satellite signals and determine their corresponding Doppler frequencies. For constellations that use burst signals (such as Iridium), the system will detect and track the center frequency of each burst to ensure that Doppler shift changes are captured when the signal is intermittently transmitted; if the detected peak PSD exceeds the preset threshold (e.g. -40dB), the signal corresponding to the frequency is determined to be a valid satellite signal.

[0107] After the satellite signal is detected in step 3, the phase-locked loop (PLL) algorithm is used to track the Doppler shift in the signal to ensure accurate frequency offset data is obtained in real time. In order to stably extract the Doppler shift during all sampling times, the system is processed by a tracking loop filter, which is mainly composed of a coherent accumulator, a phase detector, a loop filter, a numerically controlled oscillator (NCO) and an integrator, where:

[0108] First, the input signal is mixed (multiplied) with a locally generated reference signal to obtain two signals: I (in-phase component) and Q (quadrature component).

[0109] Coherent accumulator: I and Q signals are accumulated through the coherent accumulator to improve the signal-to-noise ratio (SNR) of the signal, enhance the useful signal by accumulating the signal energy within a certain time window, and remove some noise and fast-changing interference;

[0110] Phase detector, the accumulated I and Q signals are input to the phase detector, and the phase error of the current received signal is calculated through the I and Q signals. The phase detector adjusts the locally generated reference signal (through the loop filter) according to the phase error, so that the system can continuously track the phase and frequency of the input signal. Specifically, the output of the phase detector is the phase error signal Δφ, which is calculated as follows:

[0111]

[0112] The phase error Δφ is passed to the loop filter for processing;

[0113] The loop filter smoothes the phase error signal output by the phase detector, filters out rapidly changing phase errors, and prevents the system from making excessive frequency adjustments due to transient interference. The loop filter outputs a smoothed frequency correction value, which is used to adjust the frequency of the digital control oscillator; the filtered control signal ensures that the system can stably track changes in Doppler frequency shift;

[0114] Numerically controlled oscillator (NCO), the NCO dynamically adjusts the frequency of the local reference signal according to the frequency correction amount output by the loop filter to maintain phase synchronization with the incoming satellite signal; the local reference signal generated by the NCO is locked to the received signal, allowing the receiver to stably track changes in Doppler frequency shift; the NCO tracks the frequency offset of the satellite signal in real time by adjusting the output frequency, ensuring that the system can accurately reflect the relative speed between the satellite and the receiver;

[0115] The integrator is used to accumulate the phase changes generated by the NCO and add the frequency change information into the Doppler frequency shift data output; the output of the integrator is the Doppler frequency shift estimate at the current moment, providing high-precision speed information for the subsequent extended Kalman filter (EKF) positioning calculation.

[0116] In step 4, a measurement switching filter is designed to select appropriate Doppler measurements for the positioning system. The measurement switching filter first evaluates the quality of the available satellite signals; based on the evaluation results, a certain decision logic is applied to select the best signal; after determining the best signal, the filter switches from the current signal to that signal and inputs it into the EKF for positioning calculations. The measurement switching filter improves the accuracy and reliability of Doppler positioning by dynamically selecting and switching the best signal source; through real-time monitoring and adjustment, the measurement switching filter ensures that the system can work stably and efficiently in a complex satellite signal environment. The specific process is as follows:

[0117] Satellite signal type identification, the measurement switching filter first analyzes the detected satellite signal to distinguish the signal type. In the low-orbit satellite (LEO) signal, the Orbcomm constellation is generally a continuous downlink signal, while the Iridium-Next constellation is usually a pulsed downlink signal. The Doppler measurement value of the continuous signal can be obtained continuously, while the Doppler frequency shift measurement value of the pulse signal is obtained within each pulse period; therefore, for the pulse signal, the measurement switching filter will interpolate the interval measurement value to generate a continuous measurement input for the positioning algorithm, and use the same sampling frequency to synchronize the measurement data of the Orbcomm constellation to achieve more accurate positioning;

[0118] Satellite visibility detection and measurement switching. As satellites move and signal conditions change, the measurement switching filter updates its selection in real time to ensure that the best signal is always used;

[0119] Dynamically adjust the measurement matrix. According to the number of visible satellites and signal type, the measurement switching filter automatically selects or generates a suitable measurement matrix H(k+1). In the EKF framework, the measurement matrix H(k+1) is the key to state update. It is defined as:

[0120] H(k+1)=[h(k+1) c·I n×1 -c·I n×n ].

[0121] Where k is the time step, h(k+1) is the measurement vector of each satellite, c is the speed of light, and I n×1 is an n*1-dimensional unit vector, I n×n is the n*n dimensional identity matrix;

[0122] For the measurement vector h of the nth satellite n (k+1) is expressed as:

[0123]

[0124] Among them, p r is the receiver position, p t，n (k+1) and are the position and velocity of the nth satellite, respectively, to ensure that the measurement matrix H(k+1) is adapted to the current satellite configuration;

[0125] Select appropriate Doppler measurement values. For each time step k, the system selects the optimal Doppler measurement value as input based on the judgment of the measurement switching filter. This measurement value includes the Doppler frequency shift data of all visible satellites. After the filter selection and interpolation processing, it ensures that the input value meets the continuity requirements of the EKF, thereby maintaining positioning accuracy;

[0126] In step 5, the extended Kalman filter (EKF) is used for Doppler positioning, which is the core step of the entire positioning system. The extended Kalman filter (EKF) uses the Doppler frequency shift as the measurement input, combines the dynamic system model and the measurement model, and estimates the state of the receiver, thereby achieving a high-precision estimation of the receiver position. The process can be divided into the following key steps:

[0127] Define the pseudorange rate measurement calculation. Based on the selected Doppler measurement, EKF calculates the pseudorange rate. The relationship between the pseudorange rate and the Doppler frequency is as follows:

[0128] Defines the calculation of pseudorange rate measurement values. Based on the selected Doppler measurement value, the EKF positioning system calculates the pseudorange rate z n (k), the relationship between pseudorange rate and Doppler frequency is as follows:

[0129]

[0130] Among them, f d，n (k) is the Doppler frequency of the nth satellite, f c，n is the satellite's downlink center frequency, c is the speed of light;

[0131] Then, the pseudorange rate z of the nth satellite n (k) is defined as:

[0132]

[0133] Among them, p r is the receiver position, p t，n (k+1) is the position of the nth satellite, and are the clock drifts of the receiver and the satellite respectively. Assume that the receiver is stationary and the clock drifts of the LEO satellite and the receiver do not change with time. The pseudorange rate z n (k) is the measurement value of the EKF, which is used to update the position state of the receiver. The superscript T represents the transposition; p t，n (k) and are the time step k, the position and velocity of the nth satellite, ε t，n (k) is the noise term;

[0134] Define the state vector x(k) and the measurement vector z(k) as follows:

[0135]

[0136] z(k)=[z 1 (k) ... z n1 (k)] T

[0137] Where n is an integer greater than 1 and less than or equal to n1, z n (k) is the measurement vector, p r T It represents the target calculation coordinates;

[0138] The measurement matrix h(k+1) is used to connect the state space with the measurement space and contains the measurement information of each satellite. It is constructed as follows:

[0139] h(k+1)=[h 1 T (k+1) ... h n1 T (k+1)] T

[0140]

[0141]

[0142]

[0143] in, is a variable, are the speed of the nth satellite at time step k+1, respectively;

[0144] The final form of the Jacobian row matrix is:

[0145]

[0146] State transfer and error covariance update, the state transfer matrix is ​​an (n+4)-dimensional identity matrix; the state vector x of time step k k (k) and the covariance matrix P k The time update equation of (k) is as follows:

[0147] x k (k+1)=x k (k), P k (k+1)=P k (k)+Q′

[0148] Where Q′ is the process noise covariance, x k (k+1),P k (k+1) are the updated state vector and covariance matrix respectively;

[0149] EKF is based on the prediction error covariance matrix P k (k+1) and the measurement noise covariance matrix R(k+1) calculate the Kalman gain K(k+1):

[0150] K(k+1)=P k(k+1)H T (k+1)[H(k+1)P k (k+1)H T (k+1)+R(k+1)] -1

[0151] State update, using the selected Doppler measurement value to update the state, the update equation is:

[0152] x k+1 (k+1)=x k (k+1)+K(k+1)[z(k+1)-H(k+1)x k (k+1)]

[0153] Where z(k+1) is the pseudorange rate measurement vector of the current time step.

[0154] The core idea of ​​the present invention is to use a multi-constellation combination and a measurement switching mechanism, dynamically adjust the measurement matrix and use the EKF algorithm to overcome the signal limitation problem of the traditional navigation system in a complex environment, and improve the positioning accuracy and system robustness. The specific implementation method is as follows:

[0155] (1) Doppler positioning method

[0156] See also Figure 2 The satellite signal received by the antenna is usually very weak. It first passes through a low noise amplifier (LNA), which amplifies the signal with minimal signal-to-noise ratio (SNR) change; the amplified signal passes through a fourth power module to enhance specific frequency components in the signal; the signal enters the Welch power spectral density (PSD) analysis module; based on the Welch PSD, it enters the satellite monitoring module, which uses a predetermined algorithm to identify the presence of satellite signals and extract useful data; after the satellite signal is detected, it will be frequency-down converted into a baseband signal or a lower frequency intermediate frequency signal; the down-converted signal will pass through a low-pass filter to remove high-frequency noise and unnecessary frequency components and retain useful low-frequency signals.

[0157] (2) Doppler tracking system

[0158] See also Figure 3 The input signal is mixed (multiplied) with the locally generated reference signal to obtain two signals, I and Q. The two signals are accumulated through a coherent accumulator. The accumulated signal is input into a phase detector, and the phase error calculated by the phase detector is used to adjust the frequency of the numerically controlled oscillator. The NCO tracks the frequency offset of the satellite signal in real time by adjusting the output frequency.

[0159] (3) EKF positioning algorithm

[0160] See also Figure 1 Each constellation has multiple satellite signals, and the measurement results of each satellite correspond to the frequency estimation of different channels; the measurement results of all constellations received will be passed to the "EKF mode switching" module, the system adapts to the signals of different constellations, and dynamically adjusts the measurement matrix and state estimation model; EKF processing is performed, and the extended Kalman filter is used to combine the received Doppler frequency data with the measurement matrix to gradually estimate the user's state; finally, the state estimation output is performed to complete the multi-constellation Doppler positioning process.

[0161] The principle of the invention is: using a multi-constellation software defined receiver (MC-SDR) to capture and process downlink signals from different LEO satellite constellations, performing signal detection through Welch PSD analysis, using a PLL algorithm to track Doppler frequency shift, and combining an EKF algorithm to estimate and update the position and velocity of the receiver in real time, thereby achieving high-precision Doppler positioning, and dynamically adjusting the measurement matrix to adapt to the visibility of different satellites, thereby improving the accuracy of positioning and the robustness of the system.

[0162] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the above-mentioned method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver when executing the computer program.

[0163] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the design method of the adaptive multi-constellation software-defined low-orbit satellite receiver as mentioned above.

[0164] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0165] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0168] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0169] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A design method for an adaptive multi-constellation software-defined low-orbit satellite receiver, characterized in that: include: Get the downlink signal from LEO satellite; The Welch power spectral density (PSD) analysis method is used to detect the downlink signal of the LEO satellite and obtain the detected satellite signal; The phase-locked loop (PLL) algorithm is used to track the Doppler frequency shift of the detected satellite signal. Selecting a Doppler measurement value using a measurement switching filter based on the Doppler frequency shift; The selected Doppler measurements are input into the EKF positioning system for positioning calculation.

2. The design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 1, characterized in that: The method of obtaining the downlink signal of the LEO satellite is as follows: Set up multiple LEO satellite constellations to work together, predict the visible time of each LEO satellite based on the orbital parameters and current position of the LEO satellites in each LEO satellite constellation, and be ready to switch to the next visible LEO satellite when a LEO satellite is not visible; The receiver acquires downlink signals from visible low earth orbit (LEO) satellites.

3. The design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 1, characterized in that: The Welch power spectral density (PSD) analysis method is used to detect the downlink signal of the LEO satellite and obtain the detected satellite signal; the details are as follows: The downlink signal of the LEO satellite passes through the low noise amplifier LNA, which amplifies the signal with the smallest signal-to-noise ratio (SNR) change. The amplified signal passes through the fourth power module to enhance the specific frequency components in the signal to obtain an enhanced signal. The enhanced signal enters the Welch power spectral density (PSD) analysis module to identify the satellite signal and determine its corresponding Doppler frequency.

4. The design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 3, characterized in that: The specific process of the detection is as follows: Signal segmentation processing: The received LEO satellite downlink signal is divided into K segments, each segment includes M sampling points, and adjacent segments have partial overlap, and the number of overlapping points is S; Power spectral density analysis: Apply the Hamming window function to each of the above segments to perform discrete Fourier transform DFT to calculate the segment spectrum FR m,k′ [v], calculated as: Among them, R m [n] is the received signal of the mth constellation, w[n] is the Hamming window function, v represents the frequency, FR m,k′ [v] represents the spectrum of the k′th segment, n is the nth sampling point, e is the natural base, and j is the imaginary unit; The power spectrum value of each segment is calculated according to the segmented spectrum, and the power spectrum of each segment is averaged to obtain the overall power spectrum density PSD; Among them, PP m,k′ [v] represents the power spectrum value of each segment, G is the normalized coefficient of the Hamming window, PSD m,kv [v] represents the overall power spectral density, PSD m,k′ [v] Used to analyze the frequency components of the signal and identify the frequency peaks of the satellite signal; Signal detection and threshold judgment: By setting a search window near the center frequency, the peak value of the overall power spectrum density PSD is detected to identify the satellite signal and determine its corresponding Doppler frequency.

5. The design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 1, characterized in that: The Doppler frequency shift of the detected satellite signal is tracked by a tracking loop filter, which includes a coherent accumulator, a phase detector, a loop filter, a numerically controlled oscillator NCO and an integrator, wherein: First, the input signal is mixed with a locally generated reference signal to obtain two signals: an I-channel signal and a Q-channel signal; wherein the input signal is a satellite signal, the I-channel signal is an in-phase component, and the Q-channel signal is an orthogonal component; A coherent accumulator is used to accumulate the I-channel and Q-channel signals through the coherent accumulator, and output the accumulated I-channel and Q-channel signals to the phase detector; The phase detector is used to calculate the phase error of the current received signal through the I and Q signals, and adjust the locally generated reference signal according to the phase error. The output of the phase detector is the phase error signal Δφ, which is output to the loop filter. The calculation formula of Δφ is as follows: Wherein, Q is the correlation value of Q-path signal, and I is the correlation value of I-path signal; The loop filter is used to smooth the Δφ output by the phase detector and output the smoothed frequency correction value, which is used to adjust the frequency of the digital control oscillator; The numerically controlled oscillator (NCO) is used to dynamically adjust the frequency of the local reference signal according to the frequency correction amount output by the loop filter to maintain phase synchronization with the input satellite signal. The local reference signal generated by the NCO is locked with the received signal, so that the receiver can stably track the changes in the Doppler frequency shift. The NCO tracks the frequency offset of the satellite signal in real time by adjusting the output frequency, ensuring that the system can accurately reflect the relative speed between the satellite and the receiver. The integrator is used to accumulate the phase changes generated by the NCO and add the frequency change information into Doppler frequency shift data output; the output of the integrator is the Doppler frequency shift estimate at the current moment.

6. The design method of an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 1, characterized in that: Based on the Doppler shift, the Doppler measurement value is selected using the measurement switching filter; specifically as follows: According to the number of visible satellites and signal type, the measurement switching filter automatically selects or generates the measurement matrix H(k+1). The measurement matrix H(k+1) is defined as: H(k+1)=[h(k+1)c·I n×1 -c·I n×n ]. Where k is the time step, h(k+1) is the measurement vector of each satellite, c is the speed of light, and I n×1 is an n*1-dimensional unit vector, I n×n is the n*n dimensional identity matrix; For the measurement vector h of the nth satellite n (k+1) is expressed as: Among them, p r is the receiver position, p t,n (k+1) and are the position and velocity of the nth satellite respectively, ensuring that the measurement matrix H(k+1) adapts to the current satellite configuration.

7. The method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver according to claim 6, characterized in that: The selected Doppler measurement value is input into the EKF positioning system for positioning calculation, as follows: Defines the calculation of pseudorange rate measurement values. Based on the selected Doppler measurement value, the EKF positioning system calculates the pseudorange rate z n (k), the relationship between pseudorange rate and Doppler frequency is as follows: Among them, f d,n (k) is the Doppler frequency of the nth satellite, f c,n is the satellite's downlink center frequency, c is the speed of light; Then, the pseudorange rate z of the nth satellite n (k) is defined as: Among them, p r is the receiver position, p t,n (k+1) is the position of the nth satellite, and are the clock drifts of the receiver and the satellite respectively. Assume that the receiver is stationary and the clock drifts of the LEO satellite and the receiver do not change with time. The pseudorange rate z n (k) is the measurement value of the EKF, which is used to update the position state of the receiver. The superscript T represents the transposition; p t,n (k) and are the time step k, the position and velocity of the nth satellite, ε t,n (k) is the noise term; Define the state vector x(k) and the measurement vector z(k) as follows: z(k)=[z1(k) … z n1 (k)] T Where n is an integer greater than 1 and less than or equal to n1, z n (k) is the measurement vector, p r T It represents the target calculation coordinates; The measurement matrix h(k+1) is used to connect the state space with the measurement space and contains the measurement information of each satellite. It is constructed as follows: h(k+1)=[h1 T (k+1) ... h n1 T (k+1)] T in, is a variable, are the speed of the nth satellite at time step k+1, respectively; The final form of the Jacobian row matrix is: State transfer and error covariance update, the state transfer matrix is ​​a (n+4)-dimensional identity matrix; the state vector x of time step k k (k) and the covariance matrix P k The time update equation of (k) is as follows: x k (k+1)=x k (k),P k (k+1)=P k (k)+Q' Where Q' is the process noise covariance, x k (k+1),P k (k+1) are the updated state vector and covariance matrix respectively; EKF is based on the prediction error covariance matrix P k (k+1) and the measurement noise covariance matrix R(k+1) calculate the Kalman gain K(k+1): K(k+1)=P k (k+1)H T (k+1)[H(k+1)P k (k+1)H T (k+1)+R(k+1)] -1 State update, using the selected Doppler measurement value to update the state, the update equation is: x k+1 (k+1)=x k (k+1)+K(k+1)[z(k+1)-H(k+1)x k (k+1)] Where z(k+1) is the pseudorange rate measurement vector of the current time step.

8. An adaptive multi-constellation software-defined low-orbit satellite receiver, characterized in that: include: A signal receiving module is used to obtain the downlink signal of the LEO satellite; The detection module includes a low noise amplifier LNA, a quartic module and a Welch power spectrum density PSD analysis module, wherein the downlink signal of the LEO satellite passes through the low noise amplifier LNA, the LNA amplifies the signal with the minimum signal-to-noise ratio SNR change, the amplified signal passes through the quartic module to enhance the specific frequency components in the signal to obtain an enhanced signal, and the enhanced signal enters the Welch power spectrum density PSD analysis module to identify the satellite signal and determine its corresponding Doppler frequency; A tracking loop filter, used for tracking the Doppler frequency shift of the detected satellite signal using a phase-locked loop PLL algorithm; A measurement switching filter is used to select Doppler measurement values ​​to be output to the EKF positioning system according to the Doppler frequency shift; The EKF positioning system is used to calculate the positioning of the selected Doppler measurement values.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for designing an adaptive multi-constellation software-defined low-orbit satellite receiver as described in any one of claims 1 to 7 are implemented.

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