Carrier rolling attitude estimation method, device and equipment based on navigation satellite signal energy

Abstract

The invention relates to a carrier rolling attitude estimation method, device and equipment based on navigation satellite signal energy, and the method comprises the steps: extracting a signal energy observation value from real-time navigation satellite signals sent by a plurality of satellites, carrying out the framing processing of the signal energy observation value, and obtaining a plurality of frames of signal energy observation values, the method comprises the following steps: acquiring the energy observation value of each frame of signal, normalizing the energy observation value of each frame of signal, calculating the weight of the energy observation value of each frame of signal according to the average amplitude of the energy observation value of each frame of signal after normalization, constructing an extended Kalman filtering model, and aiming at the energy observation value of each frame of signal, calculating the weight of the energy observation value of each frame of signal. And carrying out dynamic weighting adjustment on a measurement error covariance matrix in the extended Kalman filtering model by adopting a corresponding weight, and carrying out filtering iteration on the normalized corresponding frame signal energy observation value by utilizing the adjusted extended Kalman filtering model to obtain a real-time estimation result of the rolling attitude of the carrier. By adopting the method, the precision of estimating the rolling attitude of the carrier can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of satellite navigation signal processing, and in particular to a method, device and equipment for estimating a carrier roll attitude based on navigation satellite signal energy. Background Art

[0002] The global navigation satellite system is widely used in the fields of positioning and attitude measurement of vehicles. Compared with the attitude measurement methods of inertial devices and geomagnetic sensors, the global navigation satellite system has the advantages of all-weather, high precision, and low susceptibility to interference. The characteristics of navigation satellite signals have important applications in the measurement of the rolling attitude of vehicles.

[0003] However, when a single non-omnidirectional antenna is used to receive navigation satellite signals, the problem of the carrier blocking the antenna and the loop losing lock during the spin process may cause the signal energy observation noise to change. Under this premise, the attitude measurement method based on the navigation satellite signal energy needs to be further improved. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device and equipment for carrier roll attitude estimation based on navigation satellite signal energy, which can improve the estimation accuracy, in order to address the above technical problems.

[0005] A method for estimating a carrier roll attitude based on navigation satellite signal energy, the method comprising:

[0006] Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0007] The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized;

[0008] Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude;

[0009] The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model.

[0010] For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

[0011] In one embodiment, the signal energy observation value is extracted using an I / Q branch of a carrier loop.

[0012] In one embodiment, when the signal energy observation value is subjected to frame processing:

[0013] Calculating according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain the number of sampling points of a single frame of the signal;

[0014] The signal energy observation value is divided into multiple frames of signal energy observation values ​​according to the number of signal sampling points.

[0015] In one embodiment, the average amplitude of the energy observation values ​​of each frame signal after normalization is calculated using the following formula:

[0016]

[0017] In the above formula, i represents the frame number, n represents the satellite number, represents the mean energy value of the signal in the i-th frame, L represents the frame length of a frame of signal energy observation value, y norm (t) represents the normalized signal energy observation value.

[0018] In one embodiment, when calculating the weight of each frame signal energy observation value according to the average amplitude:

[0019] According to two preset coefficients and the average amplitude, the corresponding signal energy observation value is divided into a normal measurement interval, a suspicious measurement interval or an eliminated measurement interval;

[0020] The weights of the signal energy observation values ​​divided into the normal measurement interval and the eliminated measurement interval are assigned to 1 and 0, and the weights of the signal energy observation values ​​divided into the suspicious measurement interval are calculated according to two preset coefficients, the preset reduction coefficient and the average amplitude.

[0021] In one embodiment, the following formula is used to calculate the signal energy observation value weight of the suspicious measurement interval:

[0022]

[0023] In the above formula, represents the signal energy observation weights of all epochs of satellite n in the i-th frame, k0 and k1 represent preset coefficients, and k2 represents the reduced weight coefficient.

[0024] In one embodiment, a three-stage weight function is used to calculate the weight of each frame signal energy observation value according to the average amplitude, and the three-stage weight function is expressed as:

[0025]

[0026] In the above formula, It represents the signal energy observation weight of all epochs of satellite n in the i-th frame. The coefficients k0 and k1 determine the interval division of normal measurements, suspicious measurements and eliminated measurements in the measurements. k2 represents the weight reduction coefficient of the suspicious measurement interval.

[0027] In one embodiment, when the corresponding weights are used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model, it is expressed as:

[0028]

[0029] In the above formula, represents the measurement error covariance matrix after dynamic weight adjustment, R k represents the measurement error covariance matrix before adjustment, and W represents the weight matrix composed of the weights of multiple satellites in the corresponding frames.

[0030] The present application also provides a carrier roll attitude estimation device based on navigation satellite signal energy, the device comprising:

[0031] A signal energy observation value extraction module, used to obtain real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0032] A data preprocessing module, used to perform frame processing on the signal energy observation value according to the sampling rate of the signal energy observation value and the spin frequency of the carrier, obtain multiple frames of signal energy observation values, and normalize the signal energy observation values ​​of each frame;

[0033] Each frame weight assignment module is used to calculate the average amplitude of the signal energy observation values ​​of each frame after normalization processing, and calculate the weight of the signal energy observation value of each frame according to the average amplitude;

[0034] An extended Kalman filter model building module is used to take the real-time roll angle and roll frequency of the carrier to be estimated as state variables and the function of the satellite signal gain received by a single antenna with respect to the roll angle as a measurement equation to build an extended Kalman filter model;

[0035] The roll attitude real-time estimation module is used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model with corresponding weights for each frame signal energy observation value, and use the adjusted extended Kalman filter model to iteratively filter the normalized corresponding frame signal energy observation value to obtain the real-time estimation result of the carrier's roll attitude.

[0036] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0038] The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized;

[0039] Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude;

[0040] The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model.

[0041] For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0043] Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0044] The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized;

[0045] Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude;

[0046] The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model.

[0047] For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

[0048] The above-mentioned carrier roll attitude estimation method, device and equipment based on navigation satellite signal energy extract signal energy observation values ​​from real-time navigation satellite signals sent by multiple satellites, perform frame processing on the signal energy observation values, obtain multiple frames of signal energy observation values, and normalize the signal energy observation values ​​of each frame. The weight of each frame of signal energy observation value is calculated according to the average amplitude of each frame of signal energy observation value after normalization. At the same time, the real-time roll angle and roll frequency of the carrier to be estimated are used as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is used as the measurement equation to construct an extended Kalman filter model. For each frame of signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model. The adjusted extended Kalman filter model is used to filter and iterate the corresponding frame signal energy observation value after normalization to obtain the real-time estimation result of the carrier's roll attitude. The method can effectively improve the accuracy of carrier roll attitude estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 1 is a flow chart of a method for estimating a carrier roll attitude based on navigation satellite signal energy in one embodiment;

[0050] Figure 2 Schematic diagram of gain direction of a rectangular microstrip antenna in one embodiment, wherein: Figure 2 (a) represents the E-plane directional diagram, Figure 2 (b) represents the H-plane directional diagram;

[0051] Figure 3 A schematic diagram of the correlation between the received signal energy and the carrier roll angle in one embodiment;

[0052] Figure 4 This is a schematic diagram of the relationship between the energy mean of a single frame signal and the observation error in an experiment;

[0053] Figure 5 This is a schematic diagram of the test results of the energy roll angle of a single satellite signal at 10r / s in an experiment;

[0054] Figure 6 This is a schematic diagram of the comparison of roll attitude estimation errors at 10r / s in an experiment, where: Figure 6 (a) is a schematic diagram showing the comparison of roll angle estimation errors at 10 r / s. Figure 6(b) is a schematic diagram showing the comparison of roll frequency estimation errors at 10 r / s;

[0055] Figure 7 is a structural block diagram of a carrier roll attitude estimation device based on navigation satellite signal energy in one embodiment;

[0056] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] When the non-omnidirectional antenna rotates with the carrier, the direction of the incident signal also changes with the rotation, and the gain of the received signal will change accordingly. Its change characteristics can be used to extract the carrier roll information. In the existing roll angle estimation method based on signal energy, a phase-locked loop or Kalman filter is mainly used to track the change law of signal energy to solve the carrier roll attitude. Due to the influence of noise in the propagation process, the observation error used for rolling attitude solution cannot be ignored. If this error is used as part of the input signal of the phase-locked loop, it will directly affect the phase discrimination result of the phase-locked loop. The method of averaging after solving the roll attitude of multi-satellite observations is usually used to reduce the influence of Gaussian noise in the observation. However, when the multi-satellite observations are obtained using independent channels and there is a random non-continuous reception problem, the accuracy of the roll attitude estimation result after multi-satellite averaging will deteriorate. In the carrier roll attitude measurement method using Kalman filtering and its improved method, although the influence of the observation error can be reduced to a certain extent, its estimation accuracy is still limited by the observation error level. The changes in the statistical characteristics of the observation error caused by the discontinuous output of the observation quantity will also directly affect the estimation results of the Kalman filter.

[0059] In view of the above problems, in this application, Figure 1 As shown, the provided method for estimating the carrier rolling attitude based on navigation satellite signal energy specifically includes the following steps:

[0060] Step S100: acquiring real-time navigation satellite signals sent by a plurality of satellites, and extracting signal energy observation values ​​based on the real-time navigation satellite signals.

[0061] Step S110 , performing frame processing on the signal energy observation value according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation values, and performing normalization processing on the signal energy observation values ​​of each frame.

[0062] Step S120, calculating the average amplitude of the normalized signal energy observation values ​​of each frame, and calculating the weight of the signal energy observation value of each frame according to the average amplitude.

[0063] Step S130, taking the real-time roll angle and roll frequency of the carrier to be estimated as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle as a measurement equation, an extended Kalman filter model is constructed.

[0064] Step S140, for each frame signal energy observation value, the corresponding weight is used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iterate the filtering of the normalized corresponding frame signal energy observation value to obtain the real-time estimation result of the carrier's roll attitude.

[0065] In step S100, a satellite navigation receiver disposed on a rotating carrier simultaneously receives satellite navigation signals transmitted by multiple satellites through a single antenna and multiple independent channels.

[0066] In this embodiment, the signal energy observation value is extracted using the I / Q branch of the carrier loop.

[0067] Specifically, if the satellite navigation receiver carrier tracking loop input signal is the signal S after intermediate frequency sampling IF (t), then:

[0068]

[0069] In formula (1), P R represents the received signal power, C(t) represents the C / A code, and its value is ±1, τ represents the time delay caused by the transmission process, D(t) represents the navigation message bit, and its value is ±1, the bit rate is 50bps, ω IF represents the intermediate frequency carrier frequency, whose value is determined by the RF circuit and the Doppler frequency, φ(t) represents the initial carrier phase, n(t) represents the Gaussian noise in the transmission process, whose mean is usually considered to be 0, and the power spectrum density is usually considered to be a constant N0 / 2.

[0070] The local carrier generator will generate two orthogonal signals, which are expressed as:

[0071]

[0072] In formula (2) and formula (3), the local carrier frequency is in, is the frequency difference between the input signal carrier and the local carrier. Make a preliminary estimate. Indicates the initial phase of the local carrier. If the receiver enters stable tracking, should be close to 0, Keep the difference with φ(t) small.

[0073] Furthermore, in S IF After (t) is input into the carrier loop, it is first multiplied by two orthogonal local carriers. The results can be expressed as:

[0074]

[0075] After that, the two branch signals will be processed by the integrator, which is equivalent to passing through a low-pass filter. Therefore, after the high-frequency components in the signal are filtered out, we can get:

[0076]

[0077] After the noise part in formula (1) is multiplied by the two local carriers, the following expression is obtained:

[0078]

[0079] Next, assume that the pseudo-random code generated by the local pseudo-code generator can be expressed as Since the bit rate of the navigation message D(t) is 50 bps, the integration time T s Within ≤20ms, D(t) remains unchanged, so the integration process is regarded as a constant D. Then the outputs of the I and Q integrators are:

[0080]

[0081] In formula (10) and formula (11), For S IF (t) The initial phase difference with the local carrier.

[0082] If the pseudo-random code correlation operation can be performed during the signal acquisition process, the input signal pseudo-code phase is consistent with the locally generated pseudo-code phase, that is, formula (10) and formula (11) have When s I 、s Q They are The signal part of the two branches, the output signal power is:

[0083]

[0084] In formula (12), T, In a single signal reception, both are relatively fixed values. Therefore, the energy of the two branch signals calculated by this formula is only related to P R Related. R ∝Eφ (θ A ,φ A ), that is, the received signal energy is proportional to the antenna receiving gain. When the signal incident angle θ A ,φ A When it changes, the energy value also changes with the antenna receiving gain, such as Figure 3 shown.

[0085] In step S110, when the rolling carrier is designed, its power system will usually be able to maintain its spin frequency in a relatively stable range during operation. Frame division is performed according to the preset roll frequency value of the rolling carrier in the stable flight stage.

[0086] In this embodiment, when the signal energy observation value is framed, the number of signal sampling points is calculated based on the sampling rate of the signal energy observation value and the spin frequency of the carrier, and the frame length of each frame can be obtained. The signal energy observation value is then divided into multiple frames of signal energy observation values ​​according to the number of signal sampling points.

[0087] Specifically, when calculating the number of signal sampling points, that is, the frame length, the following formula is used:

[0088]

[0089] In formula (13), L is the frame length after framing, that is, the number of sampling points contained in each frame. s is the sampling rate of the signal energy observation value. r It is the preset spin frequency when the carrier is running stably.

[0090] It should be noted here that the spin frequency used in calculating the frame length only needs to be a rough value, because in the subsequent process, a more accurate estimation result can be obtained through the filter.

[0091] Next, each sample value in each frame, i.e., the signal energy observation sample value, is normalized within a single frame using the following formula:

[0092]

[0093] In formula (14), x(t) is the sampled value at each epoch, x Lmax is the maximum value among the L sample values ​​of the current frame, x Lmin is the minimum value among the L sample values ​​of the current frame, y norm (t) is the normalized sampling value, in which case -1≤y norm (t)≤1.

[0094] When a single antenna receives navigation satellite signals, the spin of the carrier will block the signal, resulting in discontinuous reception of the signal energy. In particular, when the carrier spins at a low speed, the receiver's carrier tracking loop loses lock, causing the signal to be gradually interrupted. At this time, the received signal energy will gradually decrease in amplitude from an obvious periodic pattern until the final energy observation value is 0. In this process, in addition to changes in the signal energy amplitude, its noise statistical characteristics will also change. Specifically, when the signal energy observation value changes normally and regularly, its variance is small; and when the signal energy observation value amplitude decays, its variance becomes larger. If the signal energy observation value amplitude is used as the basis for estimating the carrier roll angle, the impact of changes in the statistical characteristics of the observation noise must be considered.

[0095] Therefore, in step S120, the average amplitude of each frame of data is calculated first, and used as the weight of the corresponding frame.

[0096] In one embodiment, the average amplitude of the energy observation values ​​of each frame signal after normalization is calculated using the following formula:

[0097]

[0098] In formula (15), i represents the frame number, n represents the satellite number, represents the mean energy value of the signal in the i-th frame, L represents the frame length of a frame of signal energy observation value, y norm (t) represents the normalized signal energy observation value.

[0099] According to the analysis of the measured signal, if the average amplitude of a single frame exceeds a certain level, the signal energy observation value changes regularly, and the measurement error variance is small; when the average amplitude of a single frame decays but is not close to 0, its measurement error variance increases; when the average amplitude of a single frame is close to 0, the observation value has no characteristics of being associated with the carrier rolling phenomenon. Therefore, when calculating the weight of each frame signal energy observation value according to the average amplitude: first, the corresponding signal energy observation value is divided into a normal measurement interval, a suspicious measurement interval or an eliminated measurement interval according to two preset coefficients and the average amplitude, and the weights of the signal energy observation values ​​divided into the normal measurement interval and the eliminated measurement interval are assigned to 1 and 0, and then the weights of the signal energy observation values ​​divided into the suspicious measurement interval are calculated according to the two preset coefficients, the preset weight reduction coefficient and the average amplitude. In this way, the weight of each frame is assigned by the size of the average amplitude, which is conducive to the accuracy of the subsequent Kalman filter.

[0100] In this embodiment, when calculating the signal energy observation value weight of the suspicious measurement interval, the following formula is used:

[0101]

[0102] In formula (16), represents the signal energy observation weight of satellite n in all epochs in the i-th frame, k0 and k1 represent the preset coefficients, and k2 represents the reduced weight coefficient.

[0103] In one embodiment, a three-stage weight function is used to calculate the weight of each frame signal energy observation value according to the average amplitude. The three-stage weight function is expressed as:

[0104]

[0105] In formula (17), It represents the signal energy observation weight of all epochs of satellite n in the i-th frame. The coefficients k0 and k1 determine the interval division of normal measurements, suspicious measurements and eliminated measurements in the measurements. k2 represents the weight reduction coefficient of the suspicious measurement interval.

[0106] In step S130, the real-time roll angle and roll frequency of the carrier to be estimated are used as state variables, and the function of the single antenna receiving satellite signal gain with respect to the roll angle is used as a measurement equation to construct an extended Kalman filter model, wherein the basis for establishing the measurement equation is as follows: Figure 2 shown.

[0107] In the early stage of the launch of the spinning carrier, as the thrust increases, the carrier roll speed also gradually increases. During the flight of the carrier, although the carrier speed occasionally changes due to the need for attitude adjustment, in most cases, the carrier speed will remain in a relatively stable state. Therefore, in the carrier roll angle estimation process discussed in this embodiment, it is considered that the roll frequency remains relatively stable. The constant speed model can be used to model the roll angle change process. Assume that the state variable to be estimated is in, is the roll angle; ωr k is the rolling angular velocity, and:

[0108]

[0109] The system status update process is:

[0110]

[0111] In formula (19) and formula (20), is the prior estimate, W k =[0w k ] T is the random noise error generated inside the system, and its probability density function is assumed to be but is the prior state covariance matrix, is the state equation coefficient, Δt is the sampling interval, which is set to 1ms here.

[0112] In the process of extended Kalman filter correction, the multi-satellite real-time GNSS signal energy is used as the observation quantity. Where superscript n represents satellite number n, the measurement equation can be expressed as:

[0113] y k =h(x k ,v k ) (twenty one)

[0114] The nonlinear observation equation (21) is Taylor expanded into:

[0115]

[0116] In formula (22), is the function of each satellite gain with respect to the roll angle, C k 、V k Respectively expressed as:

[0117]

[0118] To observe the noise vector, in this embodiment, independent channel observations are performed on multiple satellites, and

[0119] The extended Kalman filter measurement update process is:

[0120]

[0121] In formula (25) to formula (27), R k The measurement noise v k The covariance matrix of . Since the observation errors follow a Gaussian distribution and are independent observations,

[0122] In the basic extended Kalman filter, the energy observations of multiple satellite signals are measured independently. If the energy measurement mean square error from each satellite is equal to Right now:

[0123]

[0124] At this time, the energy observation values ​​of each satellite have equal weights, and the filter gain K k After the weight function is determined according to the statistical characteristics of the observed quantity, the measurement noise covariance matrix R in the measurement update process is k The weight of each satellite energy measurement in this frame will be reassigned, and the weight matrix is:

[0125]

[0126] When the weight of the corresponding frame is used to dynamically adjust the measurement error covariance matrix, it is expressed as:

[0127]

[0128] In formula (30), represents the measurement error covariance matrix after dynamic weight adjustment, R k represents the measurement error covariance matrix before adjustment, and W represents the weight matrix composed of the weights of multiple satellites in the corresponding frames.

[0129] That is, in this embodiment, in the extended Kalman iteration process, the smaller the average energy value of each frame of the observation quantity is, the greater its weight is in the calculation of formula (17). The smaller the filter gain K is, the k When calculating, it is considered that the larger the measurement error variance is, the more obvious gross errors will be eliminated and not participate in the filter iteration. This can effectively improve the accuracy of the carrier roll attitude estimation. After substituting formula (30), i.e. the measurement error covariance matrix after dynamic weighting adjustment, into formula (25), we get:

[0130]

[0131] Substituting formula (31) into the filtering iteration process of the extended Kalman filter can make the filter adaptable to the changes in the statistical characteristics of the measurement noise. When the statistical characteristics of the observed noise change, a relatively stable roll attitude (including roll angle and roll frequency) estimate can also be obtained.

[0132] It should be noted here that the dynamic adjustment of the measurement error covariance matrix is ​​modified in real time for each frame of data, and in the iterative process of the extended Kalman filter, iterative filtering is performed on each sampling point, that is, each observation epoch, and each frame of data includes multiple sampling points, that is, multiple consecutive sampling points correspond to one weight.

[0133] Furthermore, the effectiveness of this method is demonstrated through semi-physical simulation experiments. In the semi-physical simulation experiment, an outdoor rolling test bench is used, which can achieve a speed control of 0 to 25 r / s. The GNSS (Global Navigation Satellite System) receiver is placed inside the turntable, and the rectangular microstrip antenna is installed on the side surface of the turntable. The real-time collected data is transmitted to the PC through a wireless data transmission interface for subsequent processing and analysis. In order to analyze the error of the carrier roll attitude estimated by the method, a Hall sensor is used to synchronously observe the carrier roll process. The Hall sensor is installed at a fixed position on the rolling platform, and its induction magnet is installed on the cylindrical structure of the simulated spinning carrier. When the cylindrical carrier spins, the magnet rotates with it and will be detected when passing through the Hall sensor. Two sets of receivers of the same model are used in the experiment, which are used for GNSS signal acquisition and providing clock information for the Hall sensor. The Hall sensor determines the carrier roll frequency and roll angle by the time interval between the magnet passing through the detection position twice. Since there is a fixed difference between the installation position of the Hall sensor and the 0° position of the carrier roll angle defined here, after the sensor is installed, the initial angle difference is measured by a level and a high-precision digital protractor and processed in subsequent calculations.

[0134] like Figure 4 As shown in the figure, the mean value of signal energy and the variance of observation value of each frame are statistically analyzed. According to the visualization method, the variance of signal energy observation value is distributed in three sections. When the normalized mean value of single frame signal energy exceeds 0.3, even if the mean value continues to increase, its variance does not change significantly, which is about less than 0.15; when the mean value decreases from 0.3 to 0.07, its variance increases from 0.12 to 0.26, and the two are inversely proportional; when the mean value is 0, the observation value does not participate in the roll angle solution and is not shown in the figure.

[0135] like Figure 5 As shown in the figure, the carrier roll angle is calculated by filtering and iterating the signal energy at each epoch as the observed value within 1 second. As can be seen from the figure, the real-time roll angle of the carrier can be obtained by continuously observing the signal energy of a single satellite using an extended Kalman filter. However, the real-time signal energy value contains measurement noise, so it can be seen that the roll angle measurement result in the figure also jitters.

[0136] like Figure 6As shown in the figure, it is obvious from the comparison of the given carrier roll attitude estimation results that the roll angle and roll frequency estimation results calculated by this method are more accurate than other methods after convergence. Comparing this method with the phase-locked loop method for solving the roll attitude, it can be seen that the phase-locked loop method is significantly affected by the measurement error because only the current measurement value is solved, and the roll attitude estimation accuracy is not high. When estimating in the same epoch, the traditional EKF roll angle estimation method uses the energy of multiple satellites as observations and enters the extended Kalman filter iteration. If one or more satellite signal energy observations are abnormal, it may cause a large error in the estimation result of the current epoch. Although the filter adjusts the filtering process through the estimated error, the actual observation still participates in the filter iteration process, and the change of the statistical characteristics of the observation error inevitably affects the estimation result. This method fully considers the impact of the change of the statistical characteristics of the observation error in the estimation process, designs the corresponding weight function, and adjusts the weight of the observation participating in the carrier roll attitude estimation, thereby largely avoiding the impact of the change of the statistical characteristics of the observation error.

[0137] In the above-mentioned carrier roll attitude estimation method based on navigation satellite signal energy, signal energy observation values ​​are extracted from real-time navigation satellite signals sent by multiple satellites, the signal energy observation values ​​are framed to obtain multiple frames of signal energy observation values, and the signal energy observation values ​​of each frame are normalized. The weight of each frame of signal energy observation value is calculated according to the average amplitude of the signal energy observation values ​​of each frame after normalization. At the same time, the real-time roll angle and roll frequency of the carrier to be estimated are used as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is used as the measurement equation to construct an extended Kalman filter model. For each frame of signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to filter and iterate the corresponding frame signal energy observation value after normalization to obtain the real-time estimation result of the carrier's roll attitude.

[0138] In terms of the category of observation quantities, this method only uses the signal energy amplitude that can be obtained by the receiver as the observation information to jointly estimate the roll angle and roll frequency. The sampling rate of the observation quantity is relatively high, and it can be applied to attitude estimation at various rotation speeds, breaking through the limitation that roll attitude estimation only uses the carrier phase with a low sampling rate as the observation quantity.

[0139] Furthermore, this method uses the direct relationship between the energy mean of a single frame signal and the observation noise to construct an observation error weight function under different energy means, and uses it in the extended Kalman filter to solve the problem of decreased estimation accuracy of the extended Kalman filter due to changes in the statistical characteristics of the observation noise.

[0140] In engineering applications, this method only uses a single-antenna GNSS receiver, which has the advantages of low complexity, low power consumption, and small installation space. The receiver can also simultaneously locate the carrier, and has a wide range of practical applications.

[0141] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0142] In one embodiment, Figure 7 As shown, a carrier roll attitude estimation device based on navigation satellite signal energy is provided, comprising: a signal energy observation value extraction module 200, a data preprocessing module 210, a frame weight assignment module 220, an extended Kalman filter model construction module 230 and a roll attitude real-time estimation module 240, wherein:

[0143] The signal energy observation value extraction module 200 is used to obtain real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0144] The data preprocessing module 210 is used to perform frame processing on the signal energy observation value according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation values, and perform normalization processing on the signal energy observation values ​​of each frame;

[0145] Each frame weight assignment module 220 is used to calculate the average amplitude of the signal energy observation values ​​of each frame after normalization, and calculate the weight of the signal energy observation value of each frame according to the average amplitude;

[0146] The extended Kalman filter model building module 230 is used to use the real-time roll angle and roll frequency of the carrier to be estimated as state variables and the function of the satellite signal gain received by the single antenna with respect to the roll angle as a measurement equation to build an extended Kalman filter model;

[0147] The roll attitude real-time estimation module 240 is used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model with corresponding weights for each frame signal energy observation value, and use the adjusted extended Kalman filter model to iteratively filter the normalized corresponding frame signal energy observation value to obtain the real-time estimation result of the carrier's roll attitude.

[0148] For the specific definition of the carrier roll attitude estimation device based on navigation satellite signal energy, please refer to the definition of the carrier roll attitude estimation method based on navigation satellite signal energy in the above text, which will not be repeated here. Each module in the above-mentioned carrier roll attitude estimation device based on navigation satellite signal energy can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0149] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a carrier roll attitude estimation method based on navigation satellite signal energy is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0150] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0151] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0152] Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0153] The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized;

[0154] Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude;

[0155] The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model.

[0156] For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

[0157] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0158] Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals;

[0159] The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized;

[0160] Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude;

[0161] The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model.

[0162] For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0164] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for estimating carrier roll attitude based on navigation satellite signal energy, characterized in that: The method comprises: Acquire real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals; The signal energy observation value is framed according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain multiple frames of signal energy observation value, and the signal energy observation value of each frame is normalized; Calculate the average amplitude of the energy observation values ​​of each frame signal after normalization, and calculate the weight of each frame signal energy observation value according to the average amplitude; The real-time roll angle and roll frequency of the carrier to be estimated are taken as state variables, and the function of the satellite signal gain received by a single antenna with respect to the roll angle is taken as the measurement equation to construct an extended Kalman filter model. For each frame signal energy observation value, the corresponding weight is used to dynamically weight the measurement error covariance matrix in the extended Kalman filter model, and the adjusted extended Kalman filter model is used to iteratively filter the normalized corresponding frame signal energy observation value to obtain a real-time estimation result of the carrier's roll attitude.

2. The method for estimating the rolling attitude of a carrier according to claim 1, characterized in that: The signal energy observation value is extracted using the I / Q branch of the carrier loop.

3. The method for estimating the rolling attitude of a carrier according to claim 2, characterized in that: When the signal energy observation value is subjected to frame processing: Calculating according to the sampling rate of the signal energy observation value and the spin frequency of the carrier to obtain the number of sampling points of a single frame of the signal; The signal energy observation value is divided into multiple frames of signal energy observation values ​​according to the number of signal sampling points.

4. The method for estimating the rolling attitude of a carrier according to claim 3, characterized in that: Calculate the average amplitude of the energy observation value of each frame signal after normalization using the following formula: In the above formula, i represents the frame number, n represents the satellite number, represents the mean energy value of the signal in the i-th frame, L represents the frame length of a frame of signal energy observation value, y norm (t) represents the normalized signal energy observation value.

5. The method for estimating the rolling attitude of a carrier according to claim 4, characterized in that: When calculating the weight of each frame signal energy observation value according to the average amplitude: According to two preset coefficients and the average amplitude, the corresponding signal energy observation value is divided into a normal measurement interval, a suspicious measurement interval or an eliminated measurement interval; The weights of the signal energy observation values ​​divided into the normal measurement interval and the eliminated measurement interval are assigned to 1 and 0, and the weights of the signal energy observation values ​​divided into the suspicious measurement interval are calculated according to two preset coefficients, the preset reduction coefficient and the average amplitude.

6. The method for estimating the rolling attitude of a carrier according to claim 5, characterized in that: The following formula is used to calculate the signal energy observation value weight of the suspicious measurement interval: In the above formula, represents the signal energy observation weight of satellite n in all epochs in the i-th frame, k0 and k1 represent the preset coefficients, and k2 represents the reduced weight coefficient.

7. The method for estimating the rolling attitude of a carrier according to claim 5, characterized in that: The weight of each frame signal energy observation value is calculated according to the average amplitude using a three-stage weight function, which is expressed as: In the above formula, It represents the signal energy observation weight of all epochs of satellite n in the i-th frame. The coefficients k0 and k1 determine the interval division of normal measurements, suspicious measurements and eliminated measurements in the measurements. k2 represents the weight reduction coefficient of the suspicious measurement interval.

8. The method for estimating the rolling attitude of a carrier according to any one of claims 1 to 7, characterized in that: When the corresponding weights are used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model, it is expressed as: In the above formula, represents the measurement error covariance matrix after dynamic weight adjustment, R k represents the measurement error covariance matrix before adjustment, the subscript k represents the kth sample, and W represents the weight matrix composed of the weights of multiple satellites in the corresponding frame.

9. A carrier roll attitude estimation device based on navigation satellite signal energy, characterized in that: The device comprises: A signal energy observation value extraction module, used to obtain real-time navigation satellite signals sent by multiple satellites, and extract signal energy observation values ​​based on the real-time navigation satellite signals; A data preprocessing module, used to perform frame processing on the signal energy observation value according to the sampling rate of the signal energy observation value and the spin frequency of the carrier, obtain multiple frames of signal energy observation values, and normalize the signal energy observation values ​​of each frame; Each frame weight assignment module is used to calculate the average amplitude of the signal energy observation values ​​of each frame after normalization processing, and calculate the weight of the signal energy observation value of each frame according to the average amplitude; An extended Kalman filter model building module is used to take the real-time roll angle and roll frequency of the carrier to be estimated as state variables and the function of the satellite signal gain received by a single antenna with respect to the roll angle as a measurement equation to build an extended Kalman filter model; The roll attitude real-time estimation module is used to dynamically adjust the measurement error covariance matrix in the extended Kalman filter model with corresponding weights for each frame signal energy observation value, and use the adjusted extended Kalman filter model to iteratively filter the normalized corresponding frame signal energy observation value to obtain the real-time estimation result of the carrier's roll attitude.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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