Signal quality monitoring device and method of Beidou satellite navigation system B1C navigation signal based on chip domain observation

Abstract

The present application discloses a signal quality monitoring device and method for the B1C signal of the Beidou satellite navigation system based on chip domain observations. According to the signal quality monitoring device and method of the present application, multiple original observations of the chip waveform of the B1C navigation signal can be obtained, the degree of distortion of the chip waveform can be determined, and whether the B1C navigation signal is distorted can be judged. In addition, the threat model and threat space of the Beidou satellite navigation system B1C signal newly adopted by the International Civil Aviation Organization are used to simulate and evaluate the signal quality monitoring device and method disclosed in the present application, and its performance is verified. According to the present application, by detecting the degree of distortion of the chip to determine whether there is an abnormal waveform in the signal, the signal quality monitoring sensitivity in the satellite-based augmentation system can be improved, and the ability of the B1C navigation signal to meet the requirements of the dual-frequency multi-constellation satellite-based augmentation system can be effectively improved, and the user integrity can be better protected.

Description

Technical Field

[0001] The present application relates to the technical field of radio navigation signal integrity monitoring, and in particular to a device and method for monitoring the signal quality of a Beidou satellite navigation system B1C navigation signal based on chip domain observations. Background Art

[0002] Defects or occasional failures in the signal generation payload of navigation satellites are generally considered to be the cause of evil waveforms (EWF) in the signal and an important risk factor for user integrity loss. Since the GPS SVN-19 event in 1993, when EWF was first observed, a variety of EWF threat models have been proposed to accurately characterize the abnormal performance of the GPS L1 C / A navigation signal in the SVN-19 event. In 2000, the International Civil Aviation Organization (ICAO) adopted the second-order step threat model (2OS-TM) in the Standards and Recommended Practices (SARPs) document, in which the parameter Δ is used to represent the advance or delay of each falling edge of the nominal signal, and the unit is the chip length (T C ), using the oscillation frequency f d and the damping coefficient σ represent the level damping oscillation of each chip transition edge of the nominal signal, and the units are megahertz (MHz) and mega nepera per second (MNp / s) respectively. A threat model of a navigation signal characterizes the modeling approximation of the EWF that may be generated by the signal.

[0003] In order to protect safety-critical users such as civil aviation from the integrity risks brought by EWF, signal quality monitoring (SQM) technology came into being. Usually, the Global Navigation Satellite System (GNSS) signal receiver achieves capture and tracking by correlating the received signal with the local reproduced nominal signal. Therefore, early single-frequency satellite-based augmentation systems (SBAS), including North America's Wide Area Augmentation System (WAAS) and Europe's Geostationary Navigation Overlay Service (EGNOS), all use the SQM method based on correlation peak shape detection. This method believes that if the input signal is distorted, it will be reflected as a distortion of the correlation peak in the cross-correlation function. Therefore, the basic idea of ​​the traditional SQM method based on correlation peak shape detection is to use a reference station monitoring receiver deployed with multiple correlators to continuously measure a series of detection statistics such as the slope and symmetry of the correlation peak of the navigation satellite signal at different positions under the given false alarm probability requirement of the operating performance, and subtract them from the nominal value of the detection statistic to obtain the absolute distortion degree of the correlation peak. The maximum value of the ratio of each absolute distortion degree to the corresponding detection threshold is taken as the detection factor. If the detection factor is not less than 1, EWF exists in the input signal.

[0004] Although the correlation peak shape detection method has been maturely applied in WAAS, EGNOS and other systems for many years, the performance of this method is limited, and there are still several problems in the new generation of dual-frequency multi-constellation satellite-based augmentation system (DFMC SBAS): First, the emerging and modern GNSS signals mostly use binary offset carrier (BOC) modulation, and the correlation peak shape of the new system signal is different from the traditional binary phase shift keying (BPSK) modulation signal, and the traditional SQM has applicability problems for the new signal; second, the dual-frequency ionosphere-free combined observations at the B1 and B2 frequency points will amplify the deviation on the single-frequency signal to 2.26 and 1.26 times respectively, which poses a greater challenge to the performance of the traditional SQM. Therefore, in the application of DFMC SBAS, the implementation of SQM on the BOC (1,1) modulated Beidou satellite navigation system B1C signal requires a new SQM device and method with wider applicability and higher detection sensitivity.

[0005] Figure 1 The figure shows the partial correlation peak shape diagram of the BeiDou satellite navigation system B1C signal and the distortion detection factor obtained by using the traditional multi-correlator based SQM technology, where: Figure 1 (a) is the ideal correlation peak shape of the B1C signal, Figure 1 (b) is the B1C signal at Δ = 0.05T C 、f d =7.5MHz, σ = 6.5MNp / s, Figure 1 (c) is the B1C signal at Δ = 0.0T5 C 、f d =7.5MHz, σ =7.0MNp / s (only σ has a slight change), Figure 1 (d) is the distortion detection factor obtained by using the traditional multi-correlator based SQM method. From (d), it can be seen that the distortion in (b) is just detected, and the distortion in (c) cannot be detected. Summary of the invention

[0006] The present application provides a signal quality monitoring device and method for the B1C navigation signal of the Beidou satellite navigation system based on chip domain observations. By detecting the degree of distortion of the chip, it is determined whether there is an abnormal waveform in the signal. This can improve the signal quality monitoring sensitivity in the satellite-based augmentation system, effectively enhance the ability of the B1C navigation signal to meet the requirements of the dual-frequency multi-constellation satellite-based augmentation system, and better protect user integrity.

[0007] According to one embodiment of the present application, a signal quality monitoring device for a Beidou satellite navigation system B1C navigation signal based on chip domain observations is provided to obtain multiple original observations of the chip waveform of the B1C navigation signal, determine the degree of distortion of the chip waveform, and judge whether the B1C navigation signal is distorted.

[0008] According to another embodiment of the present application, a signal quality monitoring device for a Beidou satellite navigation system B1C navigation signal based on chip domain observations is provided, the signal quality monitoring device comprising: a monitoring receiver, receiving a B1C navigation signal, and obtaining a plurality of original observations of a chip waveform of the B1C navigation signal; a quality monitoring unit, smoothing and normalizing the plurality of original observations, and generating a plurality of real-time detection statistics according to the plurality of smoothed and normalized observations; a storage unit, providing a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds under an undistorted B1C navigation signal; and a detection determination unit, judging whether the B1C navigation signal is distorted according to deviations between the plurality of real-time detection statistics and the plurality of detection statistic nominal values, and the relationship between the deviations and the corresponding detection thresholds.

[0009] According to another embodiment of the present application, a signal quality monitoring method for the Beidou satellite navigation system B1C navigation signal based on chip domain observations is provided to obtain multiple original observations of the chip waveform of the B1C navigation signal, determine the degree of distortion of the chip waveform, and judge whether the B1C navigation signal is distorted.

[0010] According to another embodiment of the present application, a signal quality monitoring method for a Beidou satellite navigation system B1C navigation signal based on chip domain observations is provided, wherein the method comprises: receiving a B1C navigation signal to obtain a plurality of original observations of a chip waveform of the B1C navigation signal; smoothing and normalizing the plurality of original observations, and generating a plurality of real-time detection statistics according to the plurality of smoothed and normalized observations; providing a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds under an undistorted B1C navigation signal; and judging whether the B1C navigation signal is distorted according to deviations between the plurality of real-time detection statistics and the plurality of detection statistic nominal values, and the relationship between the deviations and the corresponding detection thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The diagram shows the partial correlation peak shape of the BeiDou satellite navigation system B1C signal and the distortion detection factor obtained by using the traditional SQM technology based on multiple correlators.

[0012] Figure 2 A schematic block diagram of a signal quality monitoring device for a BeiDou satellite navigation system B1C navigation signal based on chip domain observations according to an embodiment of the present application is shown.

[0013] Figure 3 A schematic block diagram of a signal quality monitoring device for a BeiDou satellite navigation system B1C navigation signal based on chip domain observations according to another embodiment of the present application is shown.

[0014] Figure 4 A schematic block diagram of the process of a signal quality monitoring method according to an embodiment of the present application is shown.

[0015] Figure 5 A schematic diagram of the rising edge of a portion of the chip waveform of the BeiDou satellite navigation system B1C signal and a distortion detection factor obtained by using an SQM device and method based on chip domain observations according to an embodiment of the present application are shown.

[0016] Figure 6 The performance evaluation result obtained by evaluating the signal quality monitoring device and method according to an embodiment of the present application through an evaluation example is shown.

[0017] Figure 7The performance confidence bound comparison result of the performance evaluation result obtained by evaluating the signal quality monitoring device and method according to an embodiment of the present application through an evaluation example is shown compared with the performance confidence bound comparison result of the traditional SQM method based on multiple correlators. DETAILED DESCRIPTION

[0018] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.

[0019] In actual operation, the signal quality monitoring device continuously monitors the signals of each navigation satellite visible in orbit, and continuously performs real-time signal quality monitoring on each monitored signal.

[0020] According to one embodiment of the present application, a signal quality monitoring device for the Beidou satellite navigation system B1C navigation signal based on chip domain observations can obtain multiple original observations of the chip waveform of the B1C navigation signal, determine the degree of distortion of the chip waveform, and judge whether the B1C navigation signal is distorted.

[0021] Figure 2 The signal quality monitoring device 1 of the Beidou satellite navigation system B1C navigation signal based on chip domain observation according to an embodiment of the present application is shown. As shown in the figure, the signal quality monitoring device 1 includes a monitoring receiver 100, a quality monitoring unit 200, a storage unit 300 and a detection and determination unit 400.

[0022] The monitoring receiver 100 can receive the B1C navigation signal and obtain a plurality of original observations of the chip waveform of the B1C navigation signal, wherein the original observations of the chip waveform can characterize the shape characteristics of the transition edge (rising edge or falling edge) of the chip waveform of the B1C navigation signal.

[0023] The quality monitoring unit 200 performs smoothing and normalization processing on the multiple original observation quantities, and generates multiple real-time detection statistics according to the multiple smoothed and normalized observation quantities.

[0024] The storage unit 300 provides a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds under the undistorted B1C navigation signal.

[0025] The detection and determination unit 400 determines whether the B1C navigation signal is distorted according to the deviations between the multiple real-time detection statistics and the multiple detection statistics nominal values, and the relationship between the deviations and the corresponding detection thresholds.

[0026] Figure 3 A signal quality monitoring device for BeiDou satellite navigation system B1C navigation signal based on chip domain observations according to another embodiment of the present application is shown.

[0027] The monitoring receiver 100 receives the B1C navigation signal and obtains a plurality of original observations of the chip waveform of the B1C navigation signal, an original estimated value of the B1C navigation signal amplitude, and an elevation angle of the monitored satellite. The monitoring receiver 100 can continuously output a plurality of original chip domain observations of the B1C navigation signal, an original estimated value of the signal amplitude, and an elevation angle of the monitored satellite in real time at given time intervals (e.g., 0.5s, 1s, or other time intervals). The signal amplitude estimated value can represent the power level of the B1C navigation signal.

[0028] The quality monitoring unit 200 includes a preprocessing module 210 and a detection statistic generating module 220. The preprocessing module 210 performs smoothing processing on the obtained multiple original chip domain observations and the original estimated values ​​of the signal amplitude, and performs normalization processing on the multiple smoothed observations using the smoothed signal amplitude estimated values ​​to obtain multiple smoothed and normalized observations.

[0029] The detection statistic generation module 220 screens and combines a plurality of smoothed and normalized observations to generate a plurality of real-time detection statistics.

[0030] The storage unit 300 includes a pre-storage module 310 and a calculation module 320. The pre-storage module 310 pre-stores the detection statistic observation information obtained based on the long-term observation of the undistorted B1C navigation signal. The calculation module 320 provides a plurality of detection statistic nominal values ​​corresponding to the elevation angle and a plurality of corresponding detection thresholds according to the observation information pre-stored in the pre-storage module 310 and the elevation angle of the monitored satellite from the monitoring receiver 100.

[0031] The detection and determination unit 400 determines a distortion detection factor according to the deviations between the multiple real-time detection statistics and the multiple detection statistic nominal values ​​and the multiple corresponding detection thresholds, and determines whether the B1C navigation signal is distorted according to the distortion detection factor. For example, the detection and determination unit 400 may divide the absolute values ​​of the multiple deviations between the multiple real-time detection statistics and the multiple detection statistic nominal values ​​by the corresponding detection thresholds, wherein the largest ratio is called the distortion detection factor, and determines whether the B1C navigation signal is distorted according to whether the distortion detection factor is greater than 1.

[0032] According to another aspect of the present application, a signal quality monitoring method for the Beidou satellite navigation system B1C navigation signal based on chip domain observations is also provided to obtain multiple original observations of the chip waveform of the B1C navigation signal, determine the degree of distortion of the chip waveform, and judge whether the B1C navigation signal is distorted. Figure 4 FIG. 1 is a schematic flow chart of a signal quality monitoring method according to an embodiment of the present application. Figure 4As shown, in S10, a B1C navigation signal is received to obtain a plurality of original observations of the code waveform of the B1C navigation signal; in S20, the plurality of original observations are smoothed and normalized, and a plurality of real-time detection statistics are generated according to the plurality of smoothed and normalized observations; in S30, a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds are provided under a distortion-free B1C navigation signal; in S40, whether the B1C navigation signal is distorted is determined according to the deviations between the plurality of real-time detection statistics and the plurality of detection statistic nominal values, and the relationship between the deviations between the plurality of real-time detection statistics and the corresponding detection thresholds.

[0033] The signal quality monitoring device and method according to the present application are further described in detail below in conjunction with a specific embodiment.

[0034] For a certain Beidou navigation satellite being monitored, the monitoring receiver 100 receives the B1C navigation signal broadcasted by it, and then int For each observation epoch of the interval, the original chip domain observations CDO1, CDO2, ..., CDO of the M B1C navigation signals are continuously output in real time. M , the original estimated value of the signal amplitude P I , the altitude angle θ of the monitored satellite. Among them, the M chip domain observations characterize the shape characteristics of the rising edge of the chip waveform of the B1C navigation signal, and the signal amplitude estimation value characterizes the power level of the signal.

[0035] The preprocessing module 210 of the quality monitoring unit 200 processes the obtained M original chip domain observations and the original estimated values ​​of the signal amplitude into T smt The smoothing process is performed for seconds, and then the smoothed signal amplitude estimation value is used to normalize the M smoothed chip domain observations respectively to obtain M smoothed and normalized chip domain observations.

[0036] Smoothing

[0037] Since the BeiDou navigation satellite was monitored, at a given observation epoch interval T int Under this condition, the M original chip domain observations and the original estimated signal amplitude are continuously smoothed, which can effectively reduce the influence of noise and multipath effects on the original chip domain observations. For the observation epoch k,

[0038] k≤T smt Seconds:

[0039]

[0040]

[0041] k>T smt Seconds:

[0042]

[0043]

[0044] Among them, CDO i,raw , P I,raw They represent the original chip domain observation and the original estimated value of the signal amplitude, CDO i,smt , P I,smt They represent the smoothed chip domain observation and the smoothed signal amplitude estimate respectively.

[0045] Normalization

[0046] For each observation epoch k, the M smoothed chip-domain observations are normalized using the smoothed signal amplitude estimate:

[0047]

[0048] Get the M smoothed and normalized chip domain observations CDO of the observation epoch k i,nml .

[0049] The detection statistic generation module 220 of the quality monitoring unit 200 generates the M smoothed and normalized chip domain observations CDO of the observation epoch k. i,nml After screening and combination, N real-time detection statistics metric1, metric2, ..., metric N .

[0050] The detection statistic generation module 220 can select appropriate screening and combination processing methods based on comprehensive considerations such as the degree of code shape distortion reflected by the detection statistic, the signal-to-noise ratio of the detection statistic itself, detection sensitivity, computational complexity, and SQM's requirements for real-time detection.

[0051] Example 1: The chip domain observation after smoothing and normalization can be directly taken as the detection statistic, in which case N = M

[0052] metric j =CDO j,nml ,1≤j≤M.

[0053] Example 2: The square of the chip domain observation after smoothing and normalization can be taken as the detection statistic, in which case N = M

[0054]

[0055] Example 3: The chip domain observations after smoothing and normalization can be subtracted pairwise to obtain the detection statistic.

[0056]

[0057] Example 4: The chip domain observations after smoothing and normalization can be compared with the median observations one by one to obtain the detection statistic, N = M-1

[0058]

[0059] Example 5: The chip domain observations after smoothing and normalization can be subtracted from each other pair by pair, and then compared with the median observations one by one to obtain the detection statistic.

[0060]

[0061] The detection statistics generation module 220 continuously generates N real-time detection statistics of observation epoch k. Output to the detection and determination unit 400.

[0062] The pre-storage module 310 in the storage unit 300 can pre-store detection statistic observation information obtained based on long-term observation of the distortion-free B1C navigation signal. The calculation module 320 provides multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to the altitude angle based on the observation information pre-stored in the pre-storage module 310 and the altitude angle of the monitored satellite from the monitoring receiver 100.

[0063] According to one embodiment, the detection statistic observation information pre-stored in the pre-storage module 310 may include multiple detection statistic nominal value fitting coefficients obtained based on long-term observations of distortion-free B1C navigation signals, multiple corresponding detection threshold fitting coefficients, and a normal distribution expansion coefficient. The calculation module 320 can calculate multiple detection statistic nominal values ​​and multiple corresponding detection thresholds based on the coefficients pre-stored in the pre-storage module 310 and the altitude angle fitting of the monitored satellite from the monitoring receiver.

[0064] Specifically, the storage module 310 of the storage unit 300 can pre-store N×(n+1) detection statistic nominal value fitting coefficients of N detection statistics obtained based on n-order polynomial fitting of long-term observations of distortion-free B1C navigation signals, N×(n+1) corresponding detection threshold fitting coefficients, and N normal distribution expansion coefficients.

[0065] Raw data collection and processing

[0066] At a longer observation time T obsWithin seconds, the monitoring receiver is used to continuously receive the B1C signals broadcast by all visible Beidou navigation satellites in orbit, and a total of K groups of original chip domain observations, original estimated values ​​of signal amplitudes and altitude angle data of the monitored satellites are obtained, where K is the total number of epochs:

[0067] K=T obs / T int

[0068] After the aforementioned smoothing, normalization, construction and other processing, K groups of detection statistics, N in each group, and their corresponding satellite elevation angles are obtained.

[0069] Pre-stored coefficient calculation

[0070] Each of the N detection statistics is processed longitudinally in time. For example, for the constructed j-th (1≤j≤N) detection statistic:

[0071] a. Divide the 0-90° elevation angle into Q equal-length intervals, classify the K real-time detection statistics values ​​into corresponding intervals according to their satellite elevation angles, and calculate the Q means and Q standard deviations of the values ​​contained in the Q intervals;

[0072] b. Fitting the Q means and Q standard deviations with n-order polynomials respectively to obtain n+1 mean fitting coefficients and n+1 standard deviation fitting coefficients, which are stored in the pre-stored module 310;

[0073] c. Using the above fitting coefficients and K satellite altitude angles, calculate K real-time detection statistics metrics j,k The corresponding K mean μ j,k and K standard deviations σ j,k , 1≤k≤K. Standardize the test statistic:

[0074]

[0075] For K standardized real-time detection statistics Based on the standard normal distribution, consider its compliance with the expanded normal distribution Make The tail distribution of can be expanded normal distribution The normal distribution expansion coefficient f is stored in the storage module 310.

[0076] The calculation module 320 of the storage unit 300 continuously uses the altitude angle θ of the monitored satellite from the monitoring receiver 100 in each observation epoch. k Calculate N detection statistic nominal values ​​and N corresponding detection thresholds. For example, for the jth (1≤j≤N) detection statistic metric j :

[0077] Calculation of nominal value of test statistic

[0078] Take out the n+1 mean fitting coefficients stored in the storage module 320 and name them μ1, μ2, ..., μ n , μ n+1 , then the j-th test statistic nominal value is calculated as follows:

[0079]

[0080] Detection threshold calculation

[0081] Take out the n+1 standard deviation fitting coefficients stored in the storage module 320 and name them as σ1, σ2, ..., σ n , σ n+1 , then the fitted standard deviation of the j-th test statistic is calculated as follows:

[0082]

[0083] Take out the normal distribution expansion coefficient f stored in the storage module 320, and use the multiplier K corresponding to the given false alarm probability of the performance requirement ffd , then the detection threshold of the j-th detection statistic is calculated as follows:

[0084]

[0085] The calculation module 320 continuously calculates the N detection statistic nominal values ​​corresponding to the monitored satellite altitude angle at the observation epoch k and N detection thresholds j,k , 1≤j≤N, output to the detection and judgment unit 400.

[0086] According to another embodiment, the detection statistic observation information pre-stored in the pre-storage module 310 of the storage unit 300 may include multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to different altitude angles obtained based on long-term observation of the distortion-free B1C navigation signal. The calculation module 320 can query the pre-storage module according to the altitude angle of the monitored satellite from the monitoring receiver 100 to obtain multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to the altitude angle.

[0087] Specifically, the pre-storage module 310 of the storage unit 300 pre-stores Q groups of N detection statistic nominal values ​​at different altitudes and Q groups of N detection thresholds at different altitudes, obtained based on long-term observation of the undistorted B1C navigation signal.

[0088] Raw data collection and processing

[0089] At a longer observation time Tobs Within seconds, the monitoring receiver is used to continuously receive the B1C signals broadcast by all visible Beidou navigation satellites in orbit, and a total of K groups of original chip domain observations, original estimated values ​​of signal amplitudes and altitude angle data of the monitored satellites are obtained, where K is the total number of epochs:

[0090] K=T obs / T int

[0091] After the aforementioned smoothing, normalization, construction and other processing, K groups of real-time detection statistics, N in each group, and their corresponding satellite elevation angles are obtained.

[0092] Pre-stored observation information calculation

[0093] Each of the N detection statistics is processed longitudinally in time. For example, for the constructed j-th (1≤j≤N) detection statistic:

[0094] a. Divide the 0-90° altitude angle into Q equal-length intervals Classify the K real-time detection statistics values ​​into corresponding intervals according to their satellite altitude angles, and calculate the Q means of the values ​​contained in the Q intervals and Q standard deviations The Q test statistics interval mean Stored in the storage module 310 as a nominal value of the detection statistic;

[0095] b. For Q means and Q standard deviations Perform n-order polynomial fitting respectively to obtain n+1 mean fitting coefficients and n+1 standard deviation fitting coefficients;

[0096] c. Using the above fitting coefficients and K satellite altitude angles, calculate K real-time detection statistics metrics j,k The corresponding K mean μ j,k and K standard deviations σ j,k , 1≤k≤K. Standardize the test statistic:

[0097]

[0098] For K standardized real-time detection statistics Based on the standard normal distribution, consider its compliance with the expanded normal distribution Make The tail distribution of can be expanded normal distribution covered by the tail;

[0099] d. Use the multiplier K corresponding to the given false alarm probability of the performance requirement ffd, then the detection threshold of the jth detection statistic in the altitude interval q is calculated as follows:

[0100]

[0101] Set Q detection thresholds TH j,q Stored in storage module 310.

[0102] The calculation module 320 of the storage unit 300 continuously uses the altitude angle θ of the monitored satellite in each observation epoch. k , query the N detection statistic nominal values ​​and N corresponding detection thresholds corresponding to the altitude angle in the pre-stored module 310. For example, for the jth (1≤j≤N) detection statistic metric j :

[0103] a. Determine the altitude angle θ k The altitude angle interval, for example

[0104] b. Query the nominal value of the detection statistic and the detection threshold and assign them:

[0105]

[0106] The calculation module 320 continuously calculates the N detection statistic nominal values ​​corresponding to the monitored satellite altitude angle at the observation epoch k and N detection thresholds j,k , 1≤j≤N, output to the detection and judgment unit 400.

[0107] The detection and discrimination unit 400 continuously divides the absolute values ​​of the N deviations between the N real-time detection statistics of the observation epoch k and the N nominal values ​​of the detection statistics by the corresponding N detection thresholds, and the largest ratio is called the distortion detection factor The calculation is as follows:

[0108]

[0109] If the distortion detection factor of the observed epoch k is If the B1C navigation signal broadcast by the monitored satellite is distorted, the signal is safe and available.

[0110] The following is an evaluation example of a device and method for monitoring the signal quality of a Beidou satellite navigation system B1C navigation signal based on chip domain observations according to the present application.

[0111] The evaluation of SQM performance needs to be carried out under the conditions of the threat model and threat space of the monitored signal, and should include double tests. The first test is the constant false alarm test of the detection statistic of the monitored signal by SQM itself within an observation epoch; the second test is determined by the requirements of DFMC SBAS. In order to ensure that the signal that SQM cannot detect EWF is indeed safe for users, it should be judged by whether its maximum undetected differential error (MUDE) overflows the maximum error range residual (MERR).

[0112] At the SQM detection level, ICAO defined the value ranges of each threat model parameter for GPS and GLONASS single-frequency signals in the 2OS-TM framework in SARPs in the early stage, thereby forming the threat space of the corresponding signal. The threat space of a signal represents the maximum set of EWFs that may be generated by the signal under the 2OS-TM framework. Under given application requirements and constraints, as many EWFs in the threat space as possible should be detected. The threat model and threat space applicable to the BDS dual-frequency civil signal were submitted to ICAO in June 2020 and will be adopted in the newly revised SARPs. The threat model of the BDS B1C signal also follows the 2OS-TM modeling standard, and the threat space is:

[0113] Δ: -0.05~+0.05T C f d :1.5~18MHzσ:0.1~20MNp / s

[0114] At the ranging error level, MERR is the maxPRE value that does not cause HMI under the worst satellite geometry conditions and is the standard deviation of the dual-frequency ranging error σ DFRE Function:

[0115] MERR=K V,PA ·σ DFRE

[0116] Among them, K V,PA = 5.33 from 9.8×10 of precision approach every 150 seconds -8 The fault probability distribution requirements are given. For the dual-frequency ionosphere-free combination observation, the offset on any single-frequency signal will be amplified, and the amplification factor is determined by the two carrier frequencies. For the Beidou satellite navigation system B1C and B2a dual-frequency combination, its dual-frequency pseudorange is:

[0117] ρ DF =2.26·ρ B1C -1.26·ρ B2a

[0118] Consider that the maximum dual-frequency ranging error index (DFREI) allowed by DFMC SBAS is 4, that is, MERR DF =3.64 meters, then the MERR limit of the B1C single-frequency signal is:

[0119] MERR B1C ≈3.64 / 2.26≈1.61 meters

[0120] Therefore, the criterion for judging the effectiveness of an SQM method is: on the one hand, the more EWFs in the threat space that can be detected by the SQM method, the more robust the method is and the more effective it is in protecting users against integrity risks; on the other hand, for all EWFs in the given threat space of the signal that are not detected by the SQM method, the maximum differential pseudorange error (maxPRE) introduced between the reference station and various user receivers (within the specified design space) does not overflow, or does not overflow MERR after reasonable smoothing.

[0121] The algorithm parameters used in the evaluation example of this application are configured as follows:

[0122] (1) The number of original chip domain observables (CDO) of the monitored signal output by the monitoring receiver in one observation epoch is M=8;

[0123] (2) 8 CDOs are taken from a 0.2T long channel centered on the code rising edge level jump point. C Evenly distributed within the chip, spacing BIN = 0.025T C ;

[0124] (3) The length of an observation epoch is T int = 1 second;

[0125] (4) The detection statistic is simply defined as the raw estimate of the signal amplitude within an observation epoch Normalized CDO:

[0126] metric i =CDO i,nml ,1≤i≤8

[0127] (5) The minimum detectable error (MDE) is used as the detection threshold in the evaluation, which is the nominal noise standard deviation σ of each CDO. CDO Function:

[0128]

[0129] Among them, K ffd =5.26 The false alarm probability of Category I precision approach (CAT-I) (1.5×10 -7 ) requires that K md =3.09 According to the missed detection probability requirement (1×10 -3 ) given;

[0130] (6) Although the code rising edges are not uniformly distributed within the code period and the sampling rate is not necessarily divisible by the code rate, for a longer smoothing integration time, the noise levels of each CDO tend to be consistent, and their mean can be used to calculate the MDE:

[0131]

[0132] Wherein, the bilateral bandwidth BW = 24MHz, the sampling rate F S =72MHz, the minimum carrier-to-noise ratio of the B1C signal received on the ground is C / N0 = 35.5dB-Hz, and RER is defined as the rising edge rate, which is the minimum value among the 63 PRNs.

[0133] (7) The discretization scheme of the B1C signal threat space adopted in this evaluation embodiment is as follows:

[0134] Δ B1C :-0.05:0.01:-0.01,0.01:0.01:0.05;

[0135]

[0136] σ B1C : 0.1,1:0.5:20;

[0137] (8) The correlator spacing and pre-correlation bandwidth of the reference and user receivers in the receiver configuration used in this evaluation embodiment are consistent with the DFMC SBAS regulations; the characteristics of the user receiver, such as the roll-off speed and differential group delay, are further conservatively designed based on the DFMC SBAS performance requirements to ensure that the SQM algorithm of the CDO-based BDSB1C signal proposed in this application meets the DFMC SBAS integrity monitoring performance requirements.

[0138] Figure 5 The diagram shows the rising edge of part of the chip waveform of the BeiDou satellite navigation system B1C signal and the distortion detection factor obtained by using the SQM device and method based on chip domain observations of the present application under the parameter configuration of the evaluation embodiment, wherein: Figure 5 The distortion modeling parameters used in the three chip waveforms in (a) to (c) are Figure 1 The distortion modeling parameters used for the three correlation peaks in (a) to (c) correspond one to one. Figure 5 (d) is the distortion detection factor obtained by using the SQM device and method based on chip domain observations of the present application. It can be seen that:

[0139] a. The distortion detection factor of the evaluation embodiment of the present application for the signal containing EWF is significantly higher than that of the conventional multi-correlator-based SQM;

[0140] b. Traditional methods cannot detect signals containing EWF, but the new method can greatly increase the probability of detection.

[0141] Figure 6 The performance evaluation results of the evaluation embodiment of the present application are shown. Each dot in the figure corresponds to an EWF point in the discretized threat space. The equivalent carrier-to-noise ratio of the horizontal axis is obtained by nonlinear transformation of the detection factor to establish a relationship between the SQM detection result and the signal-to-noise ratio. The vertical axis is the maximum differential pseudorange deviation maxPRE that can be introduced by the distortion parameter of the EWF point. The broken line in the figure is the upper boundary of the distribution of all dots, indicating the performance confidence limit of the SQM device and method. Figure 6 As shown in the figure, if the maxPRE introduced by any EWF in the threat space for any configuration of the user receiver is not higher than the MERR of 1.61 meters, the required equivalent carrier-to-noise ratio of the signal should be no less than 40.3 dB-Hz.

[0142] Figure 7 The performance evaluation results of this evaluation embodiment are compared with the performance confidence bounds of the traditional multi-correlator-based SQM method. It can be seen that under the traditional multi-correlator-based SQM method, the equivalent carrier-to-noise ratio required for the signal to meet the DFMC SBAS requirements should be no less than 44.1dB-Hz.

[0143] Regarding the comparative evaluation of the two SQM methods, three aspects should be considered: first, under given MERR requirements and smoothing conditions, the lower the minimum equivalent carrier-to-noise ratio of the signal required to meet the DFMC SBAS requirements, the better the performance of the SQM method; second, under given smoothing conditions and minimum equivalent carrier-to-noise ratio of the signal, the smaller the maximum undetected differential error (MUDE), the better the performance of the SQM method; third, under given minimum equivalent carrier-to-noise ratio of the signal and MERR requirements, the fewer the requirements for smoothing (such as shorter statistical smoothing time or fewer reference station smoothing numbers), the better the performance of the SQM method.

[0144] In summary, this evaluation example demonstrates that:

[0145] (1) The present application discloses a signal quality monitoring device and method for the BeiDou satellite navigation system B1C navigation signal based on chip domain observations, which can obtain an SQM detection gain of about 3.8 dB compared to the traditional signal quality monitoring based on multiple correlators. It can be seen that the present application directly uses the sampled data before correlation processing to obtain chip domain observations, and then constructs detection statistics, which effectively avoids the weakening of local distortion caused by correlation processing and improves detection sensitivity; at the same time, it weakens the influence of signal modulation methods and broadens applicability;

[0146] (2) To meet the MERR requirement of 1.61 meters, it is calculated that if reference station smoothing is directly used based on the detection statistic of a single observation epoch, the number of reference stations required for this evaluation embodiment is 3, and the number of reference stations required for the traditional multi-correlator-based signal quality monitoring is 7; if 100 seconds of detection statistic smoothing is used first and then reference station smoothing is used, the number of reference stations required for this evaluation embodiment is 2, and the number of reference stations required for the traditional multi-correlator-based signal quality monitoring is 3. It can be seen that the present application can effectively reduce the requirements of the SQM method for smoothing, thereby improving the ability of the Beidou satellite navigation system B1C signal to meet the DFMC SBAS performance requirements.

[0147] It can be understood that the SQM evaluation method, threat space discretization scheme, receiver configuration scheme and detection comparison results are only used to illustrate the present application through this evaluation embodiment, and are not intended to limit the present application. Ordinary technicians in the relevant technical field can make various changes and modifications to the implementation methods and embodiments disclosed in the present application without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application, and the scope of patent protection of the present application is defined by the claims.

Claims

1. A signal quality monitoring device for Beidou satellite navigation system B1C navigation signal based on chip domain observation, which monitors each B1C navigation signal visible in orbit based on signal quality monitoring technology SQM, continuously and in real time performs signal quality monitoring on each monitored B1C navigation signal, obtains multiple original observations of the chip waveform of the B1C navigation signal, determines the degree of distortion of the chip waveform, and judges whether each B1C navigation signal is distorted.

2. The signal quality monitoring device according to claim 1, comprising: A monitoring receiver receives the B1C navigation signal and obtains a plurality of original observations of the chip waveform of the B1C navigation signal; A quality monitoring unit, performing smoothing and normalization processing on the plurality of original observations, and generating a plurality of real-time detection statistics according to the plurality of smoothed and normalized observations; A storage unit, providing a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds under a distortion-free B1C navigation signal; as well as The detection and determination unit determines whether the B1C navigation signal is distorted according to the deviations between the multiple real-time detection statistics and the multiple detection statistics nominal values, and the relationship between the deviations and the corresponding detection thresholds.

3. The signal quality monitoring device according to claim 2, wherein: The monitoring receiver further obtains an original estimated value of the B1C navigation signal amplitude and an altitude angle of the monitored satellite according to the received B1C navigation signal; The quality monitoring unit includes a preprocessing module and a detection statistic generation module, wherein the preprocessing module performs smoothing processing on the obtained multiple original observation quantities and the original estimated values ​​of the signal amplitude, and uses the smoothed signal amplitude estimated values ​​to normalize the multiple smoothed observation quantities to obtain multiple smoothed and normalized observation quantities, and the detection statistic generation module screens and combines the multiple smoothed and normalized observation quantities to generate multiple real-time detection statistics; The storage unit includes a pre-storage module and a calculation module, wherein the pre-storage module pre-stores detection statistic observation information obtained based on long-term observation of the undistorted B1C navigation signal, and the calculation module provides a plurality of detection statistic nominal values ​​corresponding to the elevation angle and a plurality of corresponding detection thresholds according to the observation information pre-stored in the pre-storage module and the elevation angle of the monitored satellite from the monitoring receiver; as well as The detection and determination unit determines a distortion detection factor according to deviations between multiple real-time detection statistics and multiple detection statistic nominal values ​​and corresponding detection thresholds, and determines whether the B1C navigation signal is distorted according to the distortion detection factor.

4. The signal quality monitoring device according to claim 3, wherein: The detection statistic observation information pre-stored in the pre-storage module of the storage unit includes a plurality of detection statistic nominal value fitting coefficients, a plurality of corresponding detection threshold fitting coefficients, and a normal distribution expansion coefficient obtained based on long-term observation of the distortion-free B1C navigation signal. The calculation module calculates a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds according to the coefficients pre-stored in the pre-storage module and the altitude angle of the monitored satellite from the monitoring receiver.

5. The signal quality monitoring device according to claim 3, wherein: The detection statistic observation information pre-stored in the pre-storage module of the storage unit includes a plurality of detection statistic nominal values ​​corresponding to different altitude angles obtained based on long-term observation of the undistorted B1C navigation signal and a plurality of corresponding detection thresholds; And the calculation module queries the pre-stored module according to the altitude angle of the monitored satellite from the monitoring receiver to obtain multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to the altitude angle.

6. A signal quality monitoring method for the Beidou satellite navigation system B1C navigation signal based on chip domain observations. Based on the signal quality monitoring technology SQM, each B1C navigation signal visible in orbit is monitored, and the signal quality of each monitored B1C navigation signal is continuously monitored in real time. By obtaining multiple original observations of the chip waveform of the B1C navigation signal, the distortion degree of the chip waveform is determined, and it is judged whether each B1C navigation signal is distorted.

7. The signal quality monitoring method according to claim 6, wherein: The method comprises: receiving a B1C navigation signal and obtaining a plurality of original observation quantities of a chip waveform of the B1C navigation signal; Smoothing and normalizing the multiple original observations, and generating multiple real-time detection statistics according to the multiple smoothed and normalized observations; Providing multiple detection statistic nominal values ​​and multiple corresponding detection thresholds under the undistorted B1C navigation signal; and Whether the B1C navigation signal is distorted is determined based on the deviations between the multiple real-time detection statistics and the multiple detection statistics nominal values, and the relationship between the deviations and the corresponding detection thresholds.

8. The signal quality monitoring method according to claim 7, wherein: The method comprises: Further obtaining an original estimated value of the B1C navigation signal amplitude and an altitude angle of the monitored satellite according to the received B1C navigation signal; Smoothing the obtained multiple original observations and original signal amplitude estimation values, normalizing the multiple smoothed observations using the smoothed signal amplitude estimation values ​​to obtain multiple smoothed normalized observations, screening and combining the multiple smoothed normalized observations to generate multiple real-time detection statistics; Pre-storing detection statistic observation information obtained based on long-term observation of undistorted B1C navigation signals, and providing a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds corresponding to the elevation angle according to the pre-stored observation information and the elevation angle of the monitored satellite; and A distortion detection factor is determined according to deviations between a plurality of real-time detection statistics and a plurality of detection statistics nominal values, and a corresponding detection threshold, and whether the B1C navigation signal is distorted is determined according to the distortion detection factor.

9. The signal quality monitoring method according to claim 8, wherein: The detection statistic observation information includes a plurality of detection statistic nominal value fitting coefficients, a plurality of corresponding detection threshold fitting coefficients, and a normal distribution expansion coefficient obtained based on long-term observation of the distortion-free B1C navigation signal. The method further includes calculating a plurality of detection statistic nominal values ​​and a plurality of corresponding detection thresholds based on the pre-stored coefficients and the altitude angle fitting of the monitored satellite.

10. The signal quality monitoring method according to claim 8, wherein: The pre-stored detection statistic observation information includes multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to different altitude angles obtained based on long-term observation of distortion-free B1C navigation signals. The method further includes obtaining multiple detection statistic nominal values ​​and multiple corresponding detection thresholds corresponding to the altitude angle based on the altitude angle query of the monitored satellite.

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