A method and device for quality decision of GNSS quantization conversion signal

Abstract

The application discloses a quality decision method and device for GNSS quantization conversion signals, and the method comprises the following steps: obtaining the quantization bit number of the GNSS quantization conversion signal to be decided; obtaining the optimal probability distribution of the GNSS quantization conversion signal to be decided according to the quantization bit number; calculating the first error rate between the GNSS quantization conversion signal to be decided and the optimal probability distribution; inputting the first error rate into the decision function corresponding to the GNSS quantization conversion signal to be decided; and obtaining the error rate by calculation; and performing quality decision on the GNSS quantization conversion signal to be decided according to the error rate. The embodiment of the application can effectively improve the quality decision precision of the GNSS quantization conversion signal.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for determining the quality of GNSS quantized conversion signals. Background Technology

[0002] When sampling GNSS signals, a high quantization bit depth is often used to obtain more useful information, enhance the signal-to-noise ratio, and suppress various potential electromagnetic interferences. However, a high quantization bit depth places higher demands on the performance of the GNSS receiver, significantly reducing signal acquisition and tracking speed. Some special-purpose receivers, such as software receivers that prioritize fast acquisition using bitwise acquisition algorithms, may only support GNSS signals with a specific quantization bit depth. Therefore, to meet the needs of GNSS signals with different quantization bit depths, it is often necessary to convert the quantization bit depth of the GNSS signal.

[0003] When converting GNSS quantized signals, there are methods such as the optimal probability distribution method and the method of retaining only the highest quantization bits. Different quantization thresholds are used to perform the quantization conversion. After successful conversion, it is necessary to determine whether sufficient useful information has been retained and whether the quantization conversion result meets the requirements.

[0004] Current methods for judging the quality of GNSS quantized signals involve calculating the optimal probability distribution function at a certain false alarm rate, then continuously adjusting high and low thresholds while simultaneously calculating the proportion of each quantized value corresponding to different quantization bits. This process continues until the optimal probability distribution at that quantization bit depth is satisfied, at which point the quantization is considered successful. This method is complex, time-consuming, and fails to consider special satellite signal conditions such as spoofing, multipath propagation, satellite signal anomalies, and electromagnetic interference, resulting in significant loss of useful information and poor accuracy in judging the quality of GNSS quantized signals.

[0005] As can be seen from the above, the existing methods for determining the quality of GNSS quantized conversion signals have the problem of low accuracy. Summary of the Invention

[0006] This invention provides a method and apparatus for quality determination of GNSS quantized conversion signals, which improves the accuracy of quality determination for GNSS quantized conversion signals.

[0007] The first aspect of this application provides a method for determining the quality of GNSS quantized converted signals, including:

[0008] Obtain the quantization bit depth of the GNSS quantized conversion signal to be decided, and calculate the optimal probability distribution of the GNSS quantized conversion signal to be decided based on the quantization bit depth.

[0009] Calculate the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution, input the first error rate into the decision function corresponding to the GNSS quantized conversion signal to be decided, and calculate the error rate.

[0010] The quality of the GNSS quantized conversion signal to be judged is determined based on the error rate.

[0011] In one possible implementation of the first aspect, the process of generating the decision function corresponding to the GNSS quantized conversion signal to be decided is as follows:

[0012] Calculate the second and third error rates between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively.

[0013] Substituting the second and third error rates into the Neman Pearson auxiliary function, we obtain the first and second functions.

[0014] The first function and the second function are trained until they meet the preset conditions. The training ends and the decision function corresponding to the GNSS quantization conversion signal to be decided is generated.

[0015] In one possible implementation of the first aspect, the process of obtaining the ideal average proportion and the red line proportion is as follows:

[0016] Based on the GNSS quantization conversion signal to be decided, determine the count values ​​of each positive and negative amplitude value of the GNSS quantization conversion signal to be decided;

[0017] The proportion of each amplitude count value is calculated based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length.

[0018] Based on the preset quality conditions, the ideal average proportion and the red line proportion are obtained from the proportion of each amplitude count value; whereby the ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; and the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions.

[0019] In one possible implementation of the first aspect, the quality judgment of the GNSS quantized conversion signal to be judged is performed based on the error rate, specifically as follows:

[0020] When the error rate is less than the first preset threshold, the GNSS quantization conversion signal to be judged is deemed invalid.

[0021] When the error rate is greater than or equal to the first preset threshold and less than the second preset threshold, the GNSS quantization conversion signal to be judged is deemed qualified.

[0022] When the error rate is greater than or equal to the second preset threshold, the GNSS quantization conversion signal to be judged is deemed unqualified.

[0023] In one possible implementation of the first aspect, the first function and the second function satisfy a preset condition, specifically:

[0024] The first function is specifically:

[0025] Q p =P′1(e)+ΔpP2(e);

[0026] The second function is specifically:

[0027] Q r =P′1(e)+ΔrP2(e);

[0028] Among them, Q p Let Q be the first function. r Let P′1(e) be the preset false alarm rate, P2(e) be the decision error rate, Δp be the second error rate, and Δr be the third error rate.

[0029] Using the decision error rate in the first function and the second function as independent variables, calculate the partial derivative and second partial derivative of the decision error rate. If the decision error rate meets the preset range and the first function and the second function are equal to the third preset threshold, it is determined that the first function and the second function meet the preset conditions.

[0030] In one possible implementation of the first aspect, the decision function corresponding to the GNSS quantized conversion signal to be decided is generated as follows:

[0031] Using the decision error rates of the first and second functions after training as dependent variables, and the first error rate as the independent variable, the inverse function of the Neman Pearson auxiliary function is calculated to generate the decision function corresponding to the GNSS quantized conversion signal to be decided, as shown below:

[0032] P = Q min -P1(e) / μ;

[0033] Where P is the decision function corresponding to the GNSS quantized conversion signal to be decided, and Q... min The third preset threshold is P1(e), which is the false alarm rate, and μ is the first error rate.

[0034] One possible implementation of the first aspect also includes:

[0035] The first preset threshold and the second preset threshold are calculated based on the first function and the second function after training.

[0036] A second aspect of this application provides a quality decision device for GNSS quantization conversion signals, comprising: a first calculation module, a second calculation module, and a decision module;

[0037] The first calculation module is used to obtain the number of quantization bits of the GNSS quantization conversion signal to be decided, and to calculate the optimal probability distribution of the GNSS quantization conversion signal to be decided based on the number of quantization bits.

[0038] The second calculation module is used to calculate the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution. The first error rate is input into the decision function corresponding to the GNSS quantized conversion signal to be decided, and the error rate is calculated.

[0039] The decision module is used to make quality decisions on the GNSS quantized conversion signals to be decided based on the error rate.

[0040] In one possible implementation of the second aspect, the process of generating the decision function corresponding to the GNSS quantized conversion signal to be decided is as follows:

[0041] Calculate the second and third error rates between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively.

[0042] Substituting the second and third error rates into the Neman Pearson auxiliary function, we obtain the first and second functions.

[0043] The first function and the second function are trained until they meet the preset conditions. The training ends and the decision function corresponding to the GNSS quantization conversion signal to be decided is generated.

[0044] In one possible implementation of the second aspect, the process of obtaining the ideal average proportion and the red line proportion is as follows:

[0045] Based on the GNSS quantization conversion signal to be decided, determine the count values ​​of each positive and negative amplitude value of the GNSS quantization conversion signal to be decided;

[0046] The proportion of each amplitude count value is calculated based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length.

[0047] Based on the preset quality conditions, the ideal average proportion and the red line proportion are obtained from the proportion of each amplitude count value; whereby the ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; and the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions.

[0048] Compared to existing technologies, this invention provides a method and apparatus for quality determination of GNSS quantized conversion signals. The method includes: obtaining the quantization bit depth of the GNSS quantized conversion signal to be determined; calculating the optimal probability distribution of the GNSS quantized conversion signal to be determined based on the quantization bit depth; calculating a first error rate between the GNSS quantized conversion signal to be determined and the optimal probability distribution; inputting the first error rate into a decision function corresponding to the GNSS quantized conversion signal to be determined; and calculating an error rate; and making a quality determination of the GNSS quantized conversion signal to be determined based on the error rate.

[0049] Its beneficial effects are as follows: In this embodiment of the invention, after calculating the optimal probability distribution of the GNSS quantized conversion signal to be decided based on the quantization bit depth of the GNSS quantized conversion signal to be decided, the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution is further calculated. The first error rate is then input into the decision function corresponding to the GNSS quantized conversion signal to be decided to calculate the error rate. Finally, the quality decision of the GNSS quantized conversion signal to be decided is made based on the error rate. This avoids the problem of loss of useful information caused by the method of continuously cyclically adjusting the high and low thresholds in the prior art during the quality decision of the GNSS quantized conversion signal, and can effectively improve the accuracy of the quality decision of the GNSS quantized conversion signal.

[0050] Furthermore, in the GNSS quantization conversion signal quality judgment, the embodiments of the present invention can control the error rate to the lowest level while limiting the false alarm rate; moreover, the embodiments of the present invention can not only determine whether the quality of the GNSS quantization conversion signal meets the requirements, but also compare the error rate with the first preset threshold and the second preset threshold, thereby judging the degree of signal quality, and further improving the accuracy of the quality judgment of the GNSS quantization conversion signal.

[0051] Finally, in the GNSS quantization conversion signal quality judgment, the calculation process is simple and time-saving. Furthermore, considering special satellite signals under conditions such as spoofing, multipath propagation, satellite signal anomalies, and electromagnetic interference, the present invention ensures both efficiency and accuracy in the GNSS quantization conversion signal quality judgment. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart illustrating the quality determination of a GNSS quantization conversion signal according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the structure of a GNSS quantization conversion signal quality determination device provided in an embodiment of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Reference Figure 1 This is a flowchart illustrating a quality determination method for GNSS quantization conversion signals according to an embodiment of the present invention, including S101-S103:

[0056] S101: Obtain the quantization bit depth of the GNSS quantization conversion signal to be decided, and calculate the optimal probability distribution of the GNSS quantization conversion signal to be decided based on the quantization bit depth.

[0057] S102: Calculate the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution, input the first error rate into the decision function corresponding to the GNSS quantized conversion signal to be decided, and calculate the error rate.

[0058] In this embodiment, the generation process of the decision function corresponding to the GNSS quantization conversion signal to be decided is specifically as follows:

[0059] Calculate the second and third error rates between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively.

[0060] Substituting the second error rate and the third error rate into the Neman Pearson auxiliary function respectively, we obtain the first function and the second function;

[0061] The first function and the second function are trained until they meet preset conditions. The training ends and a decision function corresponding to the GNSS quantization conversion signal to be decided is generated.

[0062] In one specific embodiment, the first function and the second function satisfy a preset condition, specifically:

[0063] The first function is specifically:

[0064] Q p =P′1(e)+ΔpP2(e);

[0065] The second function is specifically:

[0066] Q r =P′1(e)+ΔrP2(e);

[0067] Among them, Q p For the first function, Q r The second function is defined as follows: P′1(e) is the preset false alarm rate, P2(e) is the decision error rate, Δp is the second error rate, and Δr is the third error rate.

[0068] Using the decision error rate in the first function and the second function as independent variables, calculate the partial derivative and second partial derivative of the decision error rate. When the decision error rate meets a preset range and the first function and the second function are equal to a third preset threshold, determine that the first function and the second function meet the preset conditions.

[0069] In one specific embodiment, the decision function for generating the GNSS quantization conversion signal to be decided is specifically as follows:

[0070] Using the decision error rate of the first function and the second function after training as the dependent variable, and the first error rate as the independent variable, the inverse function of the Neman Pearson auxiliary function is calculated to generate the decision function corresponding to the GNSS quantized conversion signal to be decided, as shown below:

[0071] P = Q min -P1(e) / μ;

[0072] Where P is the decision function corresponding to the GNSS quantized conversion signal to be decided, and Q... min The third preset threshold is defined as P1(e), where P1(e) is the false alarm rate and μ is the first error rate.

[0073] Furthermore, P′1(e) is the preset decision false alarm rate, specifically referring to a certain false alarm rate; P1(e) is the decision false alarm rate, a conceptual symbol. P′1(e) and P1(e) are related as specific and general.

[0074] In one specific embodiment, it further includes:

[0075] The first preset threshold and the second preset threshold are calculated based on the first function and the second function after training, respectively.

[0076] In one specific embodiment, the process of obtaining the ideal average proportion and the red line proportion is as follows:

[0077] Based on the GNSS quantization conversion signal to be decided, determine the count value of each positive and negative amplitude value of the GNSS quantization conversion signal to be decided;

[0078] Based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length, the proportion of each amplitude count value is calculated;

[0079] Based on preset quality conditions, the ideal average proportion and the red line proportion are obtained from the proportion of each amplitude count value; wherein, the ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; and the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions.

[0080] S103: Make a quality judgment on the GNSS quantized conversion signal to be judged based on the error rate.

[0081] In this embodiment, the quality judgment of the GNSS quantization conversion signal to be judged based on the error rate specifically includes:

[0082] When the error rate is less than a first preset threshold, the GNSS quantization conversion signal to be decided is determined to be invalid.

[0083] When the error rate is greater than or equal to the first preset threshold and less than the second preset threshold, the GNSS quantization conversion signal to be decided is deemed qualified; when the error rate is greater than or equal to the second preset threshold, the GNSS quantization conversion signal to be decided is deemed unqualified.

[0084] Furthermore, the closer the error rate is to the second preset threshold, the higher the quality of the GNSS quantization conversion signal to be decided; the closer the error rate is to the first preset threshold, the lower the quality of the GNSS quantization conversion signal to be decided.

[0085] In a preferred embodiment, the following steps are included:

[0086] Step 1: Preprocess the training samples of the GNSS intermediate frequency quantization conversion signal (i.e., GNSS quantization conversion signal) to obtain the total number of samples, the number of quantization bits, the corresponding value range, and the number of samples and the proportion of each value.

[0087] The preprocessing of GNSS intermediate frequency quantization conversion signal training samples includes the following sub-steps:

[0088] 1.1 Count the total number of training samples with different quantization bit depths, distinguish the samples with different quantization bit depths, and extract the quantization conversion signals of the same length as required for each sample. Control the quantization conversion signals to have the same signal sampling rate and intermediate frequency data length.

[0089] 1.2 For GNSS intermediate frequency quantization conversion signal samples with different quantization bits, determine the possible positive and negative amplitude values. Each time a positive or negative amplitude value appears, the corresponding count value is incremented by 1. The number of times each possible value appears is counted and recorded as the amplitude count value.

[0090] 1.3 By dividing the count values ​​of each positive and negative amplitude by the intermediate frequency data length, the ratio of each amplitude count value to the total signal count value is calculated and denoted as the proportion p of each amplitude count value. Finally, the average proportion P of each amplitude count value of each sample with different quantization bits that meet the preset quality conditions is obtained, and the average proportion R of each amplitude count value of each sample with different quantization bits that do not meet the quality requirements is obtained.

[0091] Step 2: For different quantization bits, constrain the false alarm probability to a fixed value, construct the Nieman-Pearson auxiliary function Q, calculate the minimum value of Q using training samples to minimize the false negative probability, and obtain the decision function and error rate threshold (the error rate threshold includes: a first preset threshold M). r Second preset threshold M p ).

[0092] The process of constructing the Neman Pearson auxiliary function Q, calculating its minimum value using training samples to minimize the false negative probability, and obtaining the decision function includes the following sub-steps:

[0093] 2.1 Construct the Neman Pearson auxiliary function Q = P1(e) + μP2(e), where the unknowns that need to be determined are the false alarm rate P1(e), the decision error rate P2(e), and the error rate μ of the GNSS quantization conversion signal;

[0094] 2.2 By inputting an acceptable preset false alarm rate P′1(e) into the system, and under the condition that the preset false alarm rate P′1(e) is equal to a constant, by inputting quantized conversion signal samples, and substituting the proportion of each amplitude count value of samples with different quantization bits according to the preset quality conditions, the average proportion P and the red line proportion R are calculated respectively. The second error rate Δp between P and the optimal probability distribution λ, and the third error rate Δr between R and the optimal probability distribution λ are calculated respectively. Wherein, when the false alarm rate of the proportion of amplitude count values ​​is less than P′1(e), it is determined that the proportion of amplitude count values ​​meets the preset quality conditions; when the false alarm rate of the proportion of amplitude count values ​​is greater than or equal to P′1(e), it is determined that the proportion of amplitude count values ​​does not meet the preset quality conditions.

[0095] Where P = n p / N, R=n R / N, Δp=|λ-P| / λ, Δr=|λ-R| / λ, n pLet n be the ideal count value representing the total sample size N. R This represents the red line count value when the total number of samples is N;

[0096] 2.3 Substituting Δp and Δr into the error rate μ of the GNSS quantization conversion signal, respectively, yields two functions of Q, including the first function Q. p The second function Qr, i.e.:

[0097] Q p =P′1(e)+ΔpP2(e);

[0098] Q r =P′1(e)+ΔrP2(e);

[0099] Next, taking the decision error rate P2(e) as the independent variable, calculate the partial derivative and second-order partial derivative of the decision error rate P2(e). Under the premise that the decision error rate satisfies the preset range, when Q reaches its minimum value Q... min When the error rate equals the third preset threshold, the decision function training is considered complete, and the current decision error rate is recorded as the first preset threshold M. r Second preset threshold M p :

[0100] M p =Q p -P′1(e) / Δp;

[0101] M r =Q r -P′1(e) / Δr.

[0102] 2.4 Using the decision error rate P2(e) as the dependent variable and the error rate μ of the GNSS quantization conversion signal as the independent variable, the inverse function of the Q-auxiliary function is calculated to obtain the final quality decision function, as shown below:

[0103] P = Q min -P1(e) / μ ;

[0104] Where P is the decision value, Q min The third preset threshold is defined as P1(e), which is the acceptable false alarm rate for any input decision, and μ is the error rate (i.e., the first error rate) of the GNSS quantized conversion signal to be decided. The closer the decision value P is to the second preset threshold M, the better. p The higher the signal quality, the better; once it falls below the first preset threshold M... r The signal is then deemed invalid.

[0105] Step 3: Process the GNSS quantization conversion signal that needs to be decided in real time. The quantization bit depth of the signal to be decided is obtained directly through input. The optimal AGC gain, i.e. the case with the minimum signal-to-noise ratio loss, is calculated. The optimal probability distribution of the signal is then derived.

[0106] Step 4: Calculate the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution. Each GNSS quantized conversion signal has a different quantization bit depth corresponding to a different decision function. Substitute the first error rate into the decision function for the corresponding quantization bit depth to obtain the corresponding error rate. When the error rate is lower than the second preset threshold M... p If the error rate exceeds the second preset threshold M, the quality of the GNSS quantized conversion signal to be judged is deemed acceptable. p If the error rate is close to the second preset threshold M, the quality of the GNSS quantized conversion signal to be judged is deemed unqualified. p The higher the quality of the GNSS quantized conversion signal to be decided, the closer the error rate is to the first preset threshold M. r The lower the quality of the GNSS quantization conversion signal to be decided, the lower the final decision result will be.

[0107] For a further GNSS quantization conversion signal quality determination device, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the structure of a quality decision device for GNSS quantization conversion signal according to an embodiment of the present invention, including: a first calculation module, a second calculation module and a decision module;

[0108] The first calculation module 201 is used to obtain the quantization bit number of the GNSS quantization conversion signal to be decided, and to calculate the optimal probability distribution of the GNSS quantization conversion signal to be decided based on the quantization bit number.

[0109] The second calculation module 202 is used to calculate the first error rate between the GNSS quantization conversion signal to be decided and the optimal probability distribution, and input the first error rate into the decision function corresponding to the GNSS quantization conversion signal to be decided to calculate the error rate;

[0110] The decision module 203 is used to make a quality decision on the GNSS quantization conversion signal to be decided based on the error rate.

[0111] In this embodiment, the generation process of the decision function corresponding to the GNSS quantization conversion signal to be decided is specifically as follows:

[0112] Calculate the second and third error rates between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively.

[0113] Substituting the second error rate and the third error rate into the Neman Pearson auxiliary function respectively, we obtain the first function and the second function;

[0114] The first function and the second function are trained until they meet preset conditions. The training ends and a decision function corresponding to the GNSS quantization conversion signal to be decided is generated.

[0115] In this embodiment, the process of obtaining the ideal average proportion and the red line proportion is as follows:

[0116] Based on the GNSS quantization conversion signal to be decided, determine the count value of each positive and negative amplitude value of the GNSS quantization conversion signal to be decided;

[0117] Based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length, the proportion of each amplitude count value is calculated;

[0118] Based on preset quality conditions, the ideal average proportion and the red line proportion are obtained from the proportion of each amplitude count value; wherein, the ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; and the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions.

[0119] In this embodiment, the quality judgment of the GNSS quantization conversion signal to be judged based on the error rate specifically includes:

[0120] When the error rate is less than a first preset threshold, the GNSS quantization conversion signal to be decided is determined to be invalid.

[0121] When the error rate is greater than or equal to the first preset threshold and less than the second preset threshold, the GNSS quantization conversion signal to be judged is deemed qualified.

[0122] When the error rate is greater than or equal to the second preset threshold, the GNSS quantization conversion signal to be judged is deemed unqualified.

[0123] In one specific embodiment, the first function and the second function satisfy a preset condition, specifically:

[0124] The first function is specifically:

[0125] Q p =P′1(e)+ΔpP2(e);

[0126] The second function is specifically:

[0127] Q r=P′1(e)+ΔrP2(e);

[0128] Among them, Q p For the first function, Q r The second function is defined as follows: P′1(e) is the preset false alarm rate, P2(e) is the decision error rate, Δp is the second error rate, and Δr is the third error rate.

[0129] Using the decision error rate in the first function and the second function as independent variables, calculate the partial derivative and second partial derivative of the decision error rate. When the decision error rate meets a preset range and the first function and the second function are equal to a third preset threshold, determine that the first function and the second function meet the preset conditions.

[0130] In one specific embodiment, the decision function for generating the GNSS quantization conversion signal to be decided is specifically as follows:

[0131] Using the decision error rate of the first function and the second function after training as the dependent variable, and the first error rate as the independent variable, the inverse function of the Neman Pearson auxiliary function is calculated to generate the decision function corresponding to the GNSS quantized conversion signal to be decided, as shown below:

[0132] P = Q min -P1(e) / μ;

[0133] Where P is the decision function corresponding to the GNSS quantized conversion signal to be decided, and Q... min The third preset threshold is defined as P1(e), where P1(e) is the false alarm rate and μ is the first error rate.

[0134] In one specific embodiment, it further includes:

[0135] The first preset threshold and the second preset threshold are calculated based on the first function and the second function after training, respectively.

[0136] In this embodiment of the invention, the first calculation module obtains the quantization bit depth of the GNSS quantized conversion signal to be decided, and calculates the optimal probability distribution of the GNSS quantized conversion signal to be decided based on the quantization bit depth; the second calculation module calculates the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution, and inputs the first error rate into the decision function corresponding to the GNSS quantized conversion signal to be decided to calculate the error rate; the decision module makes a quality decision on the GNSS quantized conversion signal to be decided based on the error rate.

[0137] In this embodiment of the invention, after calculating the optimal probability distribution of the GNSS quantized conversion signal to be decided based on the quantization bit depth, a first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution is further calculated. The first error rate is then input into the decision function corresponding to the GNSS quantized conversion signal to be decided to calculate the error rate. Finally, the quality of the GNSS quantized conversion signal to be decided is determined based on the error rate. This avoids the problem of loss of useful information caused by the method of continuously adjusting high and low thresholds in the prior art during the quality determination process of GNSS quantized conversion signals, and can effectively improve the accuracy of quality determination of GNSS quantized conversion signals.

[0138] Furthermore, in the GNSS quantization conversion signal quality judgment, the embodiments of the present invention can control the error rate to the lowest level while limiting the false alarm rate; moreover, the embodiments of the present invention can not only determine whether the quality of the GNSS quantization conversion signal meets the requirements, but also compare the error rate with the first preset threshold and the second preset threshold, thereby judging the degree of signal quality, and further improving the accuracy of the quality judgment of the GNSS quantization conversion signal.

[0139] Finally, in the GNSS quantization conversion signal quality judgment, the calculation process is simple and time-saving. Furthermore, considering special satellite signals under conditions such as spoofing, multipath propagation, satellite signal anomalies, and electromagnetic interference, the present invention ensures both efficiency and accuracy in the GNSS quantization conversion signal quality judgment.

[0140] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method of quality decision of a GNSS quantized conversion signal, characterized in that, include: Obtain the quantization bit depth of the GNSS quantization conversion signal to be decided, and calculate the optimal probability distribution of the GNSS quantization conversion signal to be decided based on the quantization bit depth. Calculate the first error rate between the GNSS quantized conversion signal to be decided and the optimal probability distribution, and input the first error rate into the decision function corresponding to the GNSS quantized conversion signal to be decided to calculate the error rate; The specific process for generating the decision function corresponding to the GNSS quantization conversion signal to be decided is as follows: Calculate the second error rate and the third error rate between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively; substitute the second error rate and the third error rate into the Neman Pearson auxiliary function to obtain the first function and the second function; train the first function and the second function until the first function and the second function meet the preset conditions, the training ends and the decision function corresponding to the GNSS quantization conversion signal to be decided is generated; The process of obtaining the ideal average proportion and the red line proportion specifically involves: determining the count values ​​of each positive and negative amplitude value of the GNSS quantized conversion signal to be decided based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length; calculating the proportion of each amplitude count value based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length; and obtaining the ideal average proportion and the red line proportion from the proportion of each amplitude count value according to preset quality conditions. The ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions. The quality of the GNSS quantized conversion signal to be decided is determined based on the error rate.

2. The method of claim 1, wherein the quality of the GNSS quantized conversion signal is determined by, The quality judgment of the GNSS quantized conversion signal to be judged based on the error rate specifically includes: When the error rate is less than a first preset threshold, the GNSS quantization conversion signal to be decided is determined to be invalid. When the error rate is greater than or equal to the first preset threshold and less than the second preset threshold, the GNSS quantization conversion signal to be judged is deemed qualified. When the error rate is greater than or equal to the second preset threshold, the GNSS quantization conversion signal to be judged is deemed unqualified.

3. The method of claim 2, wherein the quality of the GNSS quantized conversion signal is determined by, The first function and the second function satisfy preset conditions, specifically: The first function is specifically: ； The second function is specifically: ； Among them, Q p For the first function, Q r For the second function, To preset the false alarm rate, To determine the error rate, This is the second error rate. The third error rate; Using the decision error rate in the first function and the second function as independent variables, calculate the partial derivative and second partial derivative of the decision error rate. When the decision error rate meets a preset range and the first function and the second function are equal to a third preset threshold, determine that the first function and the second function meet the preset conditions.

4. The method of claim 3, wherein the quality of the GNSS quantized conversion signal is determined by, The decision function for generating the GNSS quantized conversion signal to be decided is specifically as follows: Using the decision error rate of the first function and the second function after training as the dependent variable, and the first error rate as the independent variable, the inverse function of the Neman Pearson auxiliary function is calculated to generate the decision function corresponding to the GNSS quantized conversion signal to be decided, as shown below: ； Wherein, P is a decision function corresponding to the GNSS quantization conversion signal to be judged, Q min is the third preset threshold, is the false alarm rate of decision, is the first error rate.

5. The method of claim 4, wherein the quality of the GNSS quantized conversion signal is determined by, Also includes: The first preset threshold and the second preset threshold are calculated based on the first function and the second function after training, respectively.

6. A quality determination device for GNSS quantization conversion signals, characterized in that, include: The system comprises a first calculation module, a second calculation module, and a decision module. The first calculation module is used to obtain the number of quantization bits of the GNSS quantization conversion signal to be decided, and to calculate the optimal probability distribution of the GNSS quantization conversion signal to be decided based on the number of quantization bits. The second calculation module is used to calculate the first error rate between the GNSS quantization conversion signal to be decided and the optimal probability distribution, and input the first error rate into the decision function corresponding to the GNSS quantization conversion signal to be decided to calculate the error rate; The specific process for generating the decision function corresponding to the GNSS quantization conversion signal to be decided is as follows: Calculate the second error rate and the third error rate between the ideal average proportion and the red line proportion and the optimal probability distribution, respectively; substitute the second error rate and the third error rate into the Neman Pearson auxiliary function to obtain the first function and the second function; train the first function and the second function until the first function and the second function meet the preset conditions, the training ends and the decision function corresponding to the GNSS quantization conversion signal to be decided is generated; The process of obtaining the ideal average proportion and the red line proportion specifically involves: determining the count values ​​of each positive and negative amplitude value of the GNSS quantized conversion signal to be decided based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length; calculating the proportion of each amplitude count value based on the count values ​​of each positive and negative amplitude value and the intermediate frequency data length; and obtaining the ideal average proportion and the red line proportion from the proportion of each amplitude count value according to preset quality conditions. The ideal average proportion represents the proportion of each amplitude count value that meets the preset quality conditions; the red line proportion represents the proportion of each amplitude count value that does not meet the preset quality conditions. The decision module is used to make a quality decision on the GNSS quantization conversion signal to be decided based on the error rate.

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