GNSS pseudocode estimation method, device and equipment based on conditional entropy

Abstract

The present application relates to a GNSS pseudocode estimation method, device and equipment based on conditional entropy. Code chip uncertainty is quantified by conditional entropy, high entropy code chips are processed preferentially, and the correction process is dynamically optimized by using the monotonic relationship between entropy and signal-to-noise ratio. Under low signal-to-noise ratio, the robustness to noise interference is enhanced, and the reliability of pseudocode estimation is improved; at high signal-to-noise ratio, it quickly converges to a low entropy state and improves synchronization accuracy. Through correlation iterative correction and priority adaptive adjustment, it can effectively cope with complex channel environments, significantly enhance the reliability and efficiency of pseudocode synchronization, reduce bit error rate, provide high-precision signal processing capabilities for satellite navigation, wireless communication and other spread spectrum systems, improve signal capture and tracking performance, and realize full process optimization from uncertainty quantification to dynamic correction, which has broad application value and technical innovation.

Description

Technical Field

[0001] The present application relates to the field of navigation signal processing technology, and in particular to a GNSS pseudocode estimation method, apparatus, and device based on conditional entropy. Background Art

[0002] With the widespread adoption of non-cooperative GNSS signals in civilian applications such as commercial service signal authentication and low-orbit navigation signals, their quality monitoring has become a key technology for safeguarding these services. The essential difference between non-cooperative and cooperative GNSS signal quality monitoring lies in the ability to obtain complete information about the authorized signal system. Non-cooperative signal quality monitoring requires evaluation and analysis based solely on received signals, without relying on prior GNSS signal information. Consequently, non-cooperative signal quality monitoring faces challenges such as non-disclosure of signal systems, complex modulation schemes, and high real-time requirements.

[0003] The analysis of unknown pseudo-random codes is the core technology for non-cooperative signal estimation. Existing PN sequence estimation for DSSS signals is based on the relationship between the pseudo-code period and the information code period, and is mainly divided into short codes, periodic long codes, and aperiodic long codes. Spreading code estimation for aperiodic long code direct sequence spread spectrum signals is typically solved by reconstructing the signal's periodicity or leveraging high-order autocorrelations. However, the pseudo-random codes of non-non-cooperative GNSS signals lack aperiodic correlation characteristics and instead implement long code encryption through polynomial coefficient variations. Therefore, methods from the communications field are not applicable to GNSS signals. Pseudo-code sequence estimation is a core monitoring technology, and its development evolves with the modulation scheme. If traditional BPSK / QPSK modulation schemes are used, the orthogonal relationship between the two can be exploited to separate the I and Q branch signals and directly determine the pseudo-code sequence symbols of the corresponding branch based on the positive and negative polarity of the I and Q branch baseband signals. In modern navigation systems, multiple military and civilian code signals are transmitted and synthesized using frequency multiplexing. Signal recovery can be achieved using methods such as constellation templates, influenced by factors such as power allocation and modulation scheme. But no matter which technology is used, its pseudo-random code recovery performance is limited by the signal-to-noise ratio.

[0004] Reducing the bit error rate (BER) of monitoring signal estimation to effectively analyze non-cooperative signals is a key requirement for GNSS pseudo-code sequence estimation. The BER of monitoring signals decreases monotonically with the signal-to-noise ratio (SNR), and the theoretical limit of pseudo-code sequence estimation performance is clear. Therefore, high-gain antennas can be used to improve the SNR of received signals. However, further reducing the BER through high-gain phased arrays is increasingly costly: every 3dB increase in beam gain requires doubling the antenna aperture. Therefore, the cost of receiving navigation signal antennas limits the current theoretical limit of decoding. Optimizing pseudo-code sequence estimation technology is necessary to break through the BER limit imposed by the SNR, gradually reduce the BER of non-cooperative signals, and achieve even lower BER BER estimation. Summary of the Invention

[0005] Based on this, it is necessary to provide a GNSS pseudocode estimation method, device and equipment based on conditional entropy to address the above technical problems.

[0006] A conditional entropy GNSS pseudocode estimation method, the method comprising:

[0007] Estimate the noise variance of the received signal;

[0008] Constructing a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculating a pseudo-code posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic;

[0009] Calculating the conditional entropy of a single chip based on the pseudocode posterior probability;

[0010] The chips are sorted in descending order according to the conditional entropy, and the priority of chip correction is determined based on the sorting. For the sorted chips, a local pseudocode sequence is generated, and a local signal is generated based on the local pseudocode sequence. A correlation value with a related signal is calculated based on the local signal, and the chips are updated based on the correlation value so that the entropy of all the chips is ultimately less than a threshold, and a pseudocode sequence is output.

[0011] In one embodiment, the method further includes: constructing a decision confidence level according to the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index:

[0012] ;

[0013] in, represents the decision confidence of the mth chip, Indicates the chip length, , Represents the real signal after baseband demodulation.

[0014] In one embodiment, the method further includes: estimating the pseudo code symbol to be Bayesian formula:

[0015] ;

[0016] Assume that the received signal statistics The conditional distribution is:

[0017] ;

[0018] Substitute into the Bayesian formula:

[0019] ;

[0020] Let quantized signal-to-noise ratio , then the pseudocode posterior probability after Bayes' formula simplification is:

[0021] ;

[0022] The posterior probability of the pseudo code with the estimated symbol of -1 is:

[0023] .

[0024] In one embodiment, the method further includes: single chip conditional entropy:

[0025] ;

[0026] Confidence in judgment Determined to a specific value When the conditions For a specific value, the single chip conditional entropy degenerates to:

[0027] ;

[0028] Since the code value is ,set up:

[0029] ;

[0030] The single chip conditional entropy is simplified to:

[0031] .

[0032] In one embodiment, the method further includes: sorting the chips in descending order of conditional entropy as follows:

[0033] ;

[0034] in, Indicates returning the sorted index sequence. Indicates the The chip index corresponding to the priority level, , Indicates the maximum number of correlation corrections to be performed in priority order.

[0035] In one embodiment, it further includes:

[0036] In one embodiment, the method further includes: Priority chips , generating the The code chips are The two local pseudocode sequences are:

[0037] ;

[0038] in , Indicates the Priority chips The local pseudo code sequence, represents the original local pseudo code sequence;

[0039] Through pseudo code modulation and carrier modulation, the local signal is generated according to the local pseudo code sequence:

[0040] ;

[0041] ;

[0042] in, Indicates the Priority chips Take the local signal at +1, Indicates the Priority chips The local signal when it is -1;

[0043] Calculate the correlation value between the local signal and the received signal according to the local signal:

[0044] ;

[0045] ;

[0046] In one embodiment, the further embodiment further comprises: The correlation value of the chip Big time, The code chips are , otherwise .

[0047] A GNSS pseudocode estimation device based on conditional entropy, comprising:

[0048] A noise calculation module is used to estimate the noise variance of the received signal;

[0049] a posterior probability calculation module, configured to construct a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculate a pseudocode posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic;

[0050] A conditional entropy calculation module, configured to calculate the conditional entropy of a single chip based on the pseudocode posterior probability;

[0051] An estimation module is configured to sort the chips in descending order according to the conditional entropy, determine the priority of chip correction based on the sorting, generate a local pseudocode sequence for the sorted chips, generate a local signal based on the local pseudocode sequence, calculate a correlation value with a related signal based on the local signal, update the chips based on the correlation value, so that the entropy of all chips is ultimately less than a threshold, and output a pseudocode sequence.

[0052] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program:

[0053] Estimate the noise variance of the received signal;

[0054] Constructing a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculating a pseudo-code posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic;

[0055] Calculating the conditional entropy of a single chip based on the pseudocode posterior probability;

[0056] The chips are sorted in descending order according to the conditional entropy, and the priority of chip correction is determined based on the sorting. For the sorted chips, a local pseudocode sequence is generated, and a local signal is generated based on the local pseudocode sequence. A correlation value with a related signal is calculated based on the local signal, and the chips are updated based on the correlation value so that the entropy of all the chips is ultimately less than a threshold, and a pseudocode sequence is output.

[0057] A computer-readable storage medium having a computer program stored thereon, wherein the computer program is used by a processor to execute the steps of the method:

[0058] Estimate the noise variance of the received signal;

[0059] Constructing a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculating a pseudo-code posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic;

[0060] Calculating the conditional entropy of a single chip based on the pseudocode posterior probability;

[0061] The chips are sorted in descending order according to the conditional entropy, and the priority of chip correction is determined based on the sorting. For the sorted chips, a local pseudocode sequence is generated, and a local signal is generated based on the local pseudocode sequence. A correlation value with a related signal is calculated based on the local signal, and the chips are updated based on the correlation value so that the entropy of all the chips is ultimately less than a threshold, and a pseudocode sequence is output.

[0062] The above-mentioned GNSS pseudo-code estimation method, device and equipment based on conditional entropy quantifies the uncertainty of code chips through conditional entropy, prioritizes high-entropy code chips, and dynamically optimizes the correction process by using the monotonic relationship between entropy and signal-to-noise ratio. Under low signal-to-noise ratio, it enhances the robustness to noise interference and improves the reliability of pseudo-code estimation; under high signal-to-noise ratio, it quickly converges to a low-entropy state and improves synchronization accuracy. Through correlation iterative correction and priority adaptive adjustment, it effectively copes with complex channel environments, significantly enhances the reliability and efficiency of pseudo-code synchronization, reduces the bit error rate, and provides high-precision signal processing capabilities for satellite navigation, wireless communications and other spread spectrum systems, improves signal capture and tracking performance, and realizes full-process optimization from uncertainty quantification to dynamic correction. It has broad application value and technological innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 1 is a flow chart of a GNSS pseudocode estimation method based on conditional entropy in one embodiment;

[0064] Figure 2 : is a distribution histogram of conditional entropy under correct estimation and incorrect estimation when the signal-to-noise ratio is -5 dB in one embodiment;

[0065] Figure 3 : is a distribution histogram of conditional entropy under correct estimation and incorrect estimation when the signal-to-noise ratio is 5 dB in one embodiment;

[0066] Figure 4 1 is a structural flow chart of a GNSS pseudocode estimation algorithm based on conditional entropy in one embodiment;

[0067] Figure 5 FIG1 is a schematic diagram of pseudo-code recovery performance for the first 50% of chips corrected, different signal-to-noise ratios, and sampling rates in one embodiment;

[0068] Figure 6 1 is a structural block diagram of a GNSS pseudocode estimation device based on conditional entropy in one embodiment;

[0069] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0071] In one embodiment, Figure 1 As shown, a conditional entropy GNSS pseudocode estimation method is provided, comprising the following steps:

[0072] Step 102: Estimate the noise variance of the received signal.

[0073] Specifically, for the received signal, it can be expressed as:

[0074] ;

[0075] in, It is a pseudo code. is Gaussian white noise, is the chip length.

[0076] Therefore, the noise variance estimation principle can be used to reversely calculate the noise variance by combining the statistical average power of the received signal with the linear ratio converted from the signal-to-noise ratio. It can be expressed as follows:

[0077] ;

[0078] in, It means to find the expectation of the square of the modulus of the received signal. Indicates the linear power ratio converted from the signal-to-noise ratio.

[0079] Step 104: construct a decision confidence based on the total number of chips, the real signal after baseband demodulation, and the pre-constructed signal index, and calculate the pseudo code posterior probability based on the decision confidence and the quantized signal-to-noise ratio defined by the noise variance and the received signal statistics.

[0080] When estimating a pseudocode, you can typically estimate the a posteriori probability of a pseudocode with a chip symbol of +1, and then infer the a posteriori probability of a pseudocode with a chip symbol of -1. In this step, you can use the Bayesian formula for a posteriori estimation. The quantized signal-to-noise ratio (SNR) is defined based on the received signal statistics and the noise variance. A higher SNR indicates a stronger signal and lower noise.

[0081] Step 106: Calculate the conditional entropy of a single chip based on the pseudo code posterior probability.

[0082] In the definition of conditional entropy, conditional entropy Represents a known random variable hour Uncertainty measure, in this step, the chip , is a binary discrete random variable, the received signal It is a continuous random variable, and each chip is independent, so it is expressed as follows using information entropy:

[0083] ;

[0084] The system needs to calculate the real-time uncertainty of each chip m in real time, so it is necessary to decompose the global conditional entropy into the single chip conditional entropy and calculate it chip by chip:

[0085] ;

[0086] From the single chip conditional entropy .

[0087] Step 108, sorting the chips in descending order according to the conditional entropy, determining the priority of chip correction based on the sorting, generating a local pseudocode sequence for the sorted chips, generating a local signal based on the local pseudocode sequence, calculating a correlation value with the related signal based on the local signal, updating the chips based on the correlation value, so that the entropy of all chips is ultimately less than a threshold, and outputting a pseudocode sequence.

[0088] In this step, it is proved that the conditional entropy strictly decreases monotonically with the signal-to-noise ratio. Therefore, after calculating the conditional entropy of each code chip, the code chips are sorted in descending order of conditional entropy, and code chips with large entropy values ​​are corrected first.

[0089] The above-mentioned conditional entropy GNSS pseudo-code estimation method quantifies code uncertainty through conditional entropy, prioritizes high-entropy code chips, and dynamically optimizes the correction process using the monotonic relationship between entropy and signal-to-noise ratio. At low signal-to-noise ratios, it enhances robustness to noise interference and improves pseudo-code estimation reliability; at high signal-to-noise ratios, it quickly converges to a low-entropy state, improving synchronization accuracy. Through iterative correlation correction and adaptive priority adjustment, it effectively copes with complex channel environments, significantly enhances the reliability and efficiency of pseudo-code synchronization, reduces bit error rates, and provides high-precision signal processing capabilities for spread spectrum systems such as satellite navigation and wireless communications. It improves signal capture and tracking performance, achieving full-process optimization from uncertainty quantification to dynamic correction, and has broad application value and technological innovation.

[0090] In one embodiment, first, a decision confidence is constructed based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index:

[0091] ;

[0092] in, represents the decision confidence of the mth chip, Indicates the chip length, , represents the real signal after baseband demodulation. The real signal is expressed as:

[0093] ;

[0094] ;

[0095] in, represents the sampling frequency, Indicates the filter bandwidth.

[0096] In another embodiment, for the step of performing Bayesian estimation on the sign bit+1 chip, the Bayesian formula is:

[0097] ;

[0098] Assume that the received signal statistics The conditional distribution is:

[0099] ;

[0100] Substitute into the Bayesian formula:

[0101] ;

[0102] Let quantized signal-to-noise ratio , It actually reflects the signal-to-noise ratio of the received signal, because is the chip length, is the average value of the received signal, is the noise variance, then the pseudocode posterior probability after the Bayesian formula is simplified is:

[0103] ;

[0104] The posterior probability of the pseudo code with the estimated symbol of -1 is:

[0105] .

[0106] In one embodiment, the single chip conditional entropy is:

[0107] ;

[0108] Confidence in judgment Determined to a specific value When the conditions For a specific value, the single chip conditional entropy degenerates to:

[0109] ;

[0110] Since the code value is ,set up:

[0111] ;

[0112] The single chip conditional entropy is simplified to:

[0113] .

[0114] when When , there is no information uncertainty; when When , it complies with the maximum entropy principle and is completely uncertain; when When , the entropy value decreases monotonically with the increase of certainty, which belongs to the uncertain state.

[0115] The conditional entropy is calculated for pseudo codes with SNRs of 5dB and -5dB, respectively, and the following two figures are obtained. The figures plot the distribution histograms of the information entropy of the chip with correct and incorrect chip estimation, respectively. Figure 2 The relationship between conditional entropy and the accuracy of chip estimation is shown when the signal-to-noise ratio is -5dB. When the signal-to-noise ratio is low, the observed value is seriously contaminated by noise, and the integrated statistics cannot distinguish the chip symbols. , i.e. random guessing, the conditional entropy of both correct and incorrect chip estimations approaches 1. The entropy distribution of correct chips is concentrated in the medium and high entropy range, while the entropy distribution of incorrect chips is higher overall. Figure 3 The relationship between conditional entropy and chip estimation accuracy is shown when the signal-to-noise ratio is 5dB. At this time, the bit error rate approaches 0, and the conditional entropy also approaches 0.

[0116] There is a certain relationship between the conditional entropy under different signal-to-noise ratios. The conditional entropy of chips can be expressed as :

[0117] ;

[0118] make , then the original formula can be expressed as:

[0119] ;

[0120] Will Substitute back:

[0121] ;

[0122] Conditional entropy function Based on signal-to-noise ratio Derivative, prove that , the conditional entropy decreases strictly monotonically with the signal-to-noise ratio:

[0123] ;

[0124] With the support of the above theory, a pseudo code estimation method based on maximum conditional entropy is proposed. The flow chart is as follows: Figure 4 As shown, the chips are sorted in descending order of conditional entropy as follows:

[0125] ;

[0126] in, Indicates returning the sorted index sequence. Indicates the The chip index corresponding to the priority level, , Indicates the maximum number of correlation corrections to be performed in priority order.

[0127] In another embodiment, for Priority chips , generating the The code chips are The two local pseudocode sequences are:

[0128] ;

[0129] in , Indicates the Priority chips The local pseudo code sequence, represents the original local pseudo code sequence;

[0130] Through pseudo code modulation and carrier modulation, the local signal is generated according to the local pseudo code sequence:

[0131] ;

[0132] ;

[0133] in, Indicates the Priority chips Take the local signal at +1, Indicates the Priority chips The local signal when it is -1;

[0134] Calculate the correlation value between the local signal and the received signal according to the local signal:

[0135] ;

[0136] ;

[0137] Finally, when the The correlation value of the chip Big time, The code chips are , otherwise .Right now:

[0138] ;

[0139] like Figure 5The figure shows the PN code recovery performance at different sampling rates and signal-to-noise ratios, when only the first 50% of chips are corrected. The signal-to-noise ratio and bit error rate (BER) have a significant negative correlation with PN code correction performance. As the signal-to-noise ratio increases, the BER shows an overall downward trend. Furthermore, at a fixed signal-to-noise ratio, the BER gradually decreases with increasing sampling rate. This is because a high sampling rate provides more sampling points within a chip, which contains more information and provides more complete statistical features for PN code correction.

[0140] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0141] In one embodiment, Figure 6 As shown, a GNSS pseudocode estimation device based on conditional entropy is provided, comprising: a noise calculation module 602, a posterior probability calculation module 604, a conditional entropy calculation module 606 and an estimation module 608, wherein:

[0142] Noise calculation module 602, used to estimate the noise variance of the received signal;

[0143] A posterior probability calculation module 604 is configured to construct a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculate a pseudocode posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic.

[0144] The conditional entropy calculation module 606 is used to calculate the conditional entropy of a single chip according to the pseudo code posterior probability;

[0145] The estimation module 608 is used to sort the code chips in descending order according to the conditional entropy, determine the priority of code chip correction based on the sorting, generate a local pseudocode sequence for the code chips in the sorting, generate a local signal based on the local pseudocode sequence, calculate the correlation value with the related signal based on the local signal, update the code chips based on the correlation value so that the entropy of all code chips is ultimately less than a threshold, and output the pseudocode sequence.

[0146] In one embodiment, the posterior probability calculation module 604 is further configured to construct a decision confidence level according to the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index:

[0147] ;

[0148] in, represents the decision confidence of the mth chip, Indicates the chip length, , Represents the real signal after baseband demodulation.

[0149] In one embodiment, the posterior probability calculation module 604 is further configured to estimate the pseudo code symbol as Bayesian formula:

[0150] ;

[0151] Assume that the received signal statistics The conditional distribution is:

[0152] ;

[0153] Substitute into the Bayesian formula:

[0154] ;

[0155] Let quantized signal-to-noise ratio , then the pseudocode posterior probability after Bayes' formula simplification is:

[0156] ;

[0157] The posterior probability of the pseudo code with the estimated symbol of -1 is:

[0158] .

[0159] In one embodiment, the conditional entropy calculation module 606 is further configured to calculate the single-chip conditional entropy:

[0160] ;

[0161] Confidence in judgment Determined to a specific value When the conditions For a specific value, the single chip conditional entropy degenerates to:

[0162] ;

[0163] Since the code value is ,set up:

[0164] ;

[0165] The single chip conditional entropy is simplified to:

[0166] .

[0167] In one embodiment, the estimation module 608 is further configured to sort the chips in descending order of conditional entropy as follows:

[0168] ;

[0169] in, Indicates returning the sorted index sequence. Indicates the The chip index corresponding to the priority level, , Indicates the maximum number of correlation corrections to be performed in priority order.

[0170] In one embodiment, the estimation module 608 is further configured to Priority chips , generating the The code chips are The two local pseudocode sequences are:

[0171] ;

[0172] in , Indicates the Priority chips The local pseudo code sequence, represents the original local pseudo code sequence;

[0173] Through pseudo code modulation and carrier modulation, the local signal is generated according to the local pseudo code sequence:

[0174] ;

[0175] ;

[0176] in, Indicates the Priority chips Take the local signal at +1, Indicates the Priority chips The local signal when it is -1;

[0177] Calculate the correlation value between the local signal and the received signal according to the local signal:

[0178] ;

[0179] ;

[0180] In one embodiment, the estimation module 608 is further configured to: The correlation value of the chip Big time, The code chips are , otherwise .

[0181] For the specific definition of the GNSS pseudo code estimation device based on conditional entropy, please refer to the definition of the GNSS pseudo code estimation method based on conditional entropy above, which will not be repeated here. The various modules in the above-mentioned GNSS pseudo code estimation device based on conditional entropy can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0182] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store received signal data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a conditional entropy GNSS pseudocode estimation method is implemented.

[0183] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0184] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0185] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0186] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0187] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are intended to fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A GNSS pseudocode estimation method based on conditional entropy, characterized in that: The method comprises: Estimate the noise variance of the received signal; Constructing a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculating a pseudo-code posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic; Calculating the conditional entropy of a single chip based on the pseudocode posterior probability; The chips are sorted in descending order according to the conditional entropy, and the priority of chip correction is determined based on the sorting. For the sorted chips, a local pseudocode sequence is generated, and a local signal is generated based on the local pseudocode sequence. A correlation value with a related signal is calculated based on the local signal, and the chips are updated based on the correlation value so that the entropy of all the chips is ultimately less than a threshold, and a pseudocode sequence is output.

2. The method according to claim 1, characterized in that The decision confidence level is constructed based on the total number of chips, the real signal after baseband demodulation, and the pre-constructed signal index, including: The decision confidence is constructed based on the total number of chips, the real signal after baseband demodulation, and the pre-constructed signal index: ； in, represents the decision confidence of the mth chip, Indicates the chip length, , Represents the real signal after baseband demodulation.

3. The method according to claim 1, characterized in that Calculating a pseudocode posterior probability according to the decision confidence and a quantized signal-to-noise ratio defined according to the noise variance and a received signal statistic, including: For the pseudocode estimation symbol Bayesian formula: ； Assume that the received signal statistics The conditional distribution is: ； Substitute into the Bayesian formula: ； Let quantized signal-to-noise ratio , then the pseudocode posterior probability after Bayes' formula simplification is: ； The posterior probability of the pseudo code with the estimated symbol of -1 is: 。 4. The method according to any one of claims 1 to 3, characterized in that Calculating the conditional entropy of a single chip according to the pseudocode posterior probability includes: Single chip conditional entropy: ； Confidence in judgment Determined to a specific value When the conditions For a specific value, the single chip conditional entropy degenerates to: ； Since the code value is ,set up: ； The single chip conditional entropy is simplified to: 。 5. The method according to any one of claims 1 to 3, characterized in that Sort the chips in descending order of conditional entropy, including: Sorting the chips in descending order of conditional entropy is: ； in, Indicates returning the sorted index sequence. Indicates the The chip index corresponding to the priority level, , Indicates the maximum number of correlation corrections to be performed in priority order.

6. The method according to claim 5, characterized in that For the chips in the sorting, a local pseudo code sequence is generated, a local signal is generated according to the local pseudo code sequence, and a correlation value with a correlation signal is calculated according to the local signal, further comprising: For the Priority chips , generating the The code chips are The two local pseudocode sequences are: ； in , Indicates the Priority chips The local pseudo code sequence, represents the original local pseudo code sequence; Through pseudo code modulation and carrier modulation, the local signal is generated according to the local pseudo code sequence: ； ； in, Indicates the Priority chips Take the local signal at +1, Indicates the chip with the gth priority The local signal when it is -1; Calculate the correlation value between the local signal and the received signal according to the local signal: ； 。 7. The method according to claim 6, characterized in that Update the chips according to the correlation value so that the entropy of all chips is less than the threshold, and output the pseudo code sequence, including: When The correlation value of the chip Big time, The code chips are , otherwise .

8. A GNSS pseudocode estimation device based on conditional entropy, characterized in that: The device comprises: A noise calculation module is used to estimate the noise variance of the received signal; a posterior probability calculation module, configured to construct a decision confidence criterion based on the total number of chips, the real signal after baseband demodulation, and a pre-constructed signal index, and calculate a pseudocode posterior probability based on the decision confidence criterion and a quantized signal-to-noise ratio defined according to the noise variance and the received signal statistic; A conditional entropy calculation module, configured to calculate the conditional entropy of a single chip based on the pseudocode posterior probability; An estimation module is configured to sort the chips in descending order according to the conditional entropy, determine the priority of chip correction based on the sorting, generate a local pseudocode sequence for the sorted chips, generate a local signal based on the local pseudocode sequence, calculate a correlation value with a related signal based on the local signal, update the chips based on the correlation value, so that the entropy of all chips is ultimately less than a threshold, and output a pseudocode sequence.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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