Spread spectrum signal precision tracking and measuring method under weak signal large dynamic laser link

Abstract

The invention discloses a spread spectrum signal precise tracking and measuring method under a weak-signal large-dynamic laser link, and solves the technical problem of how to realize rapid capture and high-precision, fuzzy-free, continuous and stable tracking of large-dynamic and extremely-weak pseudo code signals of inter-satellite links in an extremely-severe composite noise environment in a gravitational wave detection task. Belongs to the field of digital signal processing. The spread spectrum signal precise tracking and measuring method comprises the following steps: S1, carrying out rapid capturing based on prior information assistance; s2, code phase traction based on multi-correlator precision estimation; and S3, precise tracking based on extended Kalman filtering (EKF). According to the method, the existing main carrier Doppler frequency in the system is directly used as the prior information of the pseudo code carrier frequency, the method is more direct and efficient, the method is particularly suitable for an inter-satellite laser link system with a composite signal structure, and the frequency search range can be greatly reduced.

Description

Technical Field

[0001] This invention relates to a method for precise tracking and measurement of spread spectrum signals under weak signal and large dynamic range laser links, belonging to the field of digital signal processing. Background Technology

[0002] In space gravitational wave detection missions, pseudocode ranging technology provides crucial data support for time-delay interferometry (TDI) to effectively suppress laser frequency noise by measuring the true distance between satellites with high precision, and can be called the "lifeline" of TDI.

[0003] When satellite formations are in orbit, inter-satellite laser links must simultaneously perform three main functions: precise displacement measurement (for detecting gravitational waves), clock sideband modulation (for eliminating clock noise), and pseudo-code absolute distance measurement. Pseudo-code ranging is responsible for obtaining the inter-satellite absolute distance; although it consumes only about 1% of the carrier laser power, it is crucial for TDI processing.

[0004] To complete the absolute distance measurement of pseudocode, the following challenges need to be addressed: 1. "Large Dynamics": This encompasses time-varying factors such as changes in inter-satellite distance, Doppler frequency shift, and fluctuations in received power.

[0005] 2. "Weak signal": This refers to the essence of power limitation. Due to the extremely long transmission distance and low priority of optical power allocation, the signal-to-noise ratio is extremely low.

[0006] 3. "Rapid acquisition and precise tracking": The core contradiction that must be solved is to quickly find the signal within the range of uncertainty, and to lock onto it stably with ultra-high precision.

[0007] 4. "Extremely harsh composite noise environment": The laser frequency noise, phase noise, aiming error noise, etc., introduced by the optical path are on a much larger scale than the pseudo-code ranging signal itself. Therefore, the acquisition and tracking technology must have extremely strong anti-interference and noise suppression capabilities.

[0008] 5. "Meeting system-level measurement requirements": This means not only achieving acquisition and tracking, but also achieving "unambiguous, high-precision, continuous and reliable inter-satellite time delay measurement that meets TDI requirements". Summary of the Invention

[0009] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and solve the technical challenge of how to achieve rapid acquisition and high-precision, unambiguous, continuous and stable tracking of large dynamic and extremely weak pseudocode signals of inter-satellite links in gravitational wave detection missions under extremely harsh composite noise environments.

[0010] The objective of this invention is achieved through the following technical solutions: A method for precise tracking and measurement of spread spectrum signals in a weak signal, high dynamic range laser link, comprising: S1. Rapid capture based on prior information assistance; S2. Code phase pulling based on precise estimation by multiple correlators; S3. Precise tracking based on extended Kalman filter (EKF).

[0011] Compared with the prior art, the present invention has the following advantages: (1) The present invention directly uses the existing main carrier Doppler frequency in the system as the prior information of the pseudocode carrier frequency. This method is more direct and efficient, and is particularly suitable for inter-satellite laser links, which have a composite signal structure, and can greatly reduce the frequency search range.

[0012] (2) After capturing the coarse code phase, the present invention uses a multi-correlator array to perform open-loop fine code phase estimation, which avoids the problem that traditional delay-locked loops are prone to losing lock under high dynamic and weak signal conditions, and the code phase estimation accuracy before switching to tracking is higher.

[0013] (3) This invention uses an extended Kalman filter as a high-precision state observer to estimate the phase error of the carrier and code, while retaining the traditional loop filter to generate the control signal. The hybrid architecture of this invention absorbs the excellent estimation capability and adaptability of the EKF while retaining the stability and engineering maturity of the traditional loop, resulting in lower risk and easier implementation in practical applications.

[0014] (4) The EKF scheme of this invention has been fundamentally optimized at the algorithm level. Through accurate state modeling and noise suppression, it can achieve higher accuracy tracking.

[0015] (5) The EKF scheme of the present invention is more distinctive in the design of the state vector. It takes into account both the carrier frequency change rate (acceleration) and code frequency error, and explicitly models high dynamics, so that it has better tracking performance under large dynamic stress.

[0016] (6) This invention cleverly utilizes the existing Doppler information of the main laser carrier in the gravitational wave detection system to reduce the pseudocode acquisition from a two-dimensional search of "frequency-code phase" to a one-dimensional search of "code phase". This is not only an improvement in speed, but also the key to achieving the feasibility of acquisition under extremely low signal-to-noise ratio.

[0017] (7) This invention employs a dense correlator array to perform a “fine scan” near the coarse acquisition point, thereby improving the code phase accuracy from the “chip level” to the “sampling clock level” in an open-loop manner. This avoids the tracking errors of traditional DLLs under dynamic and weak signal conditions, and provides a high-quality initial state for subsequent precise tracking.

[0018] (8) This invention does not completely replace the traditional loop with EKF, but creatively makes EKF act as a "high-precision state observer", which is specifically responsible for extracting small phase and frequency errors in harsh noise; while the traditional loop filter acts as a "robust controller", which is responsible for generating smooth NCO control signals. This architecture combines the high precision, noise immunity and adaptive capability of EKF, while retaining the stability and engineering maturity of traditional loops.

[0019] (9) This invention proposes a complete three-stage precision measurement process of “capture-traction-tracking”, with each link closely connected. It is systematically designed for extreme scenarios and solves the systematic problem of fast convergence from the uncertain domain and maintaining extremely high measurement accuracy. Attached Figure Description

[0020] Figure 1 This is a block diagram of a precision tracking algorithm based on the Extended Kalman Filter (EKF). Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0022] A method for precise tracking and measurement of spread spectrum signals in a weak signal, high dynamic range laser link, comprising: S1. Rapid capture based on prior information assistance S11, Narrowband Search Guided by Prior Information In gravitational wave detection missions, pseudo-code ranging signals are transmitted in the optical link along with the main carrier signal and clock sideband signal. The pseudo-code ranging signal, the main carrier signal, and the clock sideband signal all experience the same Doppler effect. Therefore, during pseudo-code acquisition, the Doppler frequency of the main carrier signal can be directly used as prior information for the Doppler frequency of the pseudo-code signal.

[0023] Before pseudocode signal acquisition and tracking, the main carrier signal and clock sideband signal need to be extracted and stripped, during which the Doppler frequency estimate of the main carrier is obtained. Thus, during pseudocode acquisition, it is not necessary to perform a large-scale search in the frequency dimension, but only to search within a very small uncertainty range (this uncertainty range is determined by the estimation error of the main carrier Doppler frequency).

[0024] Specific steps: The Doppler frequency estimate of the main carrier is used as the frequency center of the pseudocode carrier.

[0025] Based on the estimation error of the main carrier Doppler frequency, a small frequency search range (e.g., ±5kHz) is determined.

[0026] Within this small frequency range, the search is performed in small steps (e.g., 100Hz) while simultaneously performing a parallel search for the code phase.

[0027] This will greatly reduce the computational load of the capture process.

[0028] S12. FFT-based parallel code phase and frequency search and capture function S121. Setting parameters: Sampling rate Intermediate frequency The pseudocode length is L.

[0029] Construct a Doppler frequency offset search interval to cover the expected dynamic range and search frequency range. , Frequency stepping The number of incoherent integral segments is M.

[0030] The local pseudocode sequence is transformed to the frequency domain using a fast Fourier transform, and its conjugate complex number is calculated.

[0031] Generate local pseudocode sequences And calculate its frequency domain representation. (Zero padding is used to avoid cyclic correlation). Where m is the time-domain index, representing the sequence number of the time-domain sampling point, and k is the frequency-domain index, representing the sequence number of the frequency component.

[0032] calculate Where conj represents the complex conjugate operation.

[0033] S122, Read a segment of input signal to be processed. To improve the visibility of weak signals through effective subsequent accumulation processing, the input signal needs to be divided into M segments, which may overlap. Points, among which Point, where L is the pseudocode length, Sampling rate, The code frequency corresponds to the number of points in one pseudocode period. That is, in the input signal Obtained by segmented intervals One point. S123, Frequency Search Loop This is a fine-grained frequency search process; the system determines the frequency search range based on... , and search step size Each candidate frequency is generated sequentially within the search range. For each candidate frequency, the following processing is performed.

[0034] a. Carrier stripping: This involves removing each segment of the signal... , and the oscillation signal generated based on the current candidate frequency Multiplication completes the down-conversion, shifting the signal from a very high frequency to a very low, near-zero frequency baseband. This yields the baseband signal. .

[0035] b. Frequency domain transformation: For each segment of the baseband signal Perform a Fast Fourier Transform (FFT) to obtain .

[0036] c. Frequency domain correlation: The frequency results obtained in the previous step are processed step by step. With advance preparation Perform the product operation in the frequency domain. The result is... .

[0037] d. Inverse FFT: The frequency domain comparison result obtained in the previous step is then transformed back to the time domain using the inverse fast Fourier transform (inverse FFT) to obtain a clear time-domain correlation result sequence, i.e. .

[0038] e. Incoherent integral: The current candidate frequency The following M segments of signals are processed according to the steps (a to d) described above to obtain relevant results, and their power is calculated respectively. Then, accumulate and fill to... Matrix (frequency × code phase).

[0039] By sequentially searching all candidate frequencies, the final result is obtained. Matrix (frequency × code phase).

[0040] S124, Peak Detection Under low signal-to-noise ratio conditions, a fixed threshold may not be effective in detecting signals. Therefore, an adaptive threshold detection technique is employed. Noise basis estimation: Estimating the noise level based on the relevant results of the entire search space.

[0041] This means calculating the average of all values ​​in the matrix, which represents the general level of noise.

[0042] This means calculating the standard deviation of all values ​​in the matrix, which represents the fluctuation and volatility of the noise.

[0043] CFAR detection: Determine a safety factor based on the system's allowable false alarm probability requirement. Then, the detection threshold is calculated, and peak values ​​exceeding the threshold are extracted.

[0044] Calculate the detection threshold: ; Determined by the probability of a false alarm; exist Finding the maximum value in the matrix (frequency × code phase) .

[0045] if Threshold If the capture is successful, the corresponding code phase and frequency will be output.

[0046] During the acquisition phase, the frequency estimation accuracy is ±50Hz and the code phase estimation accuracy is ±0.5 chips.

[0047] S2, Code phase pulling based on precise estimation using multiple correlators Code phase traction achieves precise code phase estimation after initial acquisition by arranging a dense array of correlators near the coarse code phase estimate. This design, based on open-loop estimation, avoids the dynamic tracking limitations of traditional delay-locked loops.

[0048] Specifically, multiple correlators are arranged at smaller intervals (e.g., the code phase interval corresponding to one clock cycle) near the coarse code phase. By comparing the outputs of these correlators, the code phase can be estimated more accurately.

[0049] Since a rough carrier frequency has already been obtained during the acquisition phase, this carrier frequency can continue to be used for carrier stripping when performing fine code phase estimation.

[0050] 1. Based on the captured coarse code phase and frequency, set the local carrier and multiple local pseudocodes (with different delays).

[0051] 2. Carrier stripping is performed on the input signal to obtain the baseband signal.

[0052] 3. For the baseband signal, perform correlation operations (correlator group) with multiple local pseudocodes with different delays.

[0053] 4. Calculate the correlated power (or amplitude) for each correlator.

[0054] 5. Estimate the fine code phase by finding the correlator with the highest correlation power.

[0055] This multi-correlator design can significantly improve the estimation accuracy of the code phase. (Code chip). For example, if the code rate Sampling rate ,but =0.0125 chips.

[0056] S3. Precise tracking based on extended Kalman filter (EKF) In gravitational wave detection missions, achieving high-precision, unambiguous, and continuous stable tracking of large dynamic range, weak pseudocode signals from inter-satellite links under extremely harsh composite noise environments is crucial. Considering that relatively accurate carrier frequency and code phase have already been obtained during the acquisition phase, the following approach can be adopted: replacing the traditional phase detector with an EKF. In this mode, the traditional phase detector is replaced by the EKF, but the loop filter is still retained. (Note: Traditional mode: third-order PLL + second-order DLL + phase detector) Figure 1 This is a block diagram of a precision tracking algorithm based on the Extended Kalman Filter (EKF). The EKF is used to estimate the errors in the carrier loop and code loop, and these errors are then fed into a conventional loop filter to generate the NCO control signal.

[0057] Assume the carrier ring uses a third-order ring and the code ring uses a second-order ring. The state vector of the EKF includes carrier phase error, carrier frequency error, carrier frequency rate of change error, code phase error, and code frequency error.

[0058] State vector in the algorithm: , in: Carrier phase error (rad); Carrier frequency error (Hz); Carrier frequency change rate error (Hz / s); : Code phase error (chips); Code frequency error (chips / s).

[0059] The state vector represents the deviation from the nominal value.

[0060] Measurement vector in the algorithm: (I / Q outputs of early, immediate, and late correlators); System model: The state transition uses a linear model, taking into account the dynamics of the carrier and the code; Observation model: Uses the relevant I / Q output model, including the effects of carrier phase error and code phase error.

[0061] The algorithm implementation for the EKF tracking loop is as follows: The algorithm is a continuously running prediction and correction loop.

[0062] (1) Initialization: State vector State covariance matrix P; process noise covariance Q; measurement noise covariance R.

[0063] (2) State prediction: For each time step k: State prediction (using a linear state transition model): State prediction: Based on the system's dynamic model (described by the state transition matrix F), estimate the optimal state from the previous time step. Calculate the predicted state value at the current moment. .

[0064] Uncertainty prediction: Prediction not only propagates the state itself, but also the uncertainty of the state. The uncertainty of the state estimate at the previous time step (covariance matrix), after being propagated through the dynamic model F, and then combined with process noise (random disturbances that cannot be described by the model, whose covariance is Q), yields the uncertainty of the predicted state at the current time step. .

[0065] Calculate the Jacobian matrix H of the observation model (in (linearization); the observation model h(x) describes the system state. (Five error measures) and expected observations The nonlinear relationship between the outputs of the six correlators. To fuse predictions and observations in the update step, this nonlinear relationship needs to be incorporated into the current prediction state. A linear approximation is performed at the given location to obtain the Jacobian matrix H.

[0066] Read observations from the correlator ; Calculate the observation residuals: ; Compare the actual observed value z with the predicted state Expected observations By comparing the results, we obtain the residual y.

[0067] (3) Calculate the Kalman gain: The Kalman gain K is a "weight matrix" that determines how much to trust the predictions and how much to trust the new observations when updating the state. : The uncertainty of the predicted state Projected onto the observation space (multiplied by) ), is the cross-covariance matrix between state prediction error and observation prediction error, which quantifies the strength and direction of the correlation between the uncertainty of state variables and the uncertainty of observation variables. It is the covariance matrix of the observed and predicted values ​​themselves. Uncertainty in predicting observations ( Adding the uncertainty of observation noise R (measurement noise covariance matrix), this represents the total uncertainty of the residual y. inv represents calculating the inverse matrix; matrix division is achieved by multiplying by the inverse matrix. K calculates the ratio of the uncertainty of the predicted state to the total uncertainty.

[0068] (4) Status update: State update: predict the state The observed residual y is fused with the Kalman gain K as the weight to obtain the updated state estimate. This is the optimal estimate at the current moment.

[0069] Uncertainty Update: By incorporating observational information, our state estimation becomes more accurate because uncertainty is reduced. It is the covariance matrix of the updated state estimate, reflecting the confidence improvement after this update.

[0070] (5) Extract the error estimate from the updated state: From the updated state vector From this, extract the first component (carrier phase error). ).

[0071] From the updated state vector From this, extract the fourth component (code phase error). ).

[0072] Will and Feed the corresponding loop filters (carrier loop and code loop): Carrier loop filter input: , Output: Carrier frequency correction value.

[0073] Code ring filter input: Output: Code frequency correction value.

[0074] (6) Update NCO: Carrier NCO frequency = Nominal carrier frequency + Carrier loop filter output Code NCO frequency = Nominal code frequency + Code loop filter output Set the current state to the updated state and proceed to the next moment.

[0075] In this invention, the EKF only provides error estimation, while the loop filter is responsible for generating a smooth control signal. This mode combines the accurate estimation of the EKF with the stability of a traditional loop filter.

[0076] This scheme improves phase estimation accuracy by 3-5 times compared to traditional phase detectors, meeting extremely high phase measurement accuracy requirements; the weak signal tracking threshold is reduced by 3-6dB, adapting to extremely weak signal environments; the dynamic range is adaptively adjusted to adapt to different signal conditions, maintaining system stability and reliability; implementation complexity is moderate, and it is relatively easy to implement based on existing technologies.

[0077] The contents not described in detail in this specification are common knowledge to those skilled in the art.

[0078] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for precise tracking and measuring of spread spectrum signals in a weak signal large dynamic laser link, characterized in that, include: S1. Rapid capture based on prior information assistance; S2. Code phase pulling based on precise estimation by multiple correlators; S3. Precise tracking based on extended Kalman filter (EKF).

2. The spread spectrum signal fine tracking and measuring method according to claim 1, characterized in that, Step S1 specifically includes: S11, Narrowband search guided by prior information; S12. Parallel code phase and frequency search and capture based on FFT.

3. The method for precise tracking and measurement of spread spectrum signals according to claim 2, characterized in that, Step S12 specifically includes: S121. Set parameters; construct the Doppler frequency offset search interval, covering the expected dynamic range, and the search frequency range [ , Frequency stepping The number of incoherent integral segments M; the local pseudocode sequence is transformed to the frequency domain using a fast Fourier transform, and its conjugate complex number is calculated; the local pseudocode sequence is generated. And calculate its frequency domain representation. , where m is the time-domain index and k is the frequency-domain index; S122, Read a segment of input signal to be processed. The input signal is divided into M segments, each segment Points, among which Where L is the pseudocode length, Sampling rate, Code frequency; That is, in the input signal Obtained by segmented intervals One point; S123, Search range based on frequency [ , and search step size Each candidate frequency is generated sequentially within the search range. For each candidate frequency, carrier stripping, frequency domain transformation, frequency domain correlation, inverse FFT, and incoherent integration are performed. S124, Peak detection.

4. The method for precise tracking and measurement of spread spectrum signals according to claim 3, characterized in that, In S123, carrier stripping refers to: removing each segment of signal... , and the oscillation signal generated based on the current candidate frequency Multiply to complete down-conversion and obtain the baseband signal. .

5. The method for precise tracking and measurement of spread spectrum signals according to claim 4, characterized in that, In S123, frequency domain transformation refers to: transforming each segment of the baseband signal... Perform a Fast Fourier Transform (FFT) to obtain .

6. The method for precise tracking and measurement of spread spectrum signals according to claim 5, characterized in that, In S123, frequency domain correlation refers to the frequency step-by-step results obtained from frequency domain transformation. With advance preparation Performing the product operation in the frequency domain yields... .

7. The method for precise tracking and measurement of spread spectrum signals according to claim 5, characterized in that, In S123, the incoherent integral refers to: integrating the current candidate frequency... The following M segments of signal are processed in the order of carrier stripping, frequency domain transformation, frequency domain correlation, and inverse FFT to obtain correlation results, and their power is calculated respectively. Then, accumulate and fill to... matrix.

8. The method for precise tracking and measurement of spread spectrum signals according to claim 1, characterized in that, S2 include: Based on the captured coarse code phase and frequency, set the local carrier and multiple local pseudo-codes; Carrier stripping is performed on the input signal to obtain the baseband signal; For the baseband signal, correlation operations are performed with multiple local pseudocodes with different delays; Calculate the relevant power or amplitude for each correlator; Fine code phase is estimated by finding the correlator with the highest correlation power.

9. The method for precise tracking and measurement of spread spectrum signals according to claim 1, characterized in that, The algorithm for the EKF tracking loop in S3 is implemented as follows: (1) Initialize the state vector, state covariance matrix, process noise covariance, and measurement noise covariance; (2) For each time step k, perform state prediction; (3) Calculate the Kalman gain; (4) The predicted state and the observed residual are fused with Kalman gain as the weight to obtain the updated state estimate; (5) Extract the error estimate from the updated state; (6) Update NCO.

Images