A high-sensitivity tracking method for rotating platform receiver based on KF

Abstract

The present invention discloses a high-sensitivity tracking method for a rotating platform receiver based on a KF, applicable to satellite navigation receiver systems. The method comprises: determining the system state equation and the mean square error matrix equation of the system's a priori estimation error during the Kalman filter prediction process, and determining the relationship between the system's state vector and observation vector; calculating the coherent integral, incoherent and coherent combined integrals of the phase-locked loop in the tracking loop of the satellite navigation receiver in an ideal state, and the incoherent integral signal of the Kalman filter; modifying the mean square error matrix equation of the system's a priori estimation error and the state transfer matrix of the Kalman filter; and performing correction after the Kalman filter predicts the system state. The method squaring the coherent integral and combining it with a phase detector yields an extended incoherent integral, while simultaneously combining it with the Kalman filter to obtain precise positioning information, thereby improving the receiver's tracking sensitivity.

Description

Technical Field

[0001] The present invention relates to the technical field of signal reception, and in particular to a high-sensitivity tracking method for a rotating platform receiver based on KF. Background Art

[0002] Countries around the world are currently striving to develop low-cost, high-precision guided artillery shells. As a core component of precision-guided artillery shells, satellite navigation systems have become a key research focus. Satellite navigation provides highly accurate position and velocity information, effectively improving the guidance accuracy of guided artillery shells. Given the inherent spinning characteristics of artillery shells, grenades, and other systems, research on small, low-cost, and highly integrated satellite navigation systems operating on rotating platforms is a key area of ​​future development.

[0003] Traditional receivers generally use the least squares positioning algorithm. Although the least squares algorithm can find the optimal point in the measurement values ​​containing errors and noise, so that the residual sum of squares of all measurement values ​​is minimized, the different measurement errors and noises at different times are converted into different positioning errors and noises at the corresponding times after the least squares calculation. Therefore, the positioning results are usually rough and messy. During the rotation of the artillery carrier, when the satellite navigation receiver on the rotating platform encounters problems such as sudden fading of the satellite signal-to-noise ratio and sudden loss of satellite signals, the traditional receiver will not be able to effectively track the received signals and thus cannot provide reliable positioning, resulting in large positioning errors of the satellite navigation system and inability to accurately guide. Summary of the Invention

[0004] To solve the above problems, the present invention provides a high-sensitivity tracking method for a rotating platform receiver based on Kalman filtering (KF). The method uses Kalman filtering (KF) technology to perform certain filtering processing on the least squares results, overcoming the disadvantage that the least squares values ​​at different times are uncorrelated, making the positioning results smoother and more accurate. At the same time, the nonlinear coherent integration is combined to improve the sensitivity to weak signals, thereby achieving effective tracking of satellite signals during the rotation of the carrier and providing accurate positioning results.

[0005] The present invention provides a high-sensitivity tracking method for a rotating platform receiver based on KF. The specific technical solution is as follows:

[0006] The method is applied to a satellite navigation receiver system and comprises the following steps:

[0007] S1: Determine the system state equation and the mean square error matrix equation of the system prior estimation error in the Kalman filter prediction process;

[0008] S2: Determine the state vector x of the system k (t k ) and the observation vector z k (tk )

[0009] S3: Calculate the coherent integral of the phase-locked loop in the tracking loop of an ideal satellite navigation receiver

[0010] S4: Calculate the incoherent and coherent combined integrals by squaring the coherent integral

[0011] S5: Calculate the Kalman filter's coherent and incoherent combined integral signal Y based on the expanded incoherent and coherent combined integral results ncoh ;

[0012] S6: Modify the mean square error matrix equation and state transfer matrix F of the system prior estimation error of the Kalman filter K ;

[0013] S7: After the Kalman filter predicts the system state, correction is performed. The specific process is as follows:

[0014] The correction gain of the Kalman filter system is calculated, and then the state estimate is updated. Finally, the mean square error matrix of the system's posterior estimation error is calculated from the prior value to complete the correction.

[0015] Furthermore, the mean square error matrix equation of the system state equation and the system prior estimation error is as follows:

[0016] x k+1 (t k+1 )=F(t k )x k (t k )+C(t k )w k (t k )

[0017]

[0018] Among them, x in the system state equation k (t k ) is the system at t k The state vector at time, x k+1 (t k+1 ) is the system at t k+1 The state vector at time, w k (t k ) is the process noise vector, F(t k ) indicates that from t k From time to t k+1 The state transfer matrix at the moment, C(t k ) is the colored noise state transfer matrix; P kis the posterior estimated mean square error matrix, Q is the state transition noise covariance matrix, which satisfies δ kn is the Kronecker function.

[0019] Furthermore, according to the system measurement delay and discretization model, the state transfer matrix is ​​defined as follows:

[0020]

[0021] Where β represents the carrier auxiliary factor.

[0022] Furthermore, the state vector x of the system k (t k ) and the observation vector z k (t k ) are as follows:

[0023] z k (t k )=Hx k (t k )+v k (t k )

[0024] Among them, H represents the observation matrix between the observation quantity and the system state, v k (t k ) represents the measurement noise vector.

[0025] Furthermore, in step S3, the coherent integration The calculation is as follows:

[0026]

[0027] Among them, Y i represents the received signal output by the tracking iteration of a satellite channel, K is the number of correlated signals in T intervals, b i is the data bit.

[0028] Furthermore, in step S4, the incoherent and coherent combination integral It is expressed as follows:

[0029]

[0030] Among them, Y coh,m Represents the coherent integration signal output by the tracking iteration of a satellite channel.

[0031] Furthermore, in step S5, the incoherent and coherent combined integral signal Y ncoh , which is expressed as follows:

[0032]

[0033] Among them, Im represents the imaginary part, and M represents the integrated data amount of the incoherent and coherent combined integration.

[0034] Furthermore, the Kalman filter gain during the correction process is calculated as follows:

[0035]

[0036] Where R is the covariance matrix of the measurement noise vector, satisfying is the mean square error matrix of the system's posterior estimation error by the prior value, and H represents the observation matrix between the observation quantity and the system state.

[0037] Furthermore, the updated state estimate is expressed as follows:

[0038]

[0039] in, is a priori estimate of the system state.

[0040] Furthermore, the mean square error matrix of the system's posterior estimation error is calculated as follows:

[0041]

[0042] Where I represents the identity matrix.

[0043] The beneficial effects of the present invention are as follows:

[0044] The present invention utilizes a Kalman filter to obtain precise positioning information. At the same time, by squaring the coherent integral and utilizing an incoherent process to expand the integration time of the discriminator in the receiver system, the tracking sensitivity of the satellite navigation receiver when the carrier platform rotates is effectively improved. Even for receivers in the event of a sudden attenuation of the satellite signal carrier-to-noise ratio and a decrease in the number of tracked satellites during platform rotation, the invention can still provide precise positioning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the process framework of the present invention. DETAILED DESCRIPTION

[0046] The following description clearly and completely describes the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0047] In the description of the embodiments of the present invention, it should be noted that the indicated orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is typically placed when in use, or the orientations or positional relationships commonly understood by those skilled in the art, or the orientations or positional relationships in which the inventive product is typically placed when in use. These are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention. In addition, the terms "first" and "second" are used only to distinguish descriptions and should not be understood as indicating or implying relative importance.

[0048] In describing the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "disposed" and "connected" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0049] Example 1

[0050] Embodiment 1 of the present invention discloses a high-sensitivity tracking method for a rotating platform receiver based on KF, which is applied to a satellite navigation receiver system. Figure 1 As shown;

[0051] The specific steps of the method are as follows:

[0052] S1: Based on the satellite navigation receiver system, the system state equation and the mean square error matrix equation of the system prior estimation error in the Kalman filter prediction process can be determined as follows:

[0053] x k+1 (t k+1 )=F(t k )x k (t k )+C(t k )w k (t k )

[0054]

[0055] Among them, x in the system state equation k (t k ) is the system at t k The state vector at time, x k+1 (t k+1 ) is the system at t k+1 The state vector at time, w k (t k) is the process noise vector, F(t k ) indicates that from t k From time to t k+1 The state transfer matrix at the moment, C(t k ) is the colored noise state transition matrix;

[0056] In the mean square error matrix equation of the system prior estimation error, P k is the posterior estimated mean square error matrix, Q is the state transition noise covariance matrix, which satisfies δ kn is the Kronecker function.

[0057] In this embodiment, the state transfer matrix is ​​obtained based on the system measurement delay and the motion trajectory of the object in the actual application scenario:

[0058]

[0059] where β represents the carrier assist factor defined by the relationship between the chip rate and the carrier frequency.

[0060] S2: Determine the state vector x of the system k (t k ) and the observation vector z k (t k )

[0061] In this embodiment, the Kalman filter assumes that the state vector x of the system k (t k ) and the observation vector z k (t k ) is:

[0062] z k (t k )=Hx k (t k )+v k (t k )

[0063] Among them, H represents the observation matrix between the observation quantity and the system state, v k (t k ) represents the measurement noise vector.

[0064] The value of the observation vector is mainly based on the code delay error and the carrier phase error. Therefore, the observation matrix can be derived from the measurement equation as follows:

[0065]

[0066] S3: Calculate the coherent integral of the phase-locked loop in the tracking loop of an ideal satellite navigation receiver

[0067] The Kalman filter system's measurement value for demodulated satellite signals is determined based on the coherent integration results in the tracking loop. Therefore, before entering the Kalman filter, it is necessary to calculate the coherent integration of the phase-locked loop in the tracking loop of the ideal satellite navigation receiver, as follows:

[0068]

[0069] where Y i represents the signal output by the tracking iteration of a satellite channel, K is the number of correlated signals in T intervals, and b i are data bits.

[0070] Further approximate calculation can be obtained:

[0071]

[0072] Among them, A coh is the signal amplitude obtained after coherent integration, which is related to the carrier-to-noise ratio, T coh is the integral calculation time, i.e. KT, f e is the carrier phase error, φ e is the Doppler frequency shift error, η coh is the filtered Gaussian noise.

[0073] S4: Calculate the incoherent and coherent combined integrals by squaring the coherent integral

[0074] In this embodiment, the extension of the integration time is achieved by applying an incoherent process;

[0075] The coherent integral obtained in S3 above is modified by squaring the signal before integration. The coherent integral is squared and accumulated to obtain the incoherent and coherent combined integral, which is expressed as:

[0076]

[0077]

[0078] Among them, Y coh,m represents the coherent integration signal output by the tracking iteration of a satellite channel, M represents the integrated data volume of the incoherent and coherent combination integration, A ncoh is the signal amplitude after incoherent integration, which is related to the carrier-to-noise ratio, T ncoh The non-coherent integral calculation time is MT coh , η ncoh is the filtered noise after incoherent integration.

[0079] S5: Calculate the Kalman filter incoherent integration signal Y based on the expanded incoherent integration result ncoh ;

[0080] In this embodiment, after obtaining the expanded incoherent integral, combined with the principle of the phase detector, the final incoherent integral signal for Kalman filtering is obtained as follows:

[0081]

[0082] Among them, Im represents the imaginary part, M represents the integrated data of the incoherent and coherent combination integration, and Y coh,m Represents the coherent integration signal output by the tracking iteration of a satellite channel.

[0083] S6: Modify the mean square error matrix equation and state transfer matrix F of the system prior estimation error of the Kalman filter K ;

[0084] According to the high-sensitivity phase detector, the relevant parameters of the Kalman filter are modified; the mean square error matrix equation of the system prior estimation error of the Kalman filter is modified as follows:

[0085]

[0086] Among them, the state transfer matrix is ​​modified as follows:

[0087]

[0088] S7: After the Kalman filter predicts the system state, correction is performed. The specific process is as follows:

[0089] Calculate the correction gain of the Kalman filter system, then update the state estimate, and finally calculate the mean square error matrix of the system's posterior estimation error from the prior value to complete the correction;

[0090] In this embodiment, the Kalman filter gain during the correction process is calculated as follows:

[0091]

[0092] Where R is the covariance matrix of the measurement noise vector, satisfying is the mean square error matrix of the system's posterior estimation error by the prior value, and H represents the observation matrix between the observation quantity and the system state.

[0093] The state estimate of the Kalman filter system is updated as:

[0094]

[0095] in, is a priori estimate of the system state.

[0096] The mean square error matrix of the system's posterior estimation error is determined by the prior value Calculated to be

[0097]

[0098] Where I represents the identity matrix.

[0099] Based on the above three formulas, the actual measurement value is used to correct the prior estimate of the prediction process of S1, and then the correction value is brought back to the prediction process for prediction. The Kalman filter is completed recursively in this way, so that the receiver of the rotating platform obtains a higher sensitivity tracking capability.

[0100] The present invention is not limited to the aforementioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A high-sensitivity tracking method for a rotating platform receiver based on KF, characterized in that: The method is applied to a satellite navigation receiver system; The method comprises the following steps: S1: Determine the system state equation and the mean square error matrix equation of the system prior estimation error in the Kalman filter prediction process; S2: Determine the state vector of the system and the observation vector the relationship between; S3: Calculate the coherent integral of the phase-locked loop in the tracking loop of an ideal satellite navigation receiver ; S4: Calculate the incoherent and coherent combined integrals by squaring the coherent integral ; S5: Calculate the Kalman filter's incoherent and coherent combined integral signals based on the expanded incoherent and coherent combined integral results ; S6: Modify the mean square error matrix equation and state transfer matrix of the system prior estimation error of the Kalman filter ; S7: After the Kalman filter predicts the system state, correction is performed. The specific process is as follows: The correction gain of the Kalman filter system is calculated, and then the state estimate is updated. Finally, the mean square error matrix of the system's posterior estimation error is calculated from the prior value to complete the correction.

2. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 1, characterized in that: The mean square error matrix equation of the system state equation and the system prior estimation error is as follows: Among them, the system state equation The system is The state vector at time, The system is The state vector at time, is the process noise vector, Indicates from The time has come The state transition matrix at the moment, is the colored noise state transition matrix; is the posterior estimated mean square error matrix, is the state transition noise covariance matrix, which satisfies , is the Kronecker function.

3. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 2, characterized in that: According to the system measurement delay and discretization model, the state transfer matrix is ​​defined as follows: in, represents the carrier assist factor.

4. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 1, characterized in that: The state vector of the system and the observation vector The relationship between them is as follows: in, represents the observation matrix between the observation quantity and the system state, represents the measurement noise vector.

5. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 1, characterized in that: In step S3, the coherent integration , calculated as follows: in, represents the received signal output by the tracking iteration of a satellite channel, for The number of correlated signals over the interval, is the data bit.

6. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 5, characterized in that: In step S4, the incoherent and coherent combination integration , which is expressed as follows: in, It represents the coherent integration signal output by the tracking iteration of a satellite channel, and M represents the integrated data volume of the non-coherent and coherent combined integration.

7. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 6, characterized in that: In step S5, the incoherent and coherent combined integrated signals , which is expressed as follows: Here, Im represents an imaginary number, and M represents the amount of integrated data of the incoherent and coherent combined integration.

8. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 1, characterized in that: The Kalman filter gain during the correction process is calculated as follows: in, is the covariance matrix of the measurement noise vector, satisfying , is the mean square error matrix of the system's posterior estimation error given by the prior values, Represents the observation matrix between the observation quantity and the system state.

9. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 8, characterized in that: The updated state estimate is expressed as follows: in, is a priori estimate of the system state.

10. The high-sensitivity tracking method for a rotating platform receiver based on KF according to claim 9, characterized in that: The mean square error matrix of the system's posterior estimation error is calculated as follows: in, Represents the identity matrix.

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