A method, apparatus, electronic device, and storage medium for determining a fusion state information

Abstract

The present application relates to the field of navigation technology, and discloses a method, apparatus, electronic device and storage medium for determining a fusion state information. The method includes: determining target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on a current observed value, state cubature information and the state covariance matrix; the current observed value is the observed value corresponding to the state quantity currently collected by an inertial device and the state quantity currently collected by a satellite device; determining target observed information based on the target state information, an observation adjustment factor and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observed value and observation cubature information; determining the fusion state information of the inertial device and the satellite device based on the target state information and the target observed information. By adjusting the contribution degrees of state data and observed data to the filtering result respectively, the navigation accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of navigation, and particularly relates to a method, apparatus, electronic device, and storage medium for determining fused state information. Background Art

[0002] With the development of technology, navigation technology has been applied in multiple fields. In navigation technology, satellite navigation has high-precision positioning and no cumulative error, but is vulnerable to interference from the wireless communication environment; inertial navigation has high autonomy and anti-interference ability, but has a large cumulative error. By combining these two methods, higher-precision positioning can be achieved.

[0003] In related technologies, the Kalman filtering method is adopted, and the best state estimate of a linear system is obtained by using the linear system state equation to acquire observation data from the system input / output.

[0004] However, the Kalman filter requires an accurate mathematical model and accurate prior noise statistical characteristics. During the filtering process, if the noise characteristics are abnormal, the filtering accuracy will decrease, affecting the navigation accuracy. Summary of the Invention

[0005] The present application provides a method, apparatus, electronic device, and storage medium for determining fused state information to improve navigation accuracy.

[0006] In a first aspect, an embodiment of the present application provides a method for determining fused state information, the method including:

[0007] Determining target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on a current observation value, state cubature information, and the state covariance matrix; the current observation value is an observation value corresponding to a state quantity currently collected by an inertial device and a state quantity currently collected by a satellite device; the state covariance matrix is a covariance matrix corresponding to the state cubature information; the state cubature information is actual cubature information of the observation value;

[0008] Determining target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is a covariance matrix corresponding to observation noise; the observation cubature information is predicted cubature information of the observation value;

[0009] Determining the fused state information of the inertial device and the satellite device based on the target state information and the target observation information.

[0010] In the above solution, by introducing a state adjustment factor and an observation adjustment factor, when the observation is abnormal, the state covariance matrix and the observation noise variance matrix are adaptively adjusted, and the contribution degrees of the state data and the observation data to the filtering result are adjusted respectively, so as to improve the adaptability and anti-interference ability of the fusion algorithm, accurately determine the fusion state information of the inertial device and the satellite device, and thus improve the navigation accuracy.

[0011] In one or more embodiments, the state adjustment factor is determined by the following method:

[0012] The difference between the current observation value and the volume value corresponding to the state volume information is determined as the state residual information;

[0013] Based on the state residual information and the trace of the state covariance matrix, a residual vector is determined;

[0014] Based on the comparison result between the norm of the residual vector and a preset adjustment coefficient, the state adjustment factor is determined.

[0015] In the above solution, by comparing the norm of the residual vector with a preset adjustment coefficient, the comparison result characterizes whether the observation is abnormal. Therefore, according to the comparison result, the state adjustment factor used to adjust the state covariance matrix can be accurately determined.

[0016] In one or more embodiments, determining the target state information based on the state adjustment factor and the state covariance matrix includes:

[0017] The product of the reciprocal of the state adjustment factor and the state covariance matrix is determined as the first prediction matrix;

[0018] The inverse matrix of the first prediction matrix is determined as the first target state information representing the predicted state; and the product of the inverse matrix of the first prediction matrix and the state volume information is determined as the second target state information representing the accuracy.

[0019] In the above solution, the state covariance matrix is adjusted by the state adjustment factor, increasing the contribution degree of the state data to the filtering result when the observation is abnormal and decreasing the contribution degree of the state data to the filtering result when the observation is normal, so as to adjust the contribution degree of the state data in different scenarios and improve the navigation accuracy.

[0020] In one or more embodiments, the observation adjustment factor is determined by the following method:

[0021] The difference between the current observation value and the observation volume information is determined as the observation residual information;

[0022] Based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information, a residual value is determined;

[0023] Based on the comparison result between the residual value and the measurement noise threshold, determine the observation adjustment factor.

[0024] In the above solution, by comparing the residual value with the measurement noise threshold, the comparison result characterizes whether the observation is abnormal. Therefore, according to the comparison result, the observation adjustment factor used to adjust the observation noise variance matrix can be accurately determined.

[0025] In one or more embodiments, the target state information includes first target state information characterizing the predicted state; based on the target state information, the observation adjustment factor, and the observation noise variance matrix, determining the target observation information includes:

[0026] Determine the product of the reciprocal of the observation adjustment factor and the observation noise variance matrix as the second prediction matrix;

[0027] Determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information as the first target observation information characterizing the predicted observation; and

[0028] Determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix as the second target observation information characterizing the accuracy.

[0029] In the above solution, the observation noise variance matrix is adjusted by the observation adjustment factor, reducing the contribution of the observation data to the filtering result when the observation is abnormal and increasing the contribution of the observation data to the filtering result when the observation is normal, thereby adjusting the contribution of the observation data in different scenarios and improving the navigation accuracy.

[0030] In one or more embodiments, based on the target state information and the target observation information, determining the fusion state information of the inertial device and the satellite device includes:

[0031] Determine the sum of the first target state information and the first target observation information as the target prediction information; and, determine the sum of the second target state information and the second target observation information as the target accuracy information; where, the first target state information is the information characterizing the predicted state in the target state information, the second target state information is the information characterizing the accuracy in the target state information; the first target observation information is the information characterizing the predicted observation in the target observation information, and the second target observation information is the information characterizing the accuracy in the target observation information;

[0032] Determine the product of the inverse matrix of the target prediction information and the target accuracy information as the fusion state information.

[0033] In a second aspect, an embodiment of the present application provides a fusion state information determination device, including:

[0034] A state adjustment module, configured to determine target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on a current observation value, state cubature information, and the state covariance matrix; the current observation value is an observation value corresponding to a state quantity currently collected by an inertial device and a state quantity currently collected by a satellite device; the state covariance matrix is a covariance matrix corresponding to the state cubature information; the state cubature information is actual cubature information of the observation value;

[0035] An observation adjustment module, configured to determine target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is a covariance matrix corresponding to observation noise; the observation cubature information is predicted cubature information of the observation value;

[0036] A fusion module, configured to determine fusion state information of the inertial device and the satellite device based on the target state information and the target observation information.

[0037] In one or more embodiments, the state adjustment module is specifically configured to:

[0038] Determine a state residual information as a difference between the current observation value and a cubature value corresponding to the state cubature information;

[0039] Determine a residual vector based on the state residual information and a trace of the state covariance matrix;

[0040] Determine the state adjustment factor based on a comparison result between a norm of the residual vector and a preset adjustment coefficient.

[0041] In one or more embodiments, the state adjustment module is specifically configured to:

[0042] Determine a first prediction matrix as a product of a reciprocal of the state adjustment factor and the state covariance matrix;

[0043] Determine an inverse matrix of the first prediction matrix as a first target state information representing a predicted state; and determine a product of the inverse matrix of the first prediction matrix and the state cubature information as a second target state information representing accuracy.

[0044] In one or more embodiments, the observation adjustment module is specifically configured to:

[0045] Determine the difference between the current observation value and the observation volume information as the observation residual information;

[0046] Determine the residual value based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information;

[0047] Determine the observation adjustment factor based on the comparison result between the residual value and the measurement noise threshold.

[0048] In one or more embodiments, the target state information includes first target state information representing a predicted state; the observation adjustment module is specifically configured to:

[0049] Determine the product of the reciprocal of the observation adjustment factor and the observation noise variance matrix as the second prediction matrix;

[0050] Determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information as the first target observation information representing a predicted observation; and determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix as the second target observation information representing accuracy.

[0051] In one or more embodiments, the fusion module is specifically configured to:

[0052] Determine the sum of the first target state information and the first target observation information as the target prediction information; and determine the sum of the second target state information and the second target observation information as the target accuracy information; wherein, the first target state information is the information representing the predicted state in the target state information, the second target state information is the information representing the accuracy in the target state information; the first target observation information is the information representing the predicted observation in the target observation information, and the second target observation information is the information representing the accuracy in the target observation information;

[0053] Determine the product of the inverse matrix of the target prediction information and the target accuracy information as the fusion state information.

[0054] In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;

[0055] Wherein, the memory stores program code, and when the program code is executed by the processor, the processor is caused to execute the fusion state information determination method according to any one of the first aspect.

[0056] Fourthly, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method for determining the fusion state information as described in any item of the first aspect is implemented. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is the system architecture diagram provided by the embodiment of the present application;

[0059] Figure 2 It is the schematic flowchart of the first method for determining the fusion state information provided by the embodiment of the present application;

[0060] Figure 3 It is the schematic flowchart of a method for determining a state adjustment factor provided by the embodiment of the present application;

[0061] Figure 4 It is the schematic flowchart of a method for determining target state information provided by the embodiment of the present application;

[0062] Figure 5 It is the schematic flowchart of a method for determining an observation adjustment factor provided by the embodiment of the present application;

[0063] Figure 6 It is the schematic flowchart of a method for determining target observation information provided by the embodiment of the present application;

[0064] Figure 7 It is the schematic flowchart of the second method for determining the fusion state information provided by the embodiment of the present application;

[0065] Figure 8 It is the schematic structural diagram of the device for determining the fusion state information provided by the embodiment of the present application;

[0066] Figure 9 It is the schematic block diagram of the electronic device provided by the embodiment of the present application. Detailed Embodiments

[0067] To make the objectives, technical solutions, and advantages of this application more clear, the following will further describe this application in detail with reference to the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of this application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0068] In the description of this application, unless otherwise clearly specified and limited, the term "connection" shall be understood in a broad sense. For example, it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal connection of two devices. For those of ordinary skill in the art, the specific meaning of the above terms in this application can be understood according to specific circumstances.

[0069] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0070] In navigation technology, satellite navigation has high-precision positioning and no cumulative error, but is vulnerable to interference from the wireless communication environment; inertial navigation has high autonomy and anti-interference ability, but has a large cumulative error. By combining these two methods, higher-precision positioning can be achieved.

[0071] In related technologies, the Kalman filtering method is adopted, and the linear system state equation is used to obtain observation data from the system input / output to obtain the optimal state estimation of the linear system.

[0072] Traditional Kalman Filter (KF) uses the linear system state equation to obtain observation data from the system input / output to obtain the optimal state estimation of the linear system. However, the equations in the navigation system are non-linear, and KF is not applicable to the information fusion between inertial devices and satellite devices.

[0073] Extended Kalman Filter (EKF) linearly expands the current state to approximate the non-linear system, introducing high-order truncation errors. Calculating the Jacobian matrix is rather cumbersome, and EKF requires accurate prior noise statistical characteristics. However, in actual navigation, if there are situations such as occlusion, it will cause abnormal observation noise characteristics, resulting in a decrease or even divergence of the filtering accuracy.

[0074] The Unscented Kalman Filter (UKF) constructs a filtering algorithm based on deterministic sampling and approximates it with second-order Taylor precision. However, when the noise characteristics are uncertain, the filtering accuracy will be significantly affected.

[0075] The Cubature Kalman Filter (CKF) has a filtering process similar to that of the UKF. It uses sampling points with weights to approximate the mean of the state variables and predicts the system state at the next moment. The prediction process of the CKF is more rigorous and uses fewer sampling points.

[0076] In summary, the above Kalman filters all require an accurate mathematical model and accurate prior noise characteristics. During the filtering process, if the noise characteristics are abnormal, the filtering accuracy will decrease, affecting the navigation accuracy. For example, if there are abnormal observations such as signal occlusion, shadow effect, multipath effect, and gross observation, the positioning accuracy of navigation will be affected.

[0077] Based on this, the embodiments of the present application provide a method, device, electronic device, and storage medium for determining fused state information. The method includes: determining target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on the current observation value, state cubature information, and the state covariance matrix; the current observation value is the observation value corresponding to the state quantity currently collected by the inertial device and the state quantity currently collected by the satellite device; the state covariance matrix is the covariance matrix corresponding to the state cubature information; the state cubature information is the actual cubature information of the observation value; determining target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is the covariance matrix corresponding to the observation noise; the observation cubature information is the predicted cubature information of the observation value; determining the fused state information of the inertial device and the satellite device based on the target state information and the target observation information.

[0078] In the above solution, by introducing a state adjustment factor and an observation adjustment factor, the state covariance matrix and the observation noise variance matrix are adaptively adjusted during abnormal observations, and the contribution degrees of the state data and the observation data to the filtering result are adjusted respectively, improving the adaptability and anti-interference ability of the fusion algorithm, and accurately determining the fused state information of the inertial device and the satellite device, thereby improving the navigation accuracy.

[0079] Refer to Figure 1 As shown, it is a system architecture diagram of the navigation system provided by the embodiments of the present application. The navigation system includes an electronic device 100, an inertial device 200, and a satellite device 300;

[0080] The electronic device 100 is configured to obtain the state quantities collected by the inertial device 200 and the state quantities collected by the satellite device 300, and process the state quantities to obtain the fused state information.

[0081] The above navigation system is only an exemplary illustration. The navigation system is disposed in a mobile device (such as a vehicle, an aircraft, etc.) for navigation.

[0082] Next, in conjunction with the accompanying drawings and specific embodiments, the technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0083] The embodiment of the present application provides a first method for determining the fused state information, which is applied to the above-mentioned electronic device, such as Figure 2 As shown, the method may include:

[0084] Step S201: Determine the target state information based on the state adjustment factor and the state covariance matrix.

[0085] Wherein, the state adjustment factor is determined based on the current observation value, the state volume information, and the state covariance matrix; the current observation value is the observation value corresponding to the state quantity currently collected by the inertial device and the state quantity currently collected by the satellite device; the state covariance matrix is the covariance matrix corresponding to the state volume information; the state volume information is the actual volume information of the observation value.

[0086] In practice, factors such as signal occlusion, shadow effect, multipath effect, and observation coarseness will all affect the positioning accuracy of navigation. By adjusting the contribution degrees of the state data and the observation data to the filtering result respectively, the above factors' influence on navigation can be reduced;

[0087] Based on this, this embodiment needs to determine the state adjustment factor and adjust the state covariance matrix, so as to adjust the contribution degree of the state data to the filtering result; it also needs to determine the observation adjustment factor and adjust the observation noise variance matrix, so as to adjust the contribution degree of the observation data to the filtering result. More accurately determine the fused state information of the inertial device and the satellite device.

[0088] Based on the current observation value, the state volume information, and the state covariance matrix, the state adjustment factor for adjusting the state covariance matrix can be determined.

[0089] In one or more embodiments, the observation value is determined by the following method:

[0090] Observation value z = H*xt+U; where H is the measurement matrix, xt is the state matrix, and U is the observation noise;

[0091] In one or more embodiments, the measurement matrix is a vector consisting of the difference between the state quantity currently collected by the inertial device and the state quantity of the satellite device;

[0092] The state matrix is a vector composed of the state quantity currently collected by the inertial device and the error information of the satellite device.

[0093] In one or more embodiments, the state volume information is determined by:

[0094] 1) Based on Determine the first volume of each volume point; where the total number of volume points is 2n, x j is the first volume of volume point j, S is the volume matrix corresponding to the initial covariance matrix, j is the preset volume parameter of volume point j, is the current state value;

[0095] Where F is the state transfer matrix, xt is the state matrix, and W is the system noise;

[0096] Initial covariance matrix P0 = S*S T ; Among them, S T is the transposed matrix of the above volume matrix S;

[0097] 2) Based on the state transfer matrix, x of each volume point j Adjust to get x j ';

[0098] 3) Based on Determine state volume information

[0099] In one or more embodiments, the state covariance matrix is determined by:

[0100] State covariance matrix Where Q is the variance corresponding to the system noise.

[0101] Step S202: Determine target observation information based on the target state information, the observation adjustment factor, and the observation noise variance matrix.

[0102] The observation adjustment factor is determined based on the current observation value and the observation volume information; the observation noise variance matrix is the covariance matrix corresponding to the observation noise; and the observation volume information is the predicted volume information of the observation value.

[0103] As described above, this embodiment also needs to determine the observation adjustment factor and adjust the observation noise variance matrix, so as to adjust the contribution degree of the observation data to the filtering result.

[0104] Based on the above target state information, the current observation value, and the observation volume information, the observation adjustment factor used to adjust the observation noise variance matrix can be determined.

[0105] The determination method of the observation value can refer to the above embodiment and will not be elaborated here.

[0106] In one or more embodiments, the observation volume information is determined by the following method:

[0107] 1) Based on Determine the second volume amount of each volume point; where the total number of volume points is 2n, x j′ is the second volume amount of volume point j, S′ is the volume matrix corresponding to the first prediction matrix, § j is the preset volume parameter of volume point j, is the state volume information;

[0108] The first prediction matrix where S′ T is the transpose matrix of the above volume matrix S′;

[0109] The first prediction matrix and the state volume information The determination methods can refer to other embodiments and will not be elaborated here.

[0110] 2) Based on the measurement matrix, adjust the x j′ of each volume point to obtain z j′ ;

[0111] 3) Based on Determine the observation volume information

[0112] Step S203: Based on the target state information and the target observation information, determine the fusion state information of the inertial device and the satellite device.

[0113] In the above solution, by introducing the state adjustment factor and the observation adjustment factor, when the observation is abnormal, the state covariance matrix and the observation noise variance matrix are adaptively adjusted, and the contribution degrees of the state data and the observation data to the filtering result are adjusted respectively, so as to improve the self - adaptability and anti - interference ability of the fusion algorithm, accurately determine the fusion state information of the inertial device and the satellite device, and thus improve the navigation accuracy.

[0114] In one or more embodiments, the determination method of the above state adjustment factor can refer to Figure 3 as shown:

[0115] Step S301: Determine the state residual information as the difference between the current observed value and the volume value corresponding to the state volume information.

[0116] Exemplarily, the state residual information is the volume value corresponding to the state volume information , where z is the current observed value and H is the measurement matrix. z is the current observed value, and H is the measurement matrix.

[0117] Step S302: Determine the residual vector based on the state residual information and the trace of the state covariance matrix.

[0118] Exemplarily, the residual vector where is the state residual information, is the transpose matrix of the state residual information , and tr(P′) is the trace of the state covariance matrix.

[0119] Step S303: Determine the state adjustment factor based on the comparison result between the norm of the residual vector and the preset adjustment coefficient.

[0120] By comparing the norm of the residual vector with the preset adjustment coefficient, different comparison results correspond to different ways of determining the state adjustment factor. The following is illustrated with a specific example:

[0121] 1) If |△V| ≤ L, the state adjustment factor is 1;

[0122] 2) If |△V| > L, the state adjustment factor is L / |△V|;

[0123] where |△V| is the norm of the residual vector and L is the preset adjustment coefficient.

[0124] In the above solution, by comparing the norm of the residual vector with the preset adjustment coefficient, the comparison result characterizes whether the observation is abnormal. Therefore, according to the comparison result, the state adjustment factor for adjusting the state covariance matrix can be accurately determined.

[0125] In one or more embodiments, the method for determining the above target state information can be referred to Figure 4 as shown:

[0126] Step S401: Determine the first prediction matrix as the product of the reciprocal of the state adjustment factor and the state covariance matrix.

[0127] Exemplarily, the first prediction matrix where ak is the state adjustment factor and P′ is the state covariance matrix;

[0128] The determination methods of the state adjustment factor and the state covariance matrix can refer to the above embodiments and will not be elaborated here.

[0129] Step S402: Determine the inverse matrix of the first prediction matrix as the first target state information representing the predicted state; and determine the product of the inverse matrix of the first prediction matrix and the state cubature information as the second target state information representing the accuracy.

[0130] Exemplarily, the first target state information representing the predicted state

[0131] The second target state information representing the accuracy (also known as the Fisher information corresponding to the state)

[0132] Wherein, is the first prediction matrix, is the state cubature information, and The determination methods of can refer to the above embodiments and will not be elaborated here.

[0133] In the above solution, the state covariance matrix is adjusted by the state adjustment factor, increasing the contribution degree of the state data to the filtering result when the observation is abnormal and decreasing the contribution degree of the state data to the filtering result when the observation is normal, so as to adjust the contribution degree of the state data in different scenarios and improve the navigation accuracy.

[0134] In one or more embodiments, the determination method of the above observation adjustment factor can refer to Figure 5 as shown:

[0135] Step S501: Determine the observation residual information as the difference between the current observation value and the observation cubature information.

[0136] Exemplarily, the observation residual information Where z is the above observation value, is the observation cubature information; the determination methods of z and can refer to the above embodiments and will not be elaborated here.

[0137] Step S502: Determine the residual value based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information.

[0138] Exemplarily, the residual value Wherein, i is a preset adjustment value, |V k | is the norm of the observation residual information V k and η is the reciprocal of the weight of the observation residual information, is |Vk | and the median;

[0139] The method for determining the observed residual information can refer to the above embodiments and will not be elaborated here.

[0140] Step S503: Determine the observation adjustment factor based on the comparison result between the residual value and the measurement noise threshold.

[0141] In implementation, there can be one or more measurement noise thresholds. By comparing the residual value with each measurement noise threshold, different comparison results correspond to different ways of determining the observation adjustment factor. The following is illustrated with a specific example:

[0142] 1) If ek ≤ b1, the observation adjustment factor is 1;

[0143] 2) If b1 < ek < b2, the observation adjustment factor is

[0144] 3) If ek ≥ b2, the observation adjustment factor is 10 -30 ;

[0145] Wherein, the above ek is the residual value, b1 is the first measurement noise threshold, and b2 is the second measurement noise threshold.

[0146] In the above solution, by comparing the residual value with the measurement noise threshold, the comparison result characterizes whether the observation is abnormal. Therefore, according to the comparison result, the observation adjustment factor used to adjust the observation noise variance matrix can be accurately determined.

[0147] In one or more embodiments, the method for determining the above target observation information can refer to Figure 6 as shown:

[0148] Step S601: Determine the second prediction matrix by multiplying the reciprocal of the observation adjustment factor and the observation noise variance matrix.

[0149] Exemplarily, the second prediction matrix where mk is the observation adjustment factor, P k is the observation noise variance matrix;

[0150] The method for determining the observation adjustment factor can refer to the above embodiments and will not be elaborated here.

[0151] Step S602: Determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information as the first target observation information characterizing the predicted observation; and determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix as the second target observation information characterizing the accuracy.

[0152] Wherein, the first target state information is the information characterizing the predicted state in the target state information.

[0153] Exemplarily, the first target observation information characterizing the predicted state

[0154] The second target observation information characterizing the accuracy (also known as the Fisher information corresponding to the observation) C is the observation prediction matrix,

[0155] Wherein, z is the current observation value, and H is the measurement matrix, is the state cubature information. The determination methods of these parameters can refer to the above embodiments and will not be elaborated here.

[0156] In one or more embodiments, the above cross-covariance matrix can be determined in the following manner:

[0157] Cross-covariance matrix

[0158] Wherein, x j ′ is obtained by adjusting x of each cubature point based on the state transition matrix j , is the state cubature information, z j′ is obtained by adjusting x of each cubature point based on the measurement matrix j′ , is the observation cubature information. The determination methods of these parameters can refer to the above embodiments and will not be elaborated here.

[0159] In the above solution, the observation noise variance matrix is adjusted by the observation adjustment factor, the contribution degree of the observation data to the filtering result is reduced when the observation is abnormal, and the contribution degree of the observation data to the filtering result is increased when the observation is normal, so as to adjust the contribution degree of the observation data in different scenarios and improve the navigation accuracy.

[0160] The embodiment of the present application provides a second method for determining the fusion state information, which is applied to the above electronic device, as Figure 7 shown, and this method may include:

[0161] Step S701: Determine the target state information based on the state adjustment factor and the state covariance matrix.

[0162] Step S702: Determine the target observation information based on the target state information, the observation adjustment factor, and the observation noise variance matrix.

[0163] For the specific implementation manners of steps S701 - S702, reference may be made to the above - mentioned embodiments, and details are not described herein again.

[0164] Step S703: Determine the target prediction information as the sum of the first target state information and the first target observation information; and determine the target accuracy information as the sum of the second target state information and the second target observation information.

[0165] Wherein, the first target state information is the information representing the predicted state in the target state information, and the second target state information is the information representing the accuracy in the target state information; the first target observation information is the information representing the predicted observation in the target observation information, and the second target observation information is the information representing the accuracy in the target observation information.

[0166] Exemplarily, the target prediction information Y0 = Y + O; where Y is the first target state information, O is the first target observation information, and the determination methods of the first target state information and the first target observation information may refer to the above - mentioned embodiments, and details are not described herein again.

[0167] The target accuracy information y0 = y + o; where y is the second target state information, o is the second target observation information, and the determination methods of the second target state information and the second target observation information may refer to the above - mentioned embodiments, and details are not described herein again.

[0168] Step S704: Determine the fused state information as the product of the inverse matrix of the target prediction information and the target accuracy information.

[0169] Exemplarily, the fused state information X0=(Y0) -1 *y0; where (Y0) -1 is the inverse matrix of the target prediction information Y0, y0 is the target accuracy information, and the determination methods of the target prediction information and the target accuracy information may refer to the above - mentioned embodiments, and details are not described herein again.

[0170] The following is an illustration with a specific example:

[0171] Step 1: Determine the current observation value z based on z = H*xt + U; where H is the measurement matrix, xt is the state matrix, and U is the observation noise;

[0172] Based on Determine the current state value where F is the state transition matrix, xt is the state matrix, and W is the system noise;

[0173] Step 2: Based on Determine the first volume quantity of each volume point; where the total number of volume points is 2n, x j is the first volume quantity of volume point j, S is the volume matrix corresponding to the initial covariance matrix, § j is the preset volume parameter of volume point j, is the current state value;

[0174] Based on Determine the state volume information where x j ′ is obtained by adjusting x of each volume point based on the state transition matrix j ;

[0175] Step 3: Based on Determine the state covariance matrix P′; where Q is the variance corresponding to the system noise;

[0176] Step 4: Based on Determine the state residual information where z is the current observed value, is the volume value corresponding to the state volume information ;

[0177] Based on Determine the residual vector △V; where, is the state residual information, is the state residual information of the transposed matrix, and tr(P′) is the trace of the state covariance matrix;

[0178] If |△V| > L, the state adjustment factor is L / |△V|; if |△V| ≤ L, the state adjustment factor is 1;

[0179] Step 5: Based on Determine the first prediction matrix where ak is the state adjustment factor and P′ is the state covariance matrix;

[0180] Step 6: Based on Determine the first target state information Y;

[0181] Based on Determine the second target state information y;

[0182] where, is the first prediction matrix, is the state volume information;

[0183] Step 7: Based on determine the second volume quantity of each volume point; where the total number of volume points is 2n, and x j′ is the second volume quantity of volume point j, S′ is the first prediction matrix corresponding volume matrix, and § j is the preset volume parameter of volume point j, is the state volume information;

[0184] Step 8: Based on determine the observed volume information where z j′ is obtained by adjusting the second volume quantity x of volume point j based on the measurement matrix j′ ;

[0185] Step 9: Based on determine the observed residual information V k ; where z is the above-mentioned observed value, is the observed volume information;

[0186] Based on determine the residual value ek; where i is the preset adjustment value, |V k | is the modulus of the observed residual information V k , η is the reciprocal of the weight of the observed residual information, is the median of |V k | and ;

[0187] If ek ≤ b1, the observation adjustment factor is 1; if b1 < ek < b2, the observation adjustment factor is If ek ≥ b2, the observation adjustment factor is 10 -30 ;

[0188] Step 10: Based on determine the second prediction matrix [[ID=6m1]] where mk is the observation adjustment factor, and P k is the observation noise variance matrix;

[0189] Step 11: Based on determine the first target observation information O;

[0190] Based on determine the second target observation information o; C is the observation prediction matrix, z is the current observed value, H is the measurement matrix, is the state volume information;

[0191] It should be noted that there seems to be a minor error in the tag "ID=6m1" which might be a typo. It should probably be "ID=61" for better consistency.Step 12. Determine the target prediction information Y0 based on Y0 = Y + O, where Y is the first target state information and O is the first target observation information;

[0192] Determine the target accuracy information y0 based on y0 = y + o, where y is the second target state information and o is the second target observation information;

[0193] Step 13. Determine the fusion state information X0 based on X0 = (Y0) -1 *y0, where (Y0) -1 is the inverse matrix of the target prediction information Y0, and y0 is the target accuracy information.

[0194] The above example is only a feasible implementation for determining the fusion state information, and this embodiment is not limited thereto.

[0195] As Figure 8 shown, based on the same inventive concept, an embodiment of the present application provides a fusion state information determination device 800, including:

[0196] A state adjustment module 801, configured to determine target state information based on a state adjustment factor and a state covariance matrix, where the state adjustment factor is determined based on a current observation value, state volume information, and the state covariance matrix; the current observation value is an observation value corresponding to a state quantity currently collected by an inertial device and a state quantity currently collected by a satellite device; the state covariance matrix is a covariance matrix corresponding to the state volume information; the state volume information is actual volume information of the observation value;

[0197] An observation adjustment module 802, configured to determine target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix, where the observation adjustment factor is determined based on the current observation value and observation volume information; the observation noise variance matrix is a covariance matrix corresponding to observation noise; the observation volume information is predicted volume information of the observation value;

[0198] A fusion module 803, configured to determine the fusion state information of the inertial device and the satellite device based on the target state information and the target observation information.

[0199] In one or more embodiments, the state adjustment module 801 is specifically configured to:

[0200] Determine the difference between the current observation value and the volume value corresponding to the state volume information as the state residual information;

[0201] Determine a residual vector based on the state residual information and the trace of the state covariance matrix;

[0202] Determine the state adjustment factor based on the comparison result between the norm of the residual vector and a preset adjustment coefficient.

[0203] In one or more embodiments, the state adjustment module 801 is specifically configured to:

[0204] Determine the first prediction matrix as the product of the reciprocal of the state adjustment factor and the state covariance matrix;

[0205] Determine the inverse matrix of the first prediction matrix as the first target state information representing the predicted state; and determine the product of the inverse matrix of the first prediction matrix and the state cubature information as the second target state information representing the accuracy.

[0206] In one or more embodiments, the observation adjustment module 802 is specifically configured to:

[0207] Determine the observation residual information as the difference between the current observation value and the observation cubature information;

[0208] Determine the residual value based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information;

[0209] Determine the observation adjustment factor based on the comparison result between the residual value and the measurement noise threshold.

[0210] In one or more embodiments, the target state information includes the first target state information representing the predicted state; the observation adjustment module 802 is specifically configured to:

[0211] Determine the second prediction matrix as the product of the reciprocal of the observation adjustment factor and the observation noise variance matrix;

[0212] Determine the first target observation information representing the predicted observation as the product among the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information; and determine the second target observation information representing the accuracy as the product among the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix.

[0213] In one or more embodiments, the fusion module 803 is specifically configured to:

[0214] Determine the sum of the first target state information and the first target observation information as the target prediction information; and, determine the sum of the second target state information and the second target observation information as the target accuracy information; wherein, the first target state information is the information representing the predicted state in the target state information, and the second target state information is the information representing the accuracy in the target state information; the first target observation information is the information representing the predicted observation in the target observation information, and the second target observation information is the information representing the accuracy in the target observation information;

[0215] Determine the product of the inverse matrix of the target prediction information and the target accuracy information as the fused state information.

[0216] Since this device is the device in the method of the embodiments of the present application, and the principle of the device to solve problems is similar to that of the method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0217] As Figure 9 shown, based on the same inventive concept, an embodiment of the present application provides an electronic device 900, including: a processor 901 and a memory 902;

[0218] The memory 902 may be a volatile memory, such as a random-access memory (RAM); the memory 902 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 902 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 902 may be a combination of the above memories.

[0219] The processor 901 may include one or more central processing units (CPUs), a graphics processing unit (GPU), or a digital processing unit, etc.

[0220] In the embodiments of the present application, the specific connection medium between the memory 902 and the processor 901 is not limited. In the embodiments of the present application Figure 9 it is connected by a bus 903 between the memory 902 and the processor 901, and the bus 903 is in Figure 9The middle is represented by a thick line, and the bus 903 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 9 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0221] Among them, the memory 902 stores program codes. When the program codes are executed by the processor 901, the processor 901 is caused to execute the following processes:

[0222] Determine target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on a current observation value, state cubature information, and the state covariance matrix; the current observation value is an observation value corresponding to a state quantity currently collected by an inertial device and a state quantity currently collected by a satellite device; the state covariance matrix is a covariance matrix corresponding to the state cubature information; the state cubature information is actual cubature information of the observation value;

[0223] Determine target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is a covariance matrix corresponding to observation noise; the observation cubature information is predicted cubature information of the observation value;

[0224] Determine the fusion state information of the inertial device and the satellite device based on the target state information and the target observation information.

[0225] In one or more embodiments, the processor 901 specifically executes:

[0226] Determine the difference between the current observation value and the cubature value corresponding to the state cubature information as state residual information;

[0227] Determine a residual vector based on the state residual information and the trace of the state covariance matrix;

[0228] Determine the state adjustment factor based on the comparison result between the norm of the residual vector and a preset adjustment coefficient.

[0229] In one or more embodiments, the processor 901 specifically executes:

[0230] Determine a first prediction matrix by multiplying the reciprocal of the state adjustment factor and the state covariance matrix;

[0231] Determine an inverse matrix of the first prediction matrix as a first target state information representing a predicted state; and determine a product of the inverse matrix of the first prediction matrix and the state cubature information as a second target state information representing accuracy.

[0232] In one or more embodiments, the processor 901 specifically performs:

[0233] Determine the difference between the current observation value and the observation volume information as the observation residual information;

[0234] Based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information, determine the residual value;

[0235] Based on the comparison result between the residual value and the measurement noise threshold, determine the observation adjustment factor.

[0236] In one or more embodiments, the target state information includes first target state information representing a predicted state; the processor 901 specifically performs:

[0237] Determine the product of the reciprocal of the observation adjustment factor and the observation noise variance matrix as the second prediction matrix;

[0238] Determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information as the first target observation information representing the predicted observation; and determine the product of the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix as the second target observation information representing the accuracy.

[0239] In one or more embodiments, the processor 901 specifically performs:

[0240] Determine the sum of the first target state information and the first target observation information as the target prediction information; and determine the sum of the second target state information and the second target observation information as the target accuracy information; wherein, the first target state information is the information representing the predicted state in the target state information, the second target state information is the information representing the accuracy in the target state information; the first target observation information is the information representing the predicted observation in the target observation information, and the second target observation information is the information representing the accuracy in the target observation information;

[0241] Determine the product of the inverse matrix of the target prediction information and the target accuracy information as the fusion state information.

[0242] Since this electronic device is the electronic device that executes the method in the embodiments of the present application, and the principle of the electronic device to solve the problem is similar to that of the method, the implementation of the electronic device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0243] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned fusion state information determination method are implemented. Among them, the readable storage medium can be a non-volatile readable storage medium.

[0244] The present application has been described above with reference to the block diagrams and / or flowcharts showing methods, apparatuses (systems) and / or computer program products according to embodiments of the present application. It should be understood that one block of the block diagrams and / or flowcharts and combinations of blocks in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, and / or other programmable devices to generate a machine, so that the instructions executed by the computer processor and / or other programmable devices create a method for implementing the functions / actions specified in the blocks of the block diagrams and / or flowcharts.

[0245] Correspondingly, the present application can also be implemented by hardware and / or software (including firmware, resident software, microcode, etc.). Further, the present application can take the form of a computer program product on a computer-usable or computer-readable storage medium, which has computer-usable or computer-readable program code implemented in the medium for use by an instruction execution system or in conjunction with an instruction execution system. In the context of the present application, a computer-usable or computer-readable medium can be any medium that can contain, store, communicate, transmit, or convey a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0246] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0247] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining a fusion state information, characterized in that, The method includes: Determining target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on the current observation value, state cubature information, and the state covariance matrix; the current observation value is the observation value corresponding to the state quantity currently collected by the inertial device and the state quantity currently collected by the satellite device; the state covariance matrix is the covariance matrix corresponding to the state cubature information; the state cubature information is the actual cubature information of the observation value; Determining target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is the covariance matrix corresponding to the observation noise; the observation cubature information is the predicted cubature information of the observation value; Determining the fusion state information of the inertial device and the satellite device based on the target state information and the target observation information.

2. The method according to claim 1, wherein, The state adjustment factor is determined by the following method: Determining the state residual information as the difference between the current observation value and the cubature value corresponding to the state cubature information; Determining a residual vector based on the state residual information and the trace of the state covariance matrix; Determining the state adjustment factor based on the comparison result between the norm of the residual vector and a preset adjustment coefficient.

3. The method according to claim 1, characterized in that, Determining target state information based on a state adjustment factor and a state covariance matrix includes: Determining a first prediction matrix as the product of the reciprocal of the state adjustment factor and the state covariance matrix; Determining the inverse matrix of the first prediction matrix as the first target state information representing the predicted state; and determining the product of the inverse matrix of the first prediction matrix and the state cubature information as the second target state information representing the accuracy.

4. The method according to claim 1, wherein The observation adjustment factor is determined by the following method: Determining the observation residual information as the difference between the current observation value and the observation cubature information; Determining a residual value based on the norm of the observation residual information and the reciprocal of the weight of the observation residual information; Determining the observation adjustment factor based on the comparison result between the residual value and a measurement noise threshold.

5. The method according to claim 1, characterized in that, The target state information includes the first target state information representing the predicted state; Determining target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix includes: Determining a second prediction matrix as the product of the reciprocal of the observation adjustment factor and the observation noise variance matrix; Determining the first target observation information representing the predicted observation as the product among the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, the transpose matrix of the cross-covariance matrix, and the transpose matrix of the first target state information; and Determining the second target observation information representing the accuracy as the product among the first target state information, the cross-covariance matrix, the inverse matrix of the second prediction matrix, and the observation prediction matrix.

6. The method according to any one of claims 1 to 5, characterized in that Determining the fusion state information of the inertial device and the satellite device based on the target state information and the target observation information includes: Determine the target prediction information as the sum of the first target state information and the first target observation information; and determine the target accuracy information as the sum of the second target state information and the second target observation information; wherein, the first target state information is the information in the target state information that represents the predicted state, and the second target state information is the information in the target state information that represents the accuracy; the first target observation information is the information in the target observation information that represents the predicted observation, and the second target observation information is the information in the target observation information that represents the accuracy; Determine the fused state information as the product of the inverse matrix of the target prediction information and the target accuracy information.

7. A fusion state information determination device, characterized in that, The apparatus includes: A state adjustment module, configured to determine target state information based on a state adjustment factor and a state covariance matrix; wherein, the state adjustment factor is determined based on the current observation value, state cubature information, and the state covariance matrix; the current observation value is the observation value corresponding to the state quantity currently collected by the inertial device and the state quantity currently collected by the satellite device; the state covariance matrix is the covariance matrix corresponding to the state cubature information; the state cubature information is the actual cubature information of the observation value; An observation adjustment module, configured to determine target observation information based on the target state information, an observation adjustment factor, and an observation noise variance matrix; wherein, the observation adjustment factor is determined based on the current observation value and observation cubature information; the observation noise variance matrix is the covariance matrix corresponding to the observation noise; the observation cubature information is the predicted cubature information of the observation value; A fusion module, configured to determine the fused state information of the inertial device and the satellite device based on the target state information and the target observation information.

8. The device according to claim 7, characterized in that, The state adjustment module is specifically configured to: Determine the state residual information as the difference between the current observation value and the cubature value corresponding to the state cubature information; Determine the residual vector based on the state residual information and the trace of the state covariance matrix; Determine the state adjustment factor based on the comparison result between the norm of the residual vector and a preset adjustment coefficient.

9. An electronic device, characterized in that, It includes: A processor and a memory; Wherein, the memory stores program codes, and when the program codes are executed by the processor, the processor is caused to execute the fused state information determination method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the fused state information determination method according to any one of claims 1 to 6 is implemented.

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