Vehicle cooperative navigation positioning method and device based on entropy information driving

Abstract

The application discloses a vehicle cooperative navigation positioning method and device based on entropy information driving and belongs to the technical field of navigation positioning. The method comprises the following steps: a cooperative relative navigation positioning model is constructed; a plurality of sub-filters are used to process relative ranging information of each cooperative positioning device through a filter to obtain a plurality of sub-state estimation results, and a measurement noise covariance is adaptively estimated according to a new information variance matrix; the measurement noise covariance and the plurality of sub-state estimation results are globally fused to obtain a global state estimation result; mutual information between each sub-state and the global state is calculated, and an adaptive information distribution factor is constructed according to the mutual information; and parameters of each sub-filter are redistributed based on the factor. Through the introduction of the adaptive distribution mechanism based on the mutual information, the robustness of positioning, the adaptive ability to dynamic noise, and the efficiency and accuracy of multi-source information fusion are improved.

Description

Technical Field

[0001] This invention belongs to the field of navigation and positioning technology, specifically relating to a vehicle cooperative navigation and positioning method and device driven by entropy information. Background Technology

[0002] Currently, autonomous vehicles primarily rely on their own sensors, such as inertial navigation systems (INS), lidar (Light Detection and Ranging, LiDAR), and visual cameras, to estimate relative or absolute pose for autonomous navigation and positioning. It is understandable that the accuracy of autonomous navigation and positioning for autonomous vehicles directly affects their safety and efficiency.

[0003] However, in harsh weather conditions (such as heavy fog or rain) or complex environments like urban canyons, vehicle navigation and positioning performance deteriorates drastically. For example, severe weather can significantly attenuate lidar signals and obstruct camera views, leading to feature extraction failures or the generation of excessive noise; urban canyon environments can cause multipath effects, lock-off, or outliers in Global Navigation Satellite System (GNSS) signals. These factors often result in pose drift, tracking loss, or even navigation failure for autonomous navigation methods used by individual vehicles.

[0004] Thus, in complex environments, the degradation of sensor information quality and noise interference can lead to poor accuracy, continuity, and reliability of vehicle navigation and positioning. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present invention aims to provide a vehicle cooperative navigation and positioning method and device based on entropy information to solve the problem of poor accuracy, continuity and reliability of vehicle navigation and positioning in complex environments.

[0006] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a vehicle cooperative navigation and positioning method based on entropy information. The method includes: constructing a cooperative relative navigation and positioning model, wherein the cooperative relative navigation and positioning model performs state prediction based on the inertial navigation system mounted on the vehicle to obtain state prediction values, and generates measurement prediction values ​​based on the state prediction values; estimating the measurement noise covariance based on the innovation variance matrix between the measurement information and the measurement prediction values ​​using filters in the cooperative relative navigation and positioning model, and obtaining at least one sub-state estimation result corresponding to at least one cooperative positioning device using at least one sub-filter, wherein the measurement information is the relative distance information between the vehicle and at least one cooperative positioning device, and the filters include at least one sub-filter, with each sub-filter corresponding to at least one cooperative positioning device; performing global fusion based on the measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result, and obtaining a vehicle relative navigation and positioning result based on the global state estimation result; calculating target mutual information based on at least one sub-state estimation result and the global state estimation result; constructing an adaptive information allocation factor based on the target mutual information, and reallocating the parameters of each sub-filter in the at least one sub-filter based on the adaptive information allocation factor for subsequent vehicle relative navigation and positioning calculations.

[0007] Furthermore, at least one of the aforementioned sub-filters employs a capacitive Kalman filter algorithm.

[0008] Furthermore, the above-mentioned global fusion based on measurement noise covariance and at least one sub-state estimation result to obtain global state estimation result includes: using an interactive multi-model approach, global fusion based on measurement noise covariance and at least one sub-state estimation result to obtain global state estimation result.

[0009] Furthermore, the aforementioned target mutual information includes the mutual information between each sub-state estimation result in at least one sub-state estimation result and the global state estimation result; the target mutual information is calculated based on at least one sub-state estimation result and the global state estimation result, including: for each sub-state estimation result in at least one sub-state estimation result, calculating its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculating the target mutual information based on the calculated entropy function and conditional entropy function.

[0010] Furthermore, the above-mentioned adaptive information allocation factor based on target mutual information includes: normalizing the mutual information between each sub-state estimation result and the global state estimation result in at least one sub-state estimation result to obtain the adaptive information allocation factor corresponding to at least one sub-filter.

[0011] Furthermore, after obtaining the global state estimation results, the vehicle cooperative navigation and positioning method based on entropy information also includes: using the global state estimation results to perform feedback correction on the original output of the inertial navigation system.

[0012] Secondly, the present invention also provides a vehicle cooperative navigation and positioning device driven by entropy information. The device includes: a model building module, a filtering module, a fusion module, and an information allocation module. The model building module is used to build a cooperative relative navigation and positioning model. The cooperative relative navigation and positioning model performs state prediction based on the inertial navigation system on the vehicle to obtain state prediction values, and generates measurement prediction values ​​based on the state prediction values. The filtering module is used to estimate the measurement noise covariance based on the innovation variance matrix between the measurement information and the measurement prediction values ​​through the filters of the cooperative relative navigation and positioning model, and obtain at least one sub-state estimation result corresponding to at least one cooperative positioning device using at least one sub-filter. The measurement information is the vehicle and at least one cooperative positioning device. The system includes a relative ranging information between the devices, a filter comprising at least one sub-filter, each sub-filter corresponding to at least one cooperative positioning device; a fusion module for performing global fusion based on measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result, and obtaining a vehicle relative navigation and positioning result based on the global state estimation result; an information allocation module for calculating target mutual information based on at least one sub-state estimation result and the global state estimation result; and an information allocation module for constructing an adaptive information allocation factor based on the target mutual information, and reallocating the parameters of each sub-filter in the at least one sub-filter based on the adaptive information allocation factor for subsequent vehicle relative navigation and positioning calculations.

[0013] Furthermore, at least one of the aforementioned sub-filters employs a capacitive Kalman filter algorithm.

[0014] Furthermore, the aforementioned fusion module is specifically used to: employ an interactive multi-model approach, perform global fusion based on the measurement noise covariance and at least one sub-state estimation result, and obtain a global state estimation result.

[0015] Furthermore, the aforementioned target mutual information includes the mutual information between each sub-state estimation result in at least one sub-state estimation result and the global state estimation result; the information allocation module is specifically used to: for each sub-state estimation result in at least one sub-state estimation result, calculate its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculate the target mutual information based on the calculated entropy function and conditional entropy function.

[0016] Furthermore, the aforementioned information allocation module is specifically used to: normalize the mutual information between each sub-state estimation result and the global state estimation result in at least one sub-state estimation result to obtain an adaptive information allocation factor corresponding to at least one sub-filter.

[0017] Furthermore, the above-mentioned device also includes: a correction module; the correction module is used to perform feedback correction on the original output of the inertial navigation system using the global state estimation result after obtaining the global state estimation result.

[0018] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method described in the first aspect.

[0019] Fourthly, the present invention also provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0020] Fifthly, the present invention also provides a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0021] In a sixth aspect, the present invention also provides a computer program product stored in a storage medium, which is executed by at least one processor to implement the method as described in the first aspect.

[0022] Compared with the prior art, the present invention has the following beneficial effects: On the one hand, by utilizing the relative ranging information provided by the cooperative positioning devices around the vehicle, effective external measurement information can still be obtained even in complex environments where the vehicle's own sensor performance is degraded or fails, thus ensuring the continuity and availability of navigation and positioning and overcoming the limitations of single-vehicle autonomous navigation. On the other hand, by adaptively estimating the measurement noise covariance using different strategies based on the changes in the innovation variance matrix, time-varying noise and outliers in the measurement information can be effectively identified and suppressed, improving the accuracy of sub-filter state estimation. Furthermore, by calculating the target mutual information between each sub-state estimation and the global state estimation results, and using this to construct an adaptive information allocation factor to dynamically adjust the parameters of each sub-filter, the contribution of different information sources can be quantified and utilized, allowing subsystems with high information quality to occupy a greater weight in the fusion, thereby improving the efficiency of multi-source information fusion and the accuracy of the final global state estimation result. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is one of the flowcharts of a vehicle cooperative navigation and positioning method based on entropy information provided by the present invention; Figure 2 This is a schematic diagram of a vehicle cooperative relative navigation and positioning structure for a multi-point cooperative positioning device provided by the present invention; Figure 3 This is a schematic diagram of the structure of a vehicle cooperative navigation and positioning device based on entropy information provided by the present invention; Figure 4 This is one of the hardware structure diagrams of an electronic device provided by the present invention; Figure 5 This is the second schematic diagram of the hardware structure of an electronic device provided by the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., used in this specification are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The entropy-information-driven vehicle cooperative navigation and positioning method and device provided by the present invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0028] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0029] (1) Cooperative positioning equipment: refers to auxiliary positioning units that can provide relative position or distance information in the vehicle's driving environment, in addition to the vehicle's own positioning system. These devices can be dynamic, such as other vehicles on the road; or static, such as various signal base stations (e.g., 5G base stations, ultra-wideband (UWB) base stations, or fixed-position positioning terminals set up on the roadside). It can be understood that by interacting with these devices, the vehicle can obtain reliable external measurement information for relative navigation and positioning when its own main positioning system (such as GNSS) signal is limited or fails.

[0030] (2) Innovation: refers to the difference between the actual measured value and the measured value predicted based on the system model during the filtering process. The magnitude and statistical characteristics of the innovation reflect the uncertainty of the model prediction and the value of external measurements. In this invention, the innovation and its variance matrix are the basis for judging whether the measurement noise characteristics have changed and for adaptively adjusting the filter parameters.

[0031] (3) Mutual information: This is a metric in information theory used to measure the correlation or shared information between two random variables. In this invention, mutual information is used to quantify the degree of information correlation between the sub-state estimation results (also known as local state estimates) provided by a single sub-filter and the global state estimation results obtained through fusion. The larger the mutual information value, the greater the contribution of the local estimate to the global estimate, and the more effective information it contains.

[0032] The vehicle cooperative navigation and positioning method based on entropy information driven by the present invention can be executed by a vehicle cooperative navigation and positioning device based on entropy information driven by the present invention. Exemplarily, this vehicle cooperative navigation and positioning device based on entropy information driven by the present invention can be an electronic device, or a component within that electronic device, such as an integrated circuit or a chip. The following will use an electronic device as an example to illustrate the vehicle cooperative navigation and positioning method based on entropy information driven by the present invention.

[0033] This invention provides a vehicle cooperative navigation and positioning method based on entropy information. Figure 1 A flowchart of a vehicle cooperative navigation and positioning method based on entropy information driven by the present invention is shown. This method can be applied to electronic devices. Figure 1 As shown, the vehicle cooperative navigation and positioning method based on entropy information driven by the present invention may include the following steps 101 to 105.

[0034] Step 101: Construct a cooperative relative navigation and positioning model.

[0035] The aforementioned cooperative relative navigation and positioning model can obtain state prediction values ​​based on the inertial navigation system on the vehicle, and generate measurement prediction values ​​based on the state prediction values.

[0036] In some embodiments of the present invention, a cooperative relative navigation and positioning model can be constructed by using the vehicle's inertial navigation system as the basis for state prediction and the relative ranging information provided by the cooperative positioning device as the external measurement information.

[0037] Specifically, the system state equation can be established based on the error model of inertial sensors such as gyroscopes and accelerometers on the vehicle, and combined with the relative distance measurement information between the cooperative positioning equipment on the road and the vehicle, to form a cooperative relative navigation and positioning model based on multi-point cooperative positioning equipment.

[0038] In some embodiments of the present invention, the state equation of the positioning system of a vehicle on the road can be determined by the following formula (1).

[0039] Formula (1) in, Sampling time; For the reason Time to The discrete state transition matrix at time t; This is noise in the discrete process; for The discrete state quantity at time t can be represented by the following formula (2).

[0040] Formula (2) in, This refers to the attitude error of the inertial sensor. The velocity error output by the inertial sensor; This refers to the position error output by the inertial sensor. This refers to random drift of the gyroscope. This refers to the random bias of the accelerometer.

[0041] It is understandable that on the road, in addition to the vehicle itself, there are various locationable signal base stations, other vehicles, roadside fixed terminal equipment and other equipment. Therefore, the vehicle can use the positioning information of such cooperative positioning equipment to replace its own sensors in harsh observation environments and build a cooperative relative navigation and positioning model for driving in order to achieve relative positioning.

[0042] In some embodiments of the present invention, the relative ranging information provided by the j-th cooperative positioning device can be represented by the following formula (3).

[0043] Formula (3) in, This indicates the current location of the vehicle. Let j be the location of the j-th cooperative positioning device; The relative distance between the currently moving vehicle and the j-th cooperative positioning device; This is for cooperative ranging noise.

[0044] In some embodiments of the present invention, when the cooperative positioning device provides relative ranging information, the cooperative positioning measurement equation obtained by other devices can be expressed by the following formula (5) through the following formula (4).

[0045] Formula (4) Formula (5) in, The location information of the vehicle based on the inertial navigation system; .

[0046] It should be noted that the above-mentioned cooperative positioning measurement equation refers to the model used in a multi-source fusion cooperative positioning system to describe the mathematical relationship between the observed values ​​and the carrier state (position, velocity, attitude, etc.). It is mainly used in estimation algorithms such as Kalman filtering to correct positioning errors by fusing data from multiple sensors or nodes.

[0047] Thus, based on the above formulas (1) and (5), as follows Figure 2 As shown, the cooperative relative navigation and positioning model of the vehicle based on the multi-point cooperative positioning device can be obtained.

[0048] Step 102: Using the filter of the cooperative relative navigation and positioning model, estimate the measurement noise covariance based on the innovation variance matrix between the measurement information and the measurement prediction value, and use at least one sub-filter to obtain at least one sub-state estimation result corresponding to at least one cooperative positioning device.

[0049] The aforementioned measurement information can be the relative distance information between the vehicle and at least one cooperative positioning device.

[0050] In this invention, the filter may include at least one sub-filter, which corresponds one-to-one with at least one cooperative positioning device.

[0051] In some embodiments of the present invention, after the cooperative relative navigation and positioning model is established, a distributed filtering architecture containing multiple sub-filters can be used to process the acquired measurement information.

[0052] In some embodiments of the present invention, since the innovation variance matrix between the measurement information and the measurement prediction value can reflect the real-time statistical characteristics of the measurement noise, the innovation variance matrix can be continuously monitored. By analyzing the changes in the innovation variance matrix, the quality of the current measurement data can be judged, and the noise model can be dynamically adjusted, thereby effectively suppressing the interference of outliers (outliers) and time-varying noise, and obtaining more reliable sub-state estimation results.

[0053] In some embodiments of the present invention, the at least one sub-filter described above may employ the Cubature Kalman Filter (CKF) algorithm.

[0054] Understandably, since vehicle motion models and relative distance measurement models are typically highly nonlinear, the traditional Extended Kalman Filter (EKF), which linearizes the nonlinear function through first-order Taylor expansion, introduces significant linearization errors. Therefore, this invention employs the CKF based on the third-order spherical radial volume criterion. It approximates the posterior probability density of the state using a set of volume points with equal weights, eliminating the need for complex Jacobian matrix calculations and achieving higher nonlinear approximation accuracy. Thus, using the CKF as the core algorithm for the sub-filter can more accurately handle nonlinear problems in the system, thereby improving the accuracy of sub-state estimation.

[0055] In some embodiments of the present invention, the filter of the cooperative relative navigation and positioning model of the vehicle can be initialized before estimating the measurement noise covariance.

[0056] In some embodiments of the present invention, the state prediction of the j-th cooperative positioning device sub-filter at time k can be solved by using the capacitive Kalman filter algorithm according to the following formula (6).

[0057] Formula (6) in, The weights of the volume points; n Indicates the number of volume points in the state; Let be the i-th transform volume point of sub-filter j at time k, which can be represented by the following formula (7).

[0058] Formula (7) in, Let be the state covariance matrix at time k-1, which can be obtained through the commutative Kalman filter equation; for The i-th column, The volume point is selected according to the volume rules.

[0059] Then, the transformation volume point of the predicted state can be calculated using the following formulas (8) and (9). .

[0060] Formula (8)

[0061] Formula (9) in, Let be the process noise covariance matrix in subfilter j at time k.

[0062] Furthermore, the measurement prediction value (point) in sub-filter j can be calculated using the following formula (10).

[0063] Formula (10) Finally, the new information variance matrix can be calculated using the following formula (11).

[0064] Formula (11) in, Let be the measurement information vector in subfilter j at time k.

[0065] In some embodiments of the present invention, the "estimation of measurement noise covariance" in step 102 above may specifically include steps A to C below.

[0066] Step A: Set the arithmetic sum of the innovation variance matrix at at least one time point as the covariance cumulative matrix, and calculate the measurement noise covariance of stationary noise based on the covariance cumulative matrix.

[0067] In some embodiments of the present invention, when the statistical characteristics of the measurement noise of the cooperative positioning device do not change significantly, it can be assumed that the innovation variance matrix at the current moment should have the same weight as the innovation variance matrix at previous moments. Therefore, a covariance cumulative matrix can be set. Let N be the arithmetic sum of the variance matrices of the new information at N time points, as shown in the following formula (12).

[0068] Formula (12) Based on this, the measurement noise covariance matrix of the cooperative positioning device can be calculated using the following formulas (13) to (15).

[0069] Formula (13) Formula (14) Formula (15) in, The measurement covariance matrix in sub-filter j when the measurement noise statistical characteristics of the cooperative positioning device do not change significantly; Let be the state-measurement covariance matrix in sub-filter j; Let be the measurement noise covariance matrix in subfilter j at time k.

[0070] In some embodiments of the present invention, based on the above formulas (14) and (15), the filter gain when the statistical characteristics of the measurement noise of the cooperative positioning device do not change significantly can be calculated by the following formula (16). .

[0071] Formula (16) Finally, using the following formulas (17) and (18), the corresponding sub-state estimate when the statistical characteristics of the measurement noise of the cooperative positioning device do not change significantly can be calculated. and its covariance .

[0072] Formula (17) Formula (18) Step B: Calculate the measurement noise covariance of abrupt change noise or outlier based on the mean of at least one historical information, the probability density function of a log-Gaussian distribution, and the log-correlation entropy.

[0073] In some embodiments of the present invention, when the statistical characteristics of the measurement noise of the cooperative positioning device change drastically, it can be assumed that the innovation variance matrix at the current moment should have different weights than the innovation variance matrix at previous moments. Therefore, the log-correlation entropy weighted formula (19) can be set as follows. .

[0074] Formula (19) in, The importance weight is used to measure the importance ratio of the corresponding information, and can be expressed by the following formula (20); The weights based on log-correlation entropy under drastic changes are used to measure the dispersion of the measurement variance statistics at the current time and can be expressed by the following formula (21).

[0075]

[0076] Formula (20) Formula (21) in, Let N be the average of historical updates; Let be the probability density function of the log-Gaussian distribution, which can be expressed by the following formula (22); The logarithmic correlation entropy can be expressed by the following formulas (23) and (24).

[0077] Formula (22) Formula (23) Formula (24) in, The kernel width is , which can be a diagonal matrix, where the diagonal elements are . Specifically, it can be calculated using the following formula (25).

[0078] Formula (25) Furthermore, the measurement noise covariance matrix of the cooperative positioning device when the noise statistical characteristics change drastically can be calculated using the following formulas (26) and (27).

[0079] Formula (26) Formula (27) The filter gain when the statistical characteristics of the measurement noise change drastically can be calculated from the above formulas (14) and (15), and it can be expressed by the following formula (28).

[0080] Formula (28) Finally, using the following formulas (29) and (30), the corresponding sub-state estimate when the measurement noise statistical characteristics of the cooperative positioning device change drastically can be calculated. and its covariance .

[0081] Formula (29) Formula (30) Step C: Fuse the measurement noise covariance of abrupt noise or outliers with the measurement noise covariance of stationary noise to obtain the fused measurement noise covariance.

[0082] It should be noted that for a detailed description of the fusion of measurement noise covariance, please refer to the following detailed description of formulas (31) and (32). It will not be repeated here.

[0083] Thus, through this dual-mode adaptive mechanism, the filter achieves intelligent switching to different noise environments, balancing estimation efficiency in steady-state conditions and robustness in drastic conditions.

[0084] Step 103: Perform global fusion based on the measurement noise covariance and at least one sub-state estimation result to obtain the global state estimation result, and obtain the vehicle relative navigation and positioning result based on the global state estimation result.

[0085] In some embodiments of the present invention, after obtaining at least one sub-state estimation result, the pre-processed positioning information from different information sources can be integrated by fusing the at least one sub-state estimation result with the measurement noise covariance to obtain a global state estimation result that is more accurate and stable than any single sub-state estimation. It is understood that this global state estimation result can be used to calculate the vehicle's relative navigation positioning result, such as calculating the vehicle's precise attitude, speed, and position.

[0086] In some embodiments of the present invention, step 103 may specifically include step 103a as described below.

[0087] Step 103a: Using an interactive multi-model approach, global fusion is performed based on the measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result, and the vehicle relative navigation and positioning result is obtained based on the global state estimation result.

[0088] In some embodiments of the present invention, since the vehicle may switch between multiple modes such as uniform linear motion, acceleration and turning, the Interactive Multiple Model (IMM) method can be used for global fusion.

[0089] It should be noted that the IMM algorithm can run multiple filters for different models in parallel and dynamically adjust the probability (or weight) of each model according to the degree of matching between each model and the actual measurement. Thus, in the fusion step, the outputs of each model filter can be weighted and combined according to their current probabilities to obtain a comprehensive and better global state estimation result, thereby improving the accuracy and robustness of the global state estimation result.

[0090] In some embodiments of the present invention, the calculation results of the above formulas (17), (18), (29) and (30) can be fused using the following formulas (31) and (32) to obtain the final sub-state estimate of the vehicle and the cooperative positioning device. and its covariance .

[0091] Formula (31) Formula (32) in, The correlation coefficient for fusion can be calculated using the following formula (33): Formula (33) in,

[0092]

[0093] In some embodiments of the present invention, after obtaining the global state estimation result, the vehicle cooperative navigation and positioning method based on entropy information driven by the present invention may further include the following step 106.

[0094] Step 106: Use the global state estimation results to perform feedback correction on the original output of the inertial navigation system.

[0095] In some embodiments of the present invention, due to its operating principle, the INS experiences drift errors that accumulate over time. Therefore, the global state estimation result includes accurate estimates of INS errors (such as attitude error, velocity error, and position error). Thus, using these error estimates to correct the original output of the INS can effectively suppress its drift, equivalent to using external collaborative information to continuously calibrate the INS online, thereby improving the long-term stability and accuracy of the navigation system.

[0096] In some embodiments of the present invention, after obtaining the final sub-state estimation results of all cooperative positioning devices, a federated filtering algorithm based on mutual information can be used to fuse and allocate the sub-filter states through the following formulas (34) and (35).

[0097] Formula (34) Formula (35) in, The covariance after fusion; This is the result of the fused global state estimation.

[0098] In some embodiments of the present invention, the overall state can be estimated using the following formulas (36) to (39). The position error, velocity error, and attitude error in the system are used to provide feedback correction to the output of the inertial navigation system.

[0099] Formula (36) Formula (37) Formula (38) Formula (39) in, , , These are the attitude, velocity, and position output by the inertial navigation system in the cooperative relative navigation and positioning system of the vehicle, respectively. , , These are the attitude, speed, and position of the vehicle in the cooperative relative navigation and positioning system, corrected by the cooperative positioning equipment.

[0100] Step 104: Calculate the target mutual information based on at least one sub-state estimation result and the global state estimation result.

[0101] In some embodiments of the present invention, target mutual information can quantify the information correlation between at least one sub-state estimation result and the global state estimation result, thereby intuitively reflecting the contribution of each sub-filter to the final fusion result.

[0102] In some embodiments of the present invention, the target mutual information may include mutual information between each sub-state estimation result in at least one sub-state estimation result and the global state estimation result.

[0103] In some embodiments of the present invention, step 104 may specifically include step 104a as described below.

[0104] Step 104a: For each sub-state estimation result in at least one sub-state estimation result, calculate its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculate the target mutual information based on the calculated entropy function and conditional entropy function.

[0105] In some embodiments of the present invention, the entropy function corresponding to each sub-state estimation result can characterize the uncertainty of the sub-state estimation result itself; the conditional entropy function between the sub-state estimation result and the global state estimation result can characterize the uncertainty of the global state after obtaining the sub-state estimation result; the mutual information between the two can represent the amount of reduction in global state uncertainty brought about by the sub-state estimation result, thereby accurately quantifying the information value provided by the sub-state estimation.

[0106] In some embodiments of the present invention, the entropy function of the sub-state estimation result can be calculated by the following formula (40), and the conditional entropy function between the sub-state estimation result and the global state estimation result can be calculated by the following formula (41).

[0107] Formula (40) Formula (41) in, and These represent the sub-state estimation result and the global state estimation result corresponding to the sub-filter, respectively; express The entropy function; express exist The conditional entropy function when known; Indicates reliance The number of sampling points obtained; M represents the number of samples; and Representing the global state respectively exist Conditional probability sum when known The marginal probability density function can be expressed by the following formulas (42) and (43).

[0108]

[0109] Formula (42)

[0110] Formula (43) Furthermore, the mutual information between the sub-state estimation results and the global state estimation results can be calculated using the following formula (44). .

[0111] Formula (44) Step 105: Based on the target mutual information, construct an adaptive information allocation factor, and reallocate the parameters of each sub-filter in at least one sub-filter based on the adaptive information allocation factor to perform subsequent vehicle relative navigation and positioning calculations.

[0112] In some embodiments of the present invention, the adaptive information allocation factor is a weighting coefficient used to indicate how the parameters of each sub-filter should be adjusted in the next round of navigation calculation.

[0113] Understandably, sub-filters with high contribution and good information quality can be assigned more trust and receive higher weight values, while sub-filters with low contribution and poor information quality will have their weight values ​​reduced accordingly.

[0114] Thus, through this parameter reallocation mechanism based on adaptive information allocation factors, the entire cooperative navigation and positioning system forms a closed-loop self-optimization process, which can intelligently adapt to dynamically changing environments and sensor characteristics, continuously perform subsequent vehicle relative navigation and positioning calculations, and thus maintain high performance in complex and challenging environments.

[0115] In some embodiments of the present invention, the step 105 above, "constructing an adaptive information allocation factor based on target mutual information", may specifically include the following step C.

[0116] Step C: Normalize the mutual information between each sub-state estimation result and the global state estimation result in at least one sub-state estimation result to obtain the adaptive information allocation factor corresponding to at least one sub-filter.

[0117] In some embodiments of the present invention, the adaptive information allocation factor can be calculated using the following formula (45).

[0118] Formula (45) Where J is the number of all sub-filters.

[0119] Then, the sub-filter parameters corresponding to the cooperative positioning device are reallocated using the following formulas (46) and (47).

[0120] Formula (46) Formula (47) in, This represents the process noise covariance matrix corresponding to the global state estimation result, which can be set according to the initial error of the inertial sensor. The process noise covariance matrix corresponding to the reallocated sub-filter j; Let be the covariance of the sub-filter j after redistribution.

[0121] Understandably, the reallocated sub-filter parameters can be used for the next calculation of navigation and positioning information by the cooperative positioning device.

[0122] Thus, in complex environments, by using cooperative positioning devices to replace the sensors on the vehicle itself, and by introducing a robust filtering subsystem based on logarithmic Gaussian entropy and a global information fusion filtering system based on mutual information into the filter, abnormal measurements, time-varying noise and system uncertainties in complex environments can be dynamically suppressed, thereby improving the accuracy and reliability of the cooperative relative navigation and positioning system.

[0123] Each of the above-described method embodiments, or various possible implementations of each method embodiment, can be executed individually or in combination of any two or more. The specific implementation can be determined according to actual usage requirements, and the present invention does not impose any restrictions on this.

[0124] The vehicle cooperative navigation and positioning method based on entropy information provided by this invention can be executed by a vehicle cooperative navigation and positioning device based on entropy information. This invention uses the execution of the vehicle cooperative navigation and positioning method based on entropy information by a vehicle cooperative navigation and positioning device based on entropy information as an example to illustrate the vehicle cooperative navigation and positioning device based on entropy information provided by this invention.

[0125] Figure 3 A schematic diagram of a possible structure of the vehicle cooperative navigation and positioning device based on entropy information driven by the present invention is shown. Figure 3 As shown, the vehicle cooperative navigation and positioning device 30 based on entropy information can include: a model building module 31, a filtering module 32, a fusion module 33, and an information distribution module 34.

[0126] The model building module 31 is used to build a cooperative relative navigation and positioning model. This model predicts the state based on the inertial navigation system mounted on the vehicle, and then generates measurement prediction values ​​based on these predictions. The filtering module 32 is used to estimate the measurement noise covariance using the filters of the cooperative relative navigation and positioning model, based on the innovation variance matrix between the measurement information and the measurement prediction values. It also uses at least one sub-filter to obtain at least one sub-state estimation result corresponding to at least one cooperative positioning device. The measurement information is the relative ranging information between the vehicle and at least one cooperative positioning device. The filters include at least one sub-filter. Each filter corresponds one-to-one with at least one cooperative positioning device; the fusion module 33 is used to perform global fusion based on the measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result, and to obtain a vehicle relative navigation positioning result based on the global state estimation result; the information allocation module 34 is used to calculate the target mutual information based on at least one sub-state estimation result and the global state estimation result; the information allocation module 34 is also used to construct an adaptive information allocation factor based on the target mutual information, and to reallocate the parameters of each sub-filter in at least one sub-filter based on the adaptive information allocation factor for subsequent vehicle relative navigation positioning calculation.

[0127] In one possible implementation, at least one of the sub-filters mentioned above employs a capacitive Kalman filter algorithm.

[0128] In one possible implementation, the aforementioned fusion module 33 is specifically used to: employ an interactive multi-model approach to perform global fusion based on the measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result.

[0129] In one possible implementation, the target mutual information includes the mutual information between each sub-state estimation result in at least one sub-state estimation result and the global state estimation result; the information allocation module 34 is specifically used to: for each sub-state estimation result in at least one sub-state estimation result, calculate its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculate the target mutual information based on the calculated entropy function and conditional entropy function.

[0130] In one possible implementation, the information allocation module 34 is specifically used to: normalize the mutual information between each sub-state estimation result and the global state estimation result in at least one sub-state estimation result to obtain an adaptive information allocation factor corresponding to at least one sub-filter.

[0131] In one possible implementation, the above-mentioned device further includes: a correction module; the correction module is used to perform feedback correction on the original output of the inertial navigation system using the global state estimation result after obtaining the global state estimation result.

[0132] This invention provides a vehicle cooperative navigation and positioning device driven by entropy information. On the one hand, by utilizing the relative ranging information provided by cooperative positioning devices around the vehicle, effective external measurement information can still be obtained even in complex environments where the vehicle's own sensor performance is degraded or fails, thus ensuring the continuity and availability of navigation and positioning and overcoming the limitations of single-vehicle autonomous navigation. On the other hand, by adaptively estimating the measurement noise covariance using different strategies based on the changes in the innovation variance matrix, time-varying noise and outliers in the measurement information can be effectively identified and suppressed, improving the accuracy of sub-filter state estimation. Furthermore, by calculating the target mutual information between each sub-state estimation and the global state estimation result, and using this to construct an adaptive information allocation factor to dynamically adjust the parameters of each sub-filter, the contribution of different information sources can be quantified and utilized, allowing subsystems with high information quality to occupy a greater weight in the fusion, thereby improving the efficiency of multi-source information fusion and the accuracy of the final global state estimation result.

[0133] The vehicle cooperative navigation and positioning device based on entropy information driven in this invention can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This invention does not impose specific limitations.

[0134] The vehicle cooperative navigation and positioning device based on entropy information in this invention can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this invention does not specifically limit it.

[0135] The vehicle cooperative navigation and positioning device based on entropy information driven by the present invention can realize the various processes implemented in the above-described embodiments of the vehicle cooperative navigation and positioning method based on entropy information driven by the present invention, and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0136] Optionally, such as Figure 4 As shown, the present invention also provides an electronic device 400, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described embodiment of the vehicle cooperative navigation and positioning method based on entropy information and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0137] It should be noted that the electronic devices in this invention include the mobile electronic devices and non-mobile electronic devices described above.

[0138] Figure 5 A schematic diagram of the hardware structure of an electronic device for implementing the present invention.

[0139] The electronic device 400 includes, but is not limited to, components such as: processor 401, memory 402, radio frequency unit 403, network module 404, audio output unit 405, input unit 406, sensor 407, display unit 408, user input unit 409, and interface unit 410.

[0140] Those skilled in the art will understand that the electronic device 400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 401 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0141] The processor 401 is used to construct a cooperative relative navigation and positioning model. This model predicts the state based on the inertial navigation system mounted on the vehicle, and generates measurement prediction values ​​based on these predictions. The processor 401 is also used to estimate the measurement noise covariance using filters in the cooperative relative navigation and positioning model, based on the innovation variance matrix between the measurement information and the measurement prediction values. Furthermore, it uses at least one sub-filter to obtain at least one sub-state estimation result corresponding to at least one cooperative positioning device. The measurement information is the relative ranging information between the vehicle and at least one cooperative positioning device. The filters include at least one sub-filter and at least one sub-state estimation result. Each filter corresponds one-to-one with at least one cooperative positioning device; processor 401 is used to perform global fusion based on measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result, and to obtain a vehicle relative navigation positioning result based on the global state estimation result; processor 401 is used to calculate target mutual information based on at least one sub-state estimation result and the global state estimation result; processor 401 is also used to construct an adaptive information allocation factor based on the target mutual information, and to reallocate the parameters of each sub-filter in at least one sub-filter based on the adaptive information allocation factor for subsequent vehicle relative navigation positioning calculation.

[0142] In one possible implementation, at least one of the sub-filters mentioned above employs a capacitive Kalman filter algorithm.

[0143] In one possible implementation, the processor 401 is specifically used to: employ an interactive multi-model to perform global fusion based on the measurement noise covariance and at least one sub-state estimation result to obtain a global state estimation result.

[0144] In one possible implementation, the target mutual information includes mutual information between each sub-state estimation result in at least one sub-state estimation result and the global state estimation result; the processor 401 is specifically used to: for each sub-state estimation result in at least one sub-state estimation result, calculate its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculate the target mutual information based on the calculated entropy function and conditional entropy function.

[0145] In one possible implementation, the processor 401 is specifically used to: normalize the mutual information between each sub-state estimation result and the global state estimation result in at least one sub-state estimation result to obtain an adaptive information allocation factor corresponding to at least one sub-filter.

[0146] In one possible implementation, the above-mentioned device further includes: a correction module; the correction module is used to perform feedback correction on the original output of the inertial navigation system using the global state estimation result after obtaining the global state estimation result.

[0147] It should be understood that, in this invention, the input unit 406 may include a graphics processing unit (GPU) 4061 and a microphone 4062. The GPU 4061 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 408 may include a display panel 4081, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 409 includes at least one of a touch panel 4091 and other input devices 4092. The touch panel 4091 is also called a touch screen. The touch panel 4091 may include two parts: a touch detection device and a touch controller. Other input devices 4092 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0148] The memory 402 can be used to store software programs and various data. The memory 402 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 402 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 402 in this invention includes, but is not limited to, these and any other suitable types of memory.

[0149] Processor 401 may include one or more processing units; optionally, processor 401 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 401.

[0150] The present invention also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described embodiments of the vehicle cooperative navigation and positioning method based on entropy information, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0151] The processor mentioned above is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. The present invention also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above embodiments of the vehicle cooperative navigation and positioning method based on entropy information, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0152] It should be understood that the chip mentioned in this invention may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0153] This invention provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described embodiment of the vehicle cooperative navigation and positioning method based on entropy information, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0154] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0156] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A vehicle cooperative navigation and positioning method based on entropy information, characterized in that, include: A cooperative relative navigation and positioning model is constructed. The cooperative relative navigation and positioning model obtains state prediction values ​​based on the state prediction value of the inertial navigation system on the vehicle, and generates measurement prediction values ​​based on the state prediction value. The measurement noise covariance is estimated by using the filter of the cooperative relative navigation and positioning model based on the innovation variance matrix between the measurement information and the measurement prediction value, and at least one sub-state estimation result corresponding to at least one cooperative positioning device is obtained by using at least one sub-filter. The measurement information is the relative distance information between the vehicle and the at least one cooperative positioning device. The filter includes at least one sub-filter, and the at least one sub-filter corresponds one-to-one with the at least one cooperative positioning device. Global fusion is performed based on the measurement noise covariance and the at least one sub-state estimation result to obtain a global state estimation result, and a vehicle relative navigation and positioning result is obtained based on the global state estimation result. Based on the at least one sub-state estimation result and the global state estimation result, the target mutual information is calculated; Based on the target mutual information, an adaptive information allocation factor is constructed, and the parameters of each sub-filter in the at least one sub-filter are reallocated based on the adaptive information allocation factor to perform subsequent vehicle relative navigation and positioning calculations.

2. The vehicle cooperative navigation and positioning method based on entropy information as described in claim 1, characterized in that, The at least one sub-filter employs the capacitive Kalman filtering algorithm.

3. The vehicle cooperative navigation and positioning method based on entropy information as described in claim 1, characterized in that, The global state estimation result obtained by globally fusing the measurement noise covariance and the at least one sub-state estimation result includes: An interactive multi-model approach is adopted, and global fusion is performed based on the measurement noise covariance and the estimation results of at least one sub-state to obtain a global state estimation result.

4. The vehicle cooperative navigation and positioning method based on entropy information driven according to any one of claims 1 to 3, characterized in that, The target mutual information includes the mutual information between each sub-state estimation result in the at least one sub-state estimation result and the global state estimation result; The calculation of target mutual information based on the at least one sub-state estimation result and the global state estimation result includes: For each sub-state estimation result in the at least one sub-state estimation result, calculate its corresponding entropy function and its conditional entropy function with the global state estimation result, and calculate the target mutual information based on the calculated entropy function and conditional entropy function.

5. The vehicle cooperative navigation and positioning method based on entropy information as described in claim 4, characterized in that, The step of constructing an adaptive information allocation factor based on the target mutual information includes: The mutual information between each sub-state estimation result and the global state estimation result in the at least one sub-state estimation result is normalized to obtain the adaptive information allocation factor corresponding to the at least one sub-filter.

6. The vehicle cooperative navigation and positioning method based on entropy information as described in claim 1, characterized in that, After obtaining the global state estimation result, the vehicle cooperative navigation and localization method based on entropy information further includes: The global state estimation results are used to correct the original output of the inertial navigation system.

7. A vehicle cooperative navigation and positioning device driven by entropy information, characterized in that, include: Model building module, filtering module, fusion module, and information distribution module; The model building module is used to build a cooperative relative navigation and positioning model. The cooperative relative navigation and positioning model obtains state prediction values ​​based on the inertial navigation system on the vehicle, and generates measurement prediction values ​​based on the state prediction values. The filtering module is used to estimate the measurement noise covariance based on the innovation variance matrix between the measurement information and the measurement prediction value through the filter of the cooperative relative navigation and positioning model, and to obtain at least one sub-state estimation result corresponding to at least one cooperative positioning device using at least one sub-filter. The measurement information is the relative distance information between the vehicle and the at least one cooperative positioning device. The filter includes at least one sub-filter, and the at least one sub-filter corresponds one-to-one with the at least one cooperative positioning device. The fusion module is used to perform global fusion based on the measurement noise covariance and the at least one sub-state estimation result to obtain a global state estimation result, and to obtain a vehicle relative navigation and positioning result based on the global state estimation result. The information allocation module is used to calculate the target mutual information based on the at least one sub-state estimation result and the global state estimation result; The information allocation module is further configured to construct an adaptive information allocation factor based on the target mutual information, and to reallocate the parameters of each sub-filter in the at least one sub-filter based on the adaptive information allocation factor for subsequent vehicle relative navigation and positioning calculations.

8. The vehicle cooperative navigation and positioning device based on entropy information as described in claim 7, characterized in that, The at least one sub-filter employs the capacitive Kalman filtering algorithm.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the vehicle cooperative navigation and positioning method based on entropy information as described in any one of claims 1 to 6.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the vehicle cooperative navigation and positioning method based on entropy information as described in any one of claims 1 to 6.

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