Nhc measurement adaptive regulation method based on brake state detection
Patent Information
- Application Number
- CN202611108353.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-24
- Publication Date
- 2026-10-23
AI Technical Summary
[0012]本发明提供一种基于刹车状态检测的组合导航NHC量测自适应调控方法，用以解决现有技术中缺乏急刹车状态感知、NHC量测噪声固定无法自适应调节、更新频率过低导致校正滞后的缺陷
[0022]第三方面，本发明还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述基于刹车状态检测的组合导航NHC量测自适应调控方法。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated navigation technology, and in particular to an adaptive control method for NHC measurement in integrated navigation based on braking state detection. Background Technology
[0002] With the rapid development of intelligent transportation systems and autonomous driving technologies, high-precision vehicle positioning technology has become a key foundation for supporting path planning, lane-level navigation, and vehicle motion control. The combined navigation scheme of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) has become the mainstream technology in the field of vehicle positioning due to its complementary advantages of GNSS's long-term high accuracy and INS's short-term high reliability. GNSS provides absolute position and velocity information, but is affected by the signal propagation environment, often experiencing signal blockage or complete loss of lock in scenarios such as tunnels, under viaducts, urban canyons, and underground parking garages, leading to positioning interruptions or a sharp degradation in accuracy. INS relies on gyroscopes and accelerometers for dead reckoning, possessing complete autonomy and high-frequency output capabilities; however, its errors accumulate over time, and without external correction constraints, positioning errors can diverge to meter-level accuracy within seconds, making it unsuitable for independently supporting high-precision navigation requirements.
[0003] To suppress the rapid accumulation of INS errors in GNSS signal-denied environments, Non-Holonomic Constraints (NHCs) have been widely introduced into vehicle-mounted integrated navigation systems. NHCs fully utilize the rigid body kinematics characteristics of land vehicles during normal driving, where lateral and vertical velocities are zero. They construct pseudo-velocity observations and constrain the velocity errors calculated by INS using a Kalman filter framework, thereby effectively slowing down the divergence in system positioning accuracy and improving navigation continuity and availability in signal-deficient scenarios such as tunnels. Currently, NHC-assisted GNSS / INS integrated navigation schemes have become a widely adopted technique in the industry, and their effectiveness has been verified by extensive theoretical analysis and engineering practice.
[0004] However, actual vehicle driving conditions are complex and variable, especially during transient dynamic events such as sudden braking inside tunnels, where existing NHC-assisted methods reveal significant performance bottlenecks. When a vehicle brakes suddenly, inertia causes significant forward pitch and roll motions. This abrupt attitude change directly alters the projection direction of the IMU accelerometer's sensitive axis, resulting in additional spurious longitudinal and lateral velocity components in the INS mechanical orchestration solution. In tunnel environments where GNSS signals are completely unavailable and absolute observation correction is lacking, if NHC constraints are not applied promptly and effectively, these spurious velocity components will be integrated, leading to significant positional shifts. This manifests as abnormal backtracking and jitter on the navigation trajectory, severely disrupting the continuity and smoothness of the trajectory and affecting the reliability of lane-level positioning.
[0005] In existing vehicle-mounted GNSS / INS integrated navigation systems, a typical implementation scheme for NHC measurement updates is as follows: First, a Kalman filter state vector is constructed with the INS error state as its core. State variables typically include three-dimensional position error, three-dimensional velocity error, three-dimensional misalignment angle error, and zero-bias errors of the gyroscope and accelerometer. When the GNSS signal is valid, the filter performs standard measurement updates using the position and velocity provided by the GNSS as external observations. When the system detects that the GNSS signal has lost lock or its quality has deteriorated below a threshold, it automatically switches to NHC-assisted mode.
[0006] The core principle of NHC measurement updates is as follows: The navigation system velocity obtained from INS mechanical orchestration is projected onto the carrier coordinate system, and the lateral and vertical velocity components are extracted as pseudo-observations, whose theoretical true values are always zero. A linearized measurement equation is established, and the measurement matrix is derived through the projection relationship of velocity error between the navigation and carrier coordinate systems, as well as the coupling relationship between attitude misalignment angle and velocity error. Subsequently, measurement updates are performed within a Kalman filter framework, feeding back the estimated values of lateral and vertical velocity errors to the INS mechanical orchestration results, thereby constraining and suppressing velocity errors.
[0007] In current engineering practice, the noise covariance matrix for NHC measurement updates is typically set as a fixed constant matrix based on the typical vibration level and installation error under normal vehicle driving conditions. The measurement update frequency is also independently set to a fixed value lower than the IMU mechanical orchestration frequency. For example, NHC measurement updates are triggered periodically at a frequency of 5Hz, while the IMU mechanical orchestration runs continuously at a frequency of 25Hz or higher. This fixed-parameter implementation scheme is widely used in current mainstream integrated navigation systems due to its simple calculation and ease of deployment.
[0008] However, the aforementioned existing technical solutions have the following significant drawbacks: First, existing NHC methods lack the ability to perceive and respond to the special and highly impactful vehicle dynamic event of emergency braking. Their measurement noise covariance matrix is fixed as a constant, making it impossible to adaptively adjust the confidence level of the NHC constraints based on the sudden changes in vehicle attitude caused by braking. This results in the filter still fusing NHC measurements with conventional weights when braking generates spurious velocity components, and the constraint strength is insufficient to suppress the amplitude of transient velocity errors.
[0009] Second, the measurement update frequency of the existing NHC method is fixed and much lower than the mechanical orchestration frequency of the IMU, resulting in a measurement correction response period of up to 200ms. In the rapid transient process of sudden braking, which lasts only 200ms to 500ms, the low-frequency update causes the filter correction to lag behind the error accumulation rate. The spurious velocity component integrates without constraint within two NHC update windows, forming a non-negligible position back-shift displacement.
[0010] Third, the two defects mentioned above have a superimposed effect in environments such as tunnels where GNSS signals are completely rejected and there are no absolute observations for reference. Without GNSS correction and adaptive NHC constraints, false speeds cannot be suppressed in time, ultimately leading to significant backtracking and jitter in the navigation trajectory, which seriously affects the continuity and smoothness of lane-level positioning in tunnels.
[0011] In summary, existing technologies generally suffer from the following drawbacks: lack of emergency braking state perception, fixed NHC measurement noise that cannot be adaptively adjusted, and low update frequency leading to calibration lag, which makes it difficult to meet the needs of practical applications. Summary of the Invention
[0012] This invention provides an adaptive control method for NHC measurement in integrated navigation based on braking state detection, which solves the defects of existing technologies such as lack of emergency braking state perception, fixed NHC measurement noise that cannot be adaptively adjusted, and low update frequency leading to correction lag.
[0013] In a first aspect, the present invention provides an adaptive control method for NHC measurement in integrated navigation based on braking state detection, comprising: Establish a state-space model for the combined navigation of the global navigation satellite system and the inertial navigation system, as well as a non-integrity-constrained NHC measurement model, and set a benchmark NHC measurement update strategy, wherein the NHC measurement update operates based on the benchmark frequency and the benchmark measurement noise covariance matrix; The accelerometer output of the inertial measurement unit is used to detect in real time whether the vehicle is in an emergency braking state. When an emergency braking state is detected, the noise covariance matrix of the NHC measurement is adaptively reduced to enhance the constraint strength, and the NHC measurement update frequency is simultaneously increased to a second frequency higher than the reference frequency, so as to achieve dual-dimensional coordinated control of constraint strength and correction timeliness. Establish a braking state exit criterion. After the braking state is determined to be over, perform smooth recovery on the NHC measurement noise covariance matrix and the NHC measurement update frequency to bring them back to the parameter values set by the baseline NHC measurement update strategy.
[0014] According to the present invention, a combined navigation NHC measurement adaptive control method based on braking state detection is provided. The NHC measurement model constructs pseudo-observations in the carrier coordinate system and uses the kinematic constraint that the lateral velocity and vertical velocity are zero when the vehicle is driving normally, with the lateral velocity and vertical velocity components as NHC pseudo-measurement vectors. The NHC measurement matrix is determined by linearization based on the projection relationship of velocity components from the navigation coordinate system to the carrier coordinate system and the cross-coupling relationship between attitude misalignment angle and velocity error.
[0015] According to the present invention, a combined navigation NHC measurement adaptive control method based on braking state detection is provided, wherein the step of detecting whether the vehicle is in an emergency braking state in real time through the accelerometer output of the inertial measurement unit includes: Extract the longitudinal acceleration component from the accelerometer output and perform smoothing filtering to obtain the estimated longitudinal acceleration value; A braking determination function is constructed based on the longitudinal acceleration estimate. When the output value of the braking determination function exceeds the preset braking determination threshold, it is determined that the current moment is in an emergency braking state.
[0016] According to the present invention, a combined navigation NHC measurement adaptive control method based on braking state detection is provided. The braking determination threshold is determined by an adaptive vehicle speed model. The adaptive vehicle speed model dynamically adjusts the determination threshold according to the current vehicle speed. The higher the vehicle speed, the larger the threshold, so as to improve the robustness of braking state determination.
[0017] According to the present invention, an adaptive control method for NHC measurement in integrated navigation based on braking state detection is provided, wherein the adaptive reduction of the NHC measurement noise covariance matrix includes: The noise covariance matrix of the NHC measurement is multiplied by a scaling factor much smaller than 1 based on the reference value, so that the Kalman filter gain approaches the theoretical optimal upper bound, thereby enhancing the confidence of the filter in the NHC zero-velocity constraint.
[0018] According to the present invention, in a combined navigation NHC measurement adaptive control method based on braking state detection, the second frequency is not lower than the fixed execution frequency of the mechanical arrangement of the inertial measurement unit.
[0019] According to the present invention, a method for adaptive control of NHC measurement in integrated navigation based on braking state detection is provided, wherein the establishment of braking state exit criteria includes: When the output value of the braking determination function continuously meets the preset exit threshold for a period of time exceeding the preset holding time, the braking state is determined to end.
[0020] According to the present invention, a combined navigation NHC measurement adaptive control method based on brake state detection is provided. The smooth recovery adopts a first-order smooth recovery strategy, which makes the NHC measurement noise covariance matrix gradually return to the reference value from the adjusted value according to the exponential decay law, and makes the NHC measurement update frequency linearly return to the reference frequency with a fixed slope within a preset recovery time.
[0021] Secondly, the present invention also provides a combined navigation NHC measurement adaptive control system based on braking state detection, comprising: The integrated navigation modeling module is used to establish a state space model of the integrated navigation system and the inertial navigation system, as well as a non-integrity-constrained NHC measurement model, and to set a benchmark NHC measurement update strategy, wherein the NHC measurement update is operated with the benchmark frequency and the benchmark measurement noise covariance matrix. The working condition perception and control module is used to detect in real time whether the vehicle is in an emergency braking state through the accelerometer output of the inertial measurement unit; when an emergency braking state is detected, the module adaptively reduces the noise covariance matrix of the NHC measurement to enhance the constraint strength, and simultaneously increases the NHC measurement update frequency to a second frequency higher than the reference frequency, so as to realize the dual-dimensional coordinated control of constraint strength and correction timeliness. The parameter recovery module is used to establish braking state exit criteria. After the braking state is determined to be over, the NHC measurement noise covariance matrix and the NHC measurement update frequency are smoothly recovered so that they return to the parameter values set by the benchmark NHC measurement update strategy.
[0022] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the adaptive control method for combined navigation NHC measurement based on braking state detection as described above.
[0023] The adaptive control method for NHC measurement of integrated navigation based on braking state detection provided by this invention detects the vehicle's emergency braking state in real time through the accelerometer, adaptively reduces the NHC measurement noise covariance matrix and simultaneously increases the measurement update frequency to be consistent with the IMU mechanical arrangement, effectively suppresses false speed accumulation and trajectory reversal caused by emergency braking in tunnels, and significantly improves the positioning accuracy and continuous smoothness of integrated navigation in GNSS denied environments. Attached Figure Description
[0024] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0025] Figure 1 This is a flowchart illustrating the adaptive control method for NHC measurement in integrated navigation based on braking state detection provided by the present invention. Figure 2 This is a schematic diagram illustrating the shortcomings of traditional navigation systems in tunnel environments during emergency braking, as provided by this invention. Figure 3 This is a core module architecture diagram of the GNSS-INS integrated navigation NHC measurement adaptive control method based on braking state detection provided by the present invention; Figure 4 This is a schematic diagram of the key mechanism of the GNSS-INS integrated navigation NHC measurement adaptive control method based on braking state detection provided by the present invention; Figure 5 This is a schematic diagram of the structure of the NHC measurement adaptive control system for integrated navigation based on braking state detection provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0027] Figure 1 This is a flowchart illustrating the adaptive control method for NHC measurement in integrated navigation based on braking state detection provided in an embodiment of the present invention. Figure 1 As shown, it includes: Step 100: Establish a combined navigation state space model of the global navigation satellite system and the inertial navigation system, as well as a non-holonomic constraint NHC measurement model, and set a benchmark NHC measurement update strategy, wherein the NHC measurement update operates based on the benchmark frequency and the benchmark measurement noise covariance matrix. Step 200: Real-time detection of whether the vehicle is in an emergency braking state is achieved through the accelerometer output of the inertial measurement unit; when an emergency braking state is detected, the noise covariance matrix of the NHC measurement is adaptively reduced to enhance the constraint strength, and the NHC measurement update frequency is simultaneously increased to a second frequency higher than the reference frequency, so as to achieve dual-dimensional coordinated control of constraint strength and correction timeliness. Step 300: Establish a braking state exit criterion. After the braking state is determined to be over, perform smooth recovery on the NHC measurement noise covariance matrix and the NHC measurement update frequency to bring them back to the parameter values set by the baseline NHC measurement update strategy.
[0028] Specifically, with Figure 2 The diagram illustrates the shortcomings of traditional navigation systems during sudden braking of a vehicle in a tunnel environment. The upper part of the tunnel is filled with diagonal lines to indicate that GNSS satellite signals are blocked by the tunnel structure and cannot be received. When a vehicle brakes suddenly in the tunnel, its attitude changes abruptly. The onboard IMU / INS inertial navigation module is affected by the attitude disturbance and calculates false speed information. Existing technologies use a fixed-parameter NHC nonholonomic constraint method to correct the inertial navigation results. The constraint update has a significant lag and cannot suppress the accumulation of errors in time. Ultimately, the vehicle's driving trajectory output by the navigation solution will have problems of turning back and jittering, resulting in distorted positioning results and failing to achieve stable and reliable high-precision onboard positioning in tunnel scenarios.
[0029] Based on the above issues, such as Figure 3 The diagram shows the core module architecture of the adaptive control method for NHC measurements in GNSS-INS integrated navigation based on braking state detection. The system consists of a core integrated navigation module, a condition perception and control module, and a parameter recovery module. The core integrated navigation module is responsible for establishing the state space model of the GNSS / INS / NHC integrated navigation system and setting the baseline NHC measurement update strategy. The condition perception and control module is responsible for detecting the vehicle's emergency braking state in real time through the accelerometer, adaptively scaling down the NHC measurement noise covariance matrix when braking is detected, and simultaneously increasing the measurement update frequency. The parameter recovery module is responsible for establishing braking state exit criteria and smoothly restoring parameters after braking ends. The condition perception and control module and the core integrated navigation module achieve bidirectional interaction between the control state and the baseline parameters through a feedback loop.
[0030] This invention first establishes a state-space model and an NHC measurement model for a GNSS / INS / NHC integrated navigation system, derives a linearized expression for the measurement matrix, and sets a baseline NHC measurement update strategy to provide a theoretical benchmark and filtering framework for adaptive parameter adjustment under braking conditions. Then, it uses accelerometers to detect the vehicle's emergency braking state in real time. Upon detecting braking, it adaptively reduces the NHC measurement noise covariance matrix to enhance constraint strength and simultaneously increases the measurement update frequency to match the IMU mechanical orchestration, achieving coordinated control of both strength and timeliness. Finally, it establishes a braking state exit criterion to avoid over-constraint filtering. After braking ends, it smoothly restores the NHC measurement noise covariance matrix and measurement update frequency, and mathematically proves and analyzes the quantitative suppression effect of this invention on trajectory reversal amplitude.
[0031] Based on the above embodiments, step 100 includes: 1. Construction of State-Space Model and Measurement Model for GNSS / INS / NHC Integrated Navigation System 1.1 State-space model of integrated navigation system The system state vector of the Kalman filter is defined as follows:
[0032] in, This refers to the positional errors in latitude, longitude, and altitude. For velocity errors in the east, north, and sky directions; This refers to the misalignment angle error (platform error angle). and These are the zero-bias errors of the accelerometer and gyroscope, respectively.
[0033] According to the error propagation equation of strapdown inertial navigation, the state differential equation in continuous time is:
[0034] in, The system transition matrix has a velocity error submatrix. With attitude error submatrix They respectively satisfy:
[0035] in, This is the time derivative of the velocity error in the navigation system. This is the attitude rotation matrix from the vehicle coordinate system to the navigation coordinate system; The specific force vector measured by the accelerometer in the carrier coordinate system; This is the misalignment angle error vector; The projection of the Earth's rotational angular rate onto the navigation system; The angular rate of rotation of the navigation system relative to the Earth system; This refers to the speed error under the navigation system. This refers to the zero bias error of the accelerometer.
[0036]
[0037] in, Here is the attitude transition matrix. It is a specific force vector. This refers to the Earth's rotation and the angular rate of rotation of the navigation system.
[0038] 1.2 NHC Measurement Model and Its Linearization Derivation In this invention, the NHC measurement update directly constructs pseudoobservations in the carrier coordinate system (b-frame). Let the carrier velocity calculated by the IMU mechanical orchestration be... Projecting it onto the b series:
[0039] in, The longitudinal (forward) speed, For lateral velocity, This represents the vertical velocity.
[0040] The theoretical constraints of NHC are: and Therefore, a measurement vector is constructed. :
[0041] Among them, is the NHC pseudo-measurement vector, which is composed of lateral and vertical velocity constraints; and These are the estimated lateral and vertical velocities of the carrier calculated by the IMU mechanical orchestration, respectively.
[0042] For measurement equations A rigorous linearization derivation is performed. Based on the chain rule of velocity projection, the true velocity... First variation for: in, For velocity variation (first-order small quantity) in the carrier coordinate system; The rotation matrix from the navigation system to the carrier system; This refers to the speed error under the navigation system. This refers to the misalignment angle error; is the true value of the vehicle velocity in the navigation system; is a second-order or higher-order small quantity, which is ignored during linearization.
[0043] Convert the cross product terms into antisymmetric matrix form. We can obtain:
[0044] in, For the velocity vector The constructed antisymmetric matrix is used for cross product operations. Transform into matrix multiplication To facilitate linearization of the derivation; the definitions of the remaining variables are the same as in the above formula.
[0045] Extracting only the lateral (row 2) and vertical (row 3) components yields the specific block expression of the measurement matrix:
[0046] Wherein, is the NHC measurement matrix, whose dimensions correspond to 2 NHC constraints and a 15-dimensional state vector; and They are respectively and Zero matrix block; subscript This indicates that the second and third rows of the corresponding matrix are extracted, which correspond to the lateral and vertical components of the carrier.
[0047] Measurement noise For zero-mean Gaussian white noise, its covariance matrix is... Set as: in, and The uncertainties of the lateral and vertical constraints are characterized respectively.
[0048] 1.3 Benchmark NHC Measurement Update Strategy When GNSS signals are available, measurements are updated using the position and velocity provided by GNSS. In environments where GNSS signals are denied, such as tunnels, NHC measurement updates are enabled.
[0049] In non-braking mode, NHC measurement updates are performed according to the following baseline strategy: Measurement update frequency: Set to the first frequency That is, measurement update cycle .
[0050] Measurement noise covariance matrix: The reference matrix set in step 1.2 is used. No scaling is applied.
[0051] This benchmark strategy can provide basic lateral and vertical velocity constraints, suppress long-term INS drift, and meet the integrated navigation accuracy requirements under normal vehicle driving conditions.
[0052] Based on the above embodiments, step 200 includes: 2. Real-time detection of braking status and adaptive adjustment of NHC measurement parameters 2.1 Mathematical Model for Real-Time Detection of Braking Status This invention does not rely on external wheel speed sensors; it identifies braking status solely through the raw accelerometer output from the IMU. Let the longitudinal specific force output by the accelerometer be... The longitudinal acceleration estimate is obtained after smoothing filtering. .
[0053] Define the braking determination function :
[0054] Among them, to improve robustness, the braking determination threshold Adaptive vehicle speed model adopted: In the formula, For static baseline thresholds (e.g.) ), For speed sensitivity coefficient (e.g.) ), This is the acceleration change rate threshold, used to eliminate false triggers of slow braking.
[0055] when At that time, the system determines the current moment. In the transient window of emergency braking Inside.
[0056] 2.2 Scaling and Control of Measurement Noise Covariance Matrix When the brake sign When triggered, execute the action. Instantaneous scaling ratio:
[0057] In this invention, it is preferred that... That is, reduced by a factor of 100.
[0058] To theoretically explain the mathematical necessity of a 100-fold scaling down, the Kalman filter gain matrix is analyzed. right The sensitivity. In discrete Kalman filtering, the gain expression is:
[0059] in, for The Kalman filter gain matrix at time step 1 determines the weights for measurement updates; for The prior error covariance matrix at time t; For measurement matrix; This is the measurement noise covariance matrix.
[0060] when At that time, the differential change relationship of the gain can be obtained:
[0061] in, Kalman filter gain versus scaling factor The partial derivatives of represent scaling Sensitivity to filter gain; for The prior error covariance matrix at time t; For measurement matrix; This is the measurement noise covariance matrix.
[0062] because The new filter gain approaches the supremum of the theoretically optimal gain, that is... This means that the filter greatly enhances the confidence in the NHC zero-velocity constraint, and the update amount of the state estimate... They will be forcibly pulled towards a subspace with zero lateral / zero vertical orientation. This strong constraint is achieved through attitude coupling terms. It counteracts the accumulation of false longitudinal and lateral velocities caused by forward tilting / lateral tilting, cutting off the excitation source of trajectory reversal at its root.
[0063] 2.3 Synchronous Adjustment of Measurement Update Frequency Upon detecting the braking state, the frequency of NHC measurement updates is increased from the first frequency to the second frequency, so that the frequency of NHC measurement updates is consistent with the frequency of IMU mechanical orchestration.
[0064] Assume the fixed execution cycle of the IMU mechanical orchestration is (i.e., 25Hz). The standard NHC cycle is... (i.e., 5Hz).
[0065] When braking occurs, the NHC measurement update cycle will be forcibly synchronized to [the specified timeframe]. :
[0066] in, The scaling update cycle for NHC measurements under braking conditions; The fixed execution cycle for the IMU mechanical orchestration corresponds to 25Hz; Update frequency for NHC measurement under braking conditions.
[0067] To theoretically prove the necessity of frequency enhancement, the error state transition matrix of the discrete-time system is analyzed. During braking transients, attitude error It drifts rapidly over time, and its discrete recursive form is:
[0068] in, and They are respectively and The misalignment angle error at any given time; This is the discretization time step; the other variables are defined as before.
[0069] In the time window Within, low-frequency updates (5Hz) are only performed. Secondary correction, while high-frequency update (25Hz) is performed. This second correction increases the correction frequency by five times. According to the sampling theorem in control theory, the higher update frequency significantly shortens the response lag time of the NHC closed-loop correction, enabling the filter's error adjustment capability to match the rate of attitude angle change caused by braking. This avoids overshoot and oscillation caused by correction lag.
[0070] 2.4 Physical significance of dual-parameter coordinated regulation Steps 2.2 and 2.3 above work synergistically from the two dimensions of "enhancing constraint strength" and "improving constraint timeliness": Intensity dimension: By reducing the size by a factor of 100, the Kalman filter imparts nearly 100% confidence to the NHC spurious measurements during information fusion, forcing the lateral and vertical velocities back to near zero, thus spatially suppressing the amplitude of spurious velocity components.
[0071] In terms of timeliness: the frequency is synchronized to 25Hz, so that the NHC constraint is updated once every IMU mechanical orchestration cycle (40ms), which ensures the immediacy of the constraint in terms of time and prevents spurious velocity from integrating and forming significant displacement during long periods of no correction window.
[0072] The two work together to suppress false longitudinal and lateral displacements caused by sudden braking, keeping the vehicle's trajectory smooth and without turning back in the tunnel.
[0073] Based on the above embodiments, step 300 includes: 3. Determination of brake state termination and recovery of NHC measurement parameters 3.1 Establishment of the criterion for exiting braking state To prevent over-constraints from causing filter divergence after braking ends, an exit criterion is set. When the braking decision function... The following conditions are met continuously for more than the holding time. When the braking state ends:
[0074] in, The resting-state small acceleration threshold, The preferred retention time for exit confirmation is... .
[0075] Duration This setting is used to avoid frequent switching of braking states due to acceleration noise, thereby enhancing the robustness of the system.
[0076] 3.2 Smoothing Recovery Strategy for NHC Measurement Parameters Once the braking state is confirmed to have ended, the adjustments made in step 2 need to be made. The matrix and NHC measurement update frequency are restored to the baseline value set in step 1.3.
[0077] 3.2.1 Smoothing recovery of the measurement noise covariance matrix To avoid filter oscillations caused by parameter step jumps, A first-order smooth recovery strategy is adopted:
[0078] in, The moment when the braking state exits the confirmation. To recover the time constant (e.g.) ).
[0079] The physical meaning of this index recovery strategy is: From the scaled matrix Following the exponential decay law, it gradually reverts to the baseline matrix. The filter smoothly transitions the confidence level of NHC measurements from "high confidence" to "normal confidence," avoiding state estimation jumps caused by abrupt changes in confidence levels.
[0080] 3.2.2 Smooth recovery of measurement update frequency NHC measurement update frequency Linear interpolation with a fixed slope is used to revert to the first frequency (5Hz) within the recovery time:
[0081] in, for The update frequency of NHC measurements at any given time; The first reference frequency; The scaling frequency during braking; The confirmation period is for the time required to exit. This is the moment to confirm braking and exiting the vehicle.
[0082] when hour, .
[0083] Through the above-mentioned smooth recovery strategy, the system smoothly returns to the baseline NHC measurement update mode after the braking state ends, ensuring the continuity and stability of the integrated navigation system under all operating conditions.
[0084] 3.3 Analysis of the theoretical benefits To mathematically demonstrate the suppressive effect of this invention on "trajectory reversal," position errors were analyzed. The change of the second derivative before and after NHC enhancement.
[0085] False speed caused by braking under normal, unenhanced NHC conditions satisfy:
[0086] in This represents the tilt / roll angle caused by braking. The spurious velocity, after integration, generates a trajectory reversal displacement.
[0087] After applying this invention, due to Posterior estimation of error covariance of Kalman filter by scaling down by 100 times satisfy:
[0088] in, for The posterior error covariance matrix at time t reflects the estimation uncertainty after the measurement update; It is the identity matrix; This is the Kalman filter gain matrix; For measurement matrix; Let be the prior error covariance matrix.
[0089] At this point, the lateral and vertical velocity errors corresponding to the NHC constraint are forced to zero:
[0090] in, and These are the estimated lateral and vertical velocity errors of the carrier after NHC correction, respectively. Under enhanced NHC constraints, they are forced to approach zero, thereby fundamentally suppressing the generation of spurious velocities.
[0091] Combining attitude-velocity coupling The roll / pitch coupling components in the longitudinal (east / north) spurious velocity increments under the navigation system are significantly reduced. Meanwhile, the 25Hz high-frequency synchronization correction reduces the covariance propagation time. Shorten, the error amplification factor in the state transition matrix Time This significantly reduces the positional backtracking amplitude after double integration by more than an order of magnitude, achieving a smooth trajectory without backtracking.
[0092] Figure 4 This is a schematic diagram of the key mechanism of the adaptive control method for NHC measurement in GNSS-INS integrated navigation based on braking state detection provided by the present invention. In the figure, firstly, the three-axis data of the IMU accelerometer are acquired and the longitudinal acceleration component is extracted; then, it is determined whether the longitudinal acceleration is close to zero; if the condition is met, it is determined that braking has been detected, and adaptive scaling of NHC measurement noise covariance is performed, the measurement update frequency is increased to the IMU rate, and intensity and timeliness are coordinated and controlled in two dimensions; otherwise, it is determined to be normal driving, and the nominal NHC parameters are maintained; finally, the adaptive NHC measurement result is output.
[0093] The adaptive control system for NHC measurement of integrated navigation based on brake state detection provided by the present invention is described below. The adaptive control system for NHC measurement of integrated navigation based on brake state detection described below can be referred to in correspondence with the adaptive control method for NHC measurement of integrated navigation based on brake state detection described above.
[0094] Figure 5 This is a schematic diagram of the structure of the integrated navigation NHC measurement adaptive control system based on braking state detection provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it includes: a combined navigation modeling module 51, a working condition perception and control module 52, and a parameter recovery module 53, wherein: The integrated navigation modeling module 51 is used to establish an integrated navigation state space model of the global navigation satellite system and the inertial navigation system, as well as a non-integrity constraint NHC measurement model, and to set a benchmark NHC measurement update strategy, wherein the NHC measurement update operates at a benchmark frequency and a benchmark measurement noise covariance matrix. The condition perception and control module 52 is used to detect in real time whether the vehicle is in an emergency braking state through the accelerometer output of the inertial measurement unit. When an emergency braking state is detected, the NHC measurement noise covariance matrix is adaptively reduced to enhance the constraint strength, and the NHC measurement update frequency is simultaneously increased to a second frequency higher than the benchmark frequency, realizing dual-dimensional coordinated control of constraint strength and correction timeliness. The parameter recovery module 53 is used to establish a braking state exit criterion. After the braking state is determined to be over, the NHC measurement noise covariance matrix and the NHC measurement update frequency are smoothly recovered to return to the parameter values set by the benchmark NHC measurement update strategy.
[0095] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logic instructions in the memory 630 to execute an adaptive control method for integrated navigation NHC measurements based on braking state detection. This method includes: establishing an integrated navigation state space model of the global navigation satellite system and the inertial navigation system, and a non-integrity constraint NHC measurement model; setting a benchmark NHC measurement update strategy, wherein the NHC measurement update operates at a benchmark frequency and a benchmark measurement noise covariance matrix; real-time detection of whether the vehicle is in an emergency braking state via the accelerometer output of the inertial measurement unit; when an emergency braking state is detected, adaptively reducing the NHC measurement noise covariance matrix to enhance constraint strength, and simultaneously increasing the NHC measurement update frequency to a second frequency higher than the benchmark frequency, achieving dual-dimensional coordinated control of constraint strength and correction timeliness; establishing a braking state exit criterion; and after the braking state is determined to be over, smoothly restoring the NHC measurement noise covariance matrix and the NHC measurement update frequency to return them to the parameter values set by the benchmark NHC measurement update strategy.
[0096] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0097] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the adaptive control method for integrated navigation NHC measurement based on braking state detection provided by the above methods. The method includes: establishing an integrated navigation state space model of a global navigation satellite system and an inertial navigation system, and a non-integrity constraint NHC measurement model; setting a benchmark NHC measurement update strategy, wherein the NHC measurement update operates at a benchmark frequency and a benchmark measurement noise covariance matrix; detecting in real time whether the vehicle is in an emergency braking state through the accelerometer output of the inertial measurement unit; when an emergency braking state is detected, adaptively reducing the NHC measurement noise covariance matrix to enhance the constraint strength, and simultaneously increasing the NHC measurement update frequency to a second frequency higher than the benchmark frequency, thereby achieving dual-dimensional coordinated control of constraint strength and correction timeliness; establishing a braking state exit criterion; and after the braking state is determined to be over, smoothly restoring the NHC measurement noise covariance matrix and the NHC measurement update frequency to return them to the parameter values set by the benchmark NHC measurement update strategy.
[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0099] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for adaptive control of NHC measurement in integrated navigation based on braking state detection, characterized in that, include: Establish a state-space model for the combined navigation of the global navigation satellite system and the inertial navigation system, as well as a non-integrity-constrained NHC measurement model, and set a benchmark NHC measurement update strategy, wherein the NHC measurement update operates based on the benchmark frequency and the benchmark measurement noise covariance matrix; The accelerometer output of the inertial measurement unit is used to detect in real time whether the vehicle is in an emergency braking state; When an emergency braking state is detected, the noise covariance matrix of the NHC measurement is adaptively reduced to enhance the constraint strength, and the NHC measurement update frequency is simultaneously increased to a second frequency higher than the reference frequency, so as to achieve dual-dimensional coordinated control of constraint strength and correction timeliness. Establish a braking state exit criterion. After the braking state is determined to be over, perform smooth recovery on the NHC measurement noise covariance matrix and the NHC measurement update frequency to bring them back to the parameter values set by the baseline NHC measurement update strategy.
2. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The NHC measurement model constructs pseudo-observations in the carrier coordinate system and utilizes the kinematic constraint that the lateral and vertical velocities are zero when the vehicle is driving normally, using the lateral and vertical velocity components as NHC pseudo-measurement vectors. The NHC measurement matrix is determined by linearization based on the projection relationship of velocity components from the navigation coordinate system to the carrier coordinate system and the cross-coupling relationship between attitude misalignment angle and velocity error.
3. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The method of real-time detection of whether the vehicle is in an emergency braking state through the accelerometer output of the inertial measurement unit includes: Extract the longitudinal acceleration component from the accelerometer output and perform smoothing filtering to obtain the estimated longitudinal acceleration value; A braking determination function is constructed based on the longitudinal acceleration estimate. When the output value of the braking determination function exceeds the preset braking determination threshold, it is determined that the current moment is in an emergency braking state.
4. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The braking determination threshold is determined using an adaptive vehicle speed model. The adaptive vehicle speed model dynamically adjusts the determination threshold according to the current vehicle speed. The higher the vehicle speed, the larger the threshold, in order to improve the robustness of braking status determination.
5. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The adaptive reduction of the NHC measurement noise covariance matrix includes: The noise covariance matrix of the NHC measurement is multiplied by a scaling factor much smaller than 1 based on the reference value, so that the Kalman filter gain approaches the theoretical optimal upper bound, thereby enhancing the confidence of the filter in the NHC zero-velocity constraint.
6. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The second frequency is not lower than the fixed execution frequency for mechanical arrangement by the inertial measurement unit.
7. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The criteria for exiting the braking state include: When the output value of the braking determination function continuously meets the preset exit threshold for a period of time exceeding the preset holding time, the braking state is determined to end.
8. The adaptive control method for NHC measurement of integrated navigation based on braking state detection according to claim 1, characterized in that, The smooth recovery adopts a first-order smooth recovery strategy, which makes the NHC measurement noise covariance matrix gradually return to the reference value from the adjusted value according to the exponential decay law, and makes the NHC measurement update frequency linearly return to the reference frequency with a fixed slope within a preset recovery time.
9. A combined navigation NHC measurement adaptive control system based on braking state detection, characterized in that, include: The integrated navigation modeling module is used to establish a state space model of the integrated navigation system and the inertial navigation system, as well as a non-integrity-constrained NHC measurement model, and to set a benchmark NHC measurement update strategy, wherein the NHC measurement update is operated with the benchmark frequency and the benchmark measurement noise covariance matrix. The working condition perception and control module is used to detect in real time whether the vehicle is in an emergency braking state through the accelerometer output of the inertial measurement unit; When an emergency braking state is detected, the noise covariance matrix of the NHC measurement is adaptively reduced to enhance the constraint strength, and the NHC measurement update frequency is simultaneously increased to a second frequency higher than the reference frequency, so as to achieve dual-dimensional coordinated control of constraint strength and correction timeliness. The parameter recovery module is used to establish braking state exit criteria. After the braking state is determined to be over, the NHC measurement noise covariance matrix and the NHC measurement update frequency are smoothly recovered so that they return to the parameter values set by the benchmark NHC measurement update strategy.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the adaptive control method for NHC measurement of integrated navigation based on braking state detection as described in any one of claims 1 to 8.