Integrated circuit automobile navigation module positioning signal anti-interference processing method

Abstract

The application provides an integrated circuit type automobile navigation module positioning signal anti-interference processing method, comprising: collecting a positioning signal of an automobile navigation module and performing preliminary processing on the positioning signal; collecting real-time temperature, electromagnetic interference intensity, multipath reflection coefficient and positioning signal signal-to-noise ratio through a multi-dimensional interference perception module composed of a temperature monitoring unit, an electromagnetic interference detection unit, a multipath feature extraction unit and a signal-to-noise ratio acquisition unit; adjusting the filtering parameters of an adaptive RC filtering circuit according to the interference parameters collected by the multi-dimensional interference perception module through a dynamic filtering regulation module containing the adaptive RC filtering circuit and a filtering parameter calibration unit; and correcting positioning errors through a positioning error correction module composed of a Kalman filtering algorithm unit and an error feedback unit, so that the positioning reliability of the automobile navigation module in a complex vehicle-mounted environment can be improved.

Description

Technical Field

[0001] This invention relates to the field of automotive navigation technology, and in particular to a method for anti-interference processing of positioning signals in integrated circuit automotive navigation modules. Background Technology

[0002] With the development of automotive intelligence, automotive-grade navigation modules face increasingly stringent requirements for positioning accuracy and stability. They need to continuously output reliable positioning data in complex in-vehicle environments to support core functions such as autonomous driving and path planning. Current anti-interference solutions for automotive navigation modules have significant technical limitations: existing solutions mostly optimize for single interference factors, failing to consider the combined interference characteristics of temperature drift, electromagnetic interference, and multipath effects in the in-vehicle environment. Temperature drift causes parameter shifts in core integrated circuit components, lowering the circuit's anti-interference threshold and amplifying the impact of electromagnetic interference on the positioning signal. It also enhances the distortion of multipath reflected signals, creating a superposition effect of interference. More importantly, in existing solutions, interference perception, filtering, and error correction modules operate independently, lacking a unified core control unit for data interaction and collaborative control between modules. There is also no closed-loop mechanism for anti-interference processing. This results in the inability to adapt filtering parameters and error correction in a timely manner when interference conditions change, leading to a significant increase in positioning errors in extreme scenarios, making it difficult to meet the precise positioning requirements of automotive-grade navigation modules.

[0003] Based on the above problems, there is an urgent need for a technical solution that can cope with the combined effects of multiple interference factors and realize module collaborative control and anti-interference closed loop, so as to improve the positioning reliability of car navigation modules in complex vehicle environments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose an anti-interference processing method for positioning signals in an integrated circuit-type automotive navigation module, comprising:

[0005] The positioning signal from the car navigation module is collected and the positioning signal is initially processed.

[0006] The multi-dimensional interference sensing module, consisting of a temperature monitoring unit, an electromagnetic interference detection unit, a multi-path feature extraction unit, and a signal-to-noise ratio acquisition unit, collects real-time temperature, electromagnetic interference intensity, multi-path reflection coefficient, and signal-to-noise ratio of the positioning signal, respectively.

[0007] The dynamic filter control module, which includes an adaptive RC filter circuit and a filter parameter calibration unit, adjusts the filter parameters of the adaptive RC filter circuit according to the interference parameters collected by the multi-dimensional interference sensing module.

[0008] The positioning error is corrected by a positioning error correction module consisting of a Kalman filter algorithm unit and an error feedback unit, based on the filtering parameters output by the dynamic filtering control module.

[0009] The core collaborative control module enables the electrical connection and collaborative control of the multi-dimensional interference sensing module, dynamic filtering and control module, and positioning error correction module.

[0010] The core collaborative control module receives real-time temperature, electromagnetic interference intensity, multipath reflection coefficient, and positioning signal signal-to-noise ratio output by the multi-dimensional interference sensing module, and sends parameter adjustment instructions to the dynamic filtering and control module.

[0011] The system receives the filtering parameters output by the dynamic filtering control module and sends them to the positioning error correction module. It also receives the corrected positioning error output by the positioning error correction module and feeds it back to the navigation module main controller. The feedback enables closed-loop control for anti-interference processing.

[0012] Preferably, the temperature monitoring unit in the multi-dimensional interference sensing module uses a high-precision NTC sensor to collect real-time temperature; the electromagnetic interference detection unit uses an ultra-wideband antenna to detect electromagnetic interference signals in the 10kHz-3GHz frequency band to obtain electromagnetic interference intensity; the multipath feature extraction unit uses a BeiDou / GPS dual-mode receiving module to calculate the multipath reflection coefficient by analyzing the multipath delay difference of BeiDou / GPS dual-mode satellite signals; and the signal-to-noise ratio (SNR) acquisition unit uses a signal analyzer to collect the SNR of the positioning signal. The temperature monitoring unit, electromagnetic interference detection unit, multipath feature extraction unit, and SNR acquisition unit are all electrically connected to the core collaborative control module, transmitting their respective collected parameters to the core collaborative control module.

[0013] More preferably, the adaptive RC filter circuit in the dynamic filter control module is composed of a programmable capacitor and a programmable resistor, used to change the filter characteristics according to the received parameter adjustment command. The filter parameter calibration unit adopts an FPGA chip for executing the parameter calibration algorithm. The filter parameter calibration unit is electrically connected to the core collaborative control module to receive the parameter adjustment command. The filter parameter calibration unit is electrically connected to the adaptive RC filter circuit to output the filter parameter adjustment signal. The filter parameter calibration unit calibrates the parameters of the adaptive RC filter circuit every 50ms to compensate for the parameter offset caused by temperature drift.

[0014] More preferably, the Kalman filter algorithm unit in the positioning error correction module adopts an embedded CPU to execute the positioning error correction algorithm, the error feedback unit adopts a CAN bus interface for communication with the navigation module main controller, the Kalman filter algorithm unit is electrically connected to the core collaborative control module to receive filtering parameters, the Kalman filter algorithm unit is electrically connected to the error feedback unit and outputs the calculated corrected positioning error to the error feedback unit, and the error feedback unit is electrically connected to the navigation module main controller and sends the corrected positioning error to the navigation module main controller.

[0015] More preferably, the formula for calculating the comprehensive interference coefficient by the core collaborative control module is as follows:

[0016] K = E × γ 2 ×e (0.03×|T-25|) ×(1+1.2τ);

[0017] Where K represents the comprehensive interference coefficient, used to quantify the degree of multi-factor synergistic interference; E represents the comprehensive electromagnetic interference intensity, in dBμV / m, calculated by combining the electromagnetic interference signal collected by the electromagnetic interference detection unit with the temperature drift coefficient; γ represents the multipath reflection coefficient, ranging from 0 to 1, calculated by the multipath feature extraction unit through the satellite signal multipath delay difference; T represents the real-time temperature, in °C, collected by the temperature monitoring unit; τ represents the temperature drift coefficient of the integrated circuit core components, ranging from -0.15 to 0.2, calculated by the difference between the parameter value of the component at the real-time temperature T and the reference parameter value at 25 °C, specifically calculated as τ=(R T -R 25 ) / R 25 +(C T -C 25 ) / C 25 , where R T R is the resistance value at real-time temperature T. 25 C is the reference resistance value at 25℃. T C is the capacitance value at real-time temperature T. 25 is the reference value of capacitance at 25℃; e is the natural constant, with a value of 2.71828.

[0018] More preferably, the formula for adjusting the filter parameters by the dynamic filter control module includes a filter cutoff frequency formula.

[0019]

[0020] And the formula for damping coefficient:

[0021] ξ=ξ0×(1+0.8K-0.2S);

[0022] Where f c This indicates the adjusted filter cutoff frequency, in kHz, used to determine the signal passband range of the adaptive RC filter circuit; f c0 The reference filter cutoff frequency at 25℃ is expressed in kHz and is the initial setting parameter of the adaptive RC filter circuit; K represents the comprehensive interference coefficient, calculated by the core collaborative control module; S represents the signal-to-noise ratio of the positioning signal, expressed in dB and acquired by the signal-to-noise ratio acquisition unit; ξ represents the adjusted damping coefficient, used to control the signal attenuation characteristics of the adaptive RC filter circuit; ξ0 represents the reference damping coefficient at 25℃ and is the initial setting parameter of the adaptive RC filter circuit.

[0023] More preferably, the formula used by the positioning error correction module to correct the positioning error includes a positioning error correction formula:

[0024]

[0025] Cutoff frequency weighting formula:

[0026] W f =0.4 + 0.3K;

[0027] And the formula for the weighting of the damping coefficient:

[0028] W ξ = 0.2 + 0.5K - 0.1S;

[0029] Where ΔP represents the corrected positioning error in meters (m), and is the final positioning error value output to the main controller of the navigation module; P0 represents the original positioning error in meters (m), i.e., the uncorrected positioning error value of the navigation module; W f The cutoff frequency weight is used to adjust the degree of influence of the filter cutoff frequency on positioning error correction; f c Indicates the adjusted filter cutoff frequency; f c0 This indicates the reference filter cutoff frequency at 25°C; W ξ ξ represents the damping coefficient weight, used to adjust the degree of influence of the damping coefficient on the positioning error correction; ξ represents the adjusted damping coefficient; γ represents the multipath reflection coefficient; K represents the comprehensive interference coefficient; S represents the signal-to-noise ratio of the positioning signal; e is a natural constant with a value of 2.71828.

[0030] More preferably, the core collaborative control module employs an ARM Cortex-M7 chip for executing collaborative control logic. The core collaborative control module receives and judges the value of the comprehensive interference coefficient K in real time. When the comprehensive interference coefficient K is greater than or equal to 0.6, the core collaborative control module sends a strong anti-interference mode command to the dynamic filtering and control module. The dynamic filtering and control module, according to the strong anti-interference mode command, increases the damping coefficient to 1.2-1.5 times ξ0 and decreases the filter cutoff frequency to f. c0 The weight is increased by 0.6-0.8 times, and simultaneously, the core collaborative control module sends a weight adjustment command to the positioning error correction module. The positioning error correction module increases the cutoff frequency weight W according to the weight adjustment command. f With damping coefficient weight W ξ To enhance anti-interference capabilities.

[0031] More preferably, the programmable capacitor of the adaptive RC filter circuit is adjusted according to the filtered cutoff frequency f. c Configure the parameters; the configuration logic is as follows:

[0032] C prog =1 / (2π×R) fix ×f c );

[0033] Where C prog R is the programmable capacitor value. fix The fixed resistor value in the adaptive RC filter circuit is defined as follows: The damping resistor of the adaptive RC filter circuit is configured according to the adjusted damping coefficient ξ, and the configuration logic is as follows:

[0034]

[0035] Where R damp L is the resistance value of the damping resistor, and L is the inductance value in the adaptive RC filter circuit.

[0036] More preferably, after receiving the filtering parameters, the Kalman filter algorithm unit first calculates the cutoff frequency weight W based on the comprehensive interference coefficient K and the positioning signal-to-noise ratio S. f With damping coefficient weight W ξ Then the W f W ξThe error feedback unit calculates the corrected positioning error by substituting the filtering parameters into the positioning error correction formula. Simultaneously, it sends the corrected positioning error to the navigation module's main controller and sends it back to the core collaborative control module. The core collaborative control module updates the reference value for the next parameter adjustment based on the returned corrected positioning error. When the comprehensive interference coefficient K received three consecutive times is greater than or equal to 0.6, the core collaborative control module adjusts the 25°C reference filtering cutoff frequency f. c0 The value was updated to 0.8 times the original baseline value to optimize the response speed of parameter adjustments in subsequent strong interference scenarios.

[0037] Technical effects:

[0038] The inventive technical point of this invention lies in constructing a multi-dimensional interference sensing module that collects temperature, electromagnetic interference intensity, multipath reflection coefficient, and signal-to-noise ratio data. This module is then combined with a dynamic filtering and control module and a positioning error correction module. A core collaborative control module enables the electrical connection and coordinated control of these three modules, forming an anti-interference processing closed loop. This technical point precisely addresses the main problems in existing solutions, such as failure to handle temperature drift, electromagnetic interference, and multipath effect interference, independent modules lacking a closed loop, and large positioning errors in extreme scenarios. It effectively improves the positioning reliability of automotive navigation modules in complex in-vehicle environments. Attached Figure Description

[0039] Figure 1 This is a flowchart of an anti-interference processing method for positioning signals in an integrated circuit-type car navigation module according to this application. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] Existing anti-interference solutions for automotive navigation modules only optimize for single interference factors and do not achieve coordinated control of interference perception, filtering and control, and error correction modules. Furthermore, they lack a closed-loop anti-interference processing mechanism, resulting in large positioning errors in extreme scenarios and failing to meet the requirements for automotive-grade accurate positioning.

[0042] Based on this, please refer to Figure 1 This embodiment provides a method for anti-interference processing of positioning signals in an integrated circuit-type automotive navigation module, including:

[0043] S1: Collect the positioning signal from the car navigation module and perform preliminary processing on the positioning signal;

[0044] S2: Through a multi-dimensional interference sensing module consisting of a temperature monitoring unit, an electromagnetic interference detection unit, a multi-path feature extraction unit, and a signal-to-noise ratio acquisition unit, real-time temperature, electromagnetic interference intensity, multi-path reflection coefficient, and signal-to-noise ratio of the positioning signal are collected respectively.

[0045] S3: The dynamic filter control module, which includes an adaptive RC filter circuit and a filter parameter calibration unit, adjusts the filter parameters of the adaptive RC filter circuit according to the interference parameters collected by the multi-dimensional interference sensing module.

[0046] S4: The positioning error is corrected according to the filtering parameters output by the dynamic filtering control module by the positioning error correction module, which is composed of a Kalman filter algorithm unit and an error feedback unit.

[0047] S5: The multi-dimensional interference sensing module, dynamic filtering and control module and positioning error correction module are electrically connected and coordinated through the core collaborative control module;

[0048] S6: The core collaborative control module receives the real-time temperature, electromagnetic interference intensity, multipath reflection coefficient and positioning signal signal-to-noise ratio output by the multi-dimensional interference sensing module, and sends parameter adjustment instructions to the dynamic filtering and control module;

[0049] S7: Receive the filtering parameters output by the dynamic filtering control module and send them to the positioning error correction module; receive the corrected positioning error output by the positioning error correction module and feed it back to the navigation module main controller; achieve anti-interference processing closed-loop control through the feedback.

[0050] This technical solution constructs an anti-interference system based on multi-module collaboration and closed-loop control. The multi-dimensional interference perception module is not a single sensor, but consists of four functionally defined units: the temperature monitoring unit is responsible for capturing changes in ambient temperature, providing data for subsequent temperature drift compensation; the electromagnetic interference detection unit collects data on common 10kHz-3GHz frequency band interference in navigation modules, ensuring coverage of frequency bands from interference sources such as vehicle-mounted high-voltage wiring harnesses and millimeter-wave radar; the multipath feature extraction unit analyzes the multipath delay of satellite signals through BeiDou / GPS dual-mode reception, thereby obtaining the reflection coefficient and solving multipath problems in scenarios such as urban canyons; and the signal-to-noise ratio acquisition unit acquires real-time positioning signal quality data, providing a signal basis for filtering and error correction.

[0051] The dynamic filtering control module employs an adaptive RC filter circuit, unlike fixed-parameter filtering. It achieves parameter adjustment through programmable capacitors and resistors, and, in conjunction with a filter parameter calibration unit, ensures that the filtering characteristics adjust according to changes in interference. The positioning error correction module uses the Kalman filter algorithm as its core, and the algorithm input is correlated with the filter parameters to avoid disconnection between error correction and filtering. The core collaborative control module is the interactive hub of the entire system, enabling data flow between the three functional modules through electrical connections: first, it receives four types of parameters from the sensing module, then generates targeted filter adjustment commands and sends them to the dynamic filtering module; next, it receives the filter parameters and transmits them to the error correction module, feeding back the corrected positioning error to the navigation module's main controller, forming a complete closed loop of sensing, filtering, correction, and feedback, ensuring that the parameters of each link can dynamically adapt to the current interference state.

[0052] The technical effects achieved by the above embodiments include: solving the problems of single interference response and module independence, realizing anti-interference closed-loop control, improving the positioning stability of the navigation module in extreme scenarios, and meeting the requirements of automotive-grade accurate positioning.

[0053] The existing interference sensing units lack a specific hardware implementation scheme, the parameter acquisition frequency band is unclear, the reflection coefficient calculation has no basis, and the connection relationship between each unit and the core module is ambiguous, resulting in low accuracy and poor reliability of the acquired data, which cannot support subsequent anti-interference processing.

[0054] Based on this, the temperature monitoring unit in the multi-dimensional interference sensing module uses a high-precision NTC sensor to collect real-time temperature; the electromagnetic interference detection unit uses an ultra-wideband antenna to detect electromagnetic interference signals in the 10kHz-3GHz frequency band to obtain electromagnetic interference intensity; the multipath feature extraction unit uses a BeiDou / GPS dual-mode receiving module to calculate the multipath reflection coefficient by analyzing the multipath delay difference of BeiDou / GPS dual-mode satellite signals; and the signal-to-noise ratio (SNR) acquisition unit uses a signal analyzer to collect the SNR of the positioning signal. The temperature monitoring unit, electromagnetic interference detection unit, multipath feature extraction unit, and SNR acquisition unit are all electrically connected to the core collaborative control module, transmitting their respective collected parameters to the core collaborative control module.

[0055] This solution refines the hardware implementation and data acquisition logic of the multi-dimensional interference perception module, ensuring clear technical support for each unit. The temperature monitoring unit uses a high-precision NTC sensor, which features high sensitivity and fast response, accurately capturing real-time temperatures within the extreme temperature range of -40℃ to 85℃ in vehicles. This provides accurate temperature data for subsequent calculations of the temperature drift coefficient, avoiding deviations in anti-interference parameters due to inaccurate temperature acquisition. The electromagnetic interference detection unit uses an ultra-wideband antenna instead of a conventional narrowband antenna. Its 10kHz-3GHz frequency band coverage precisely matches the frequency range of common interference sources in vehicle scenarios, such as low-frequency interference from high-voltage wiring harnesses and high-frequency interference from millimeter-wave radar, ensuring comprehensive electromagnetic interference intensity acquisition. The multipath feature extraction unit uses a BeiDou / GPS dual-mode receiver module. Unlike single-mode modules, dual-mode reception reduces the delay error of a single satellite system by comparing the multipath delay difference between the two satellite signals, thus more accurately calculating the multipath reflection coefficient and solving the problem of inaccurate reflection coefficient calculation in obstructed scenarios using single-mode modules. The signal-to-noise ratio (SNR) acquisition unit employs a professional signal analyzer, rather than a simple voltage detection circuit, enabling direct quantification of the signal's SNR and avoiding deviations in subsequent filter parameter adjustments caused by SNR estimation. Furthermore, all four units are electrically connected to the core collaborative control module, ensuring that the acquired parameters—temperature, electromagnetic interference intensity, multipath reflection coefficient, and SNR—are transmitted to the core module in real-time and stably, providing reliable data input for comprehensive interference assessment.

[0056] The technical effects achieved by the above embodiments include: clarifying the hardware and acquisition logic of the sensing unit, improving the accuracy and reliability of parameter acquisition, and ensuring that the data can effectively support subsequent comprehensive interference assessment and anti-interference processing.

[0057] The existing filter circuit has fixed parameters and no dynamic calibration mechanism. Temperature drift causes the filter parameters to deviate, making it unable to adapt to changes in interference. Furthermore, there is no clear hardware execution unit for adjusting the filter parameters, and the filter performance drops sharply under extreme temperatures, affecting the quality of the positioning signal.

[0058] Based on this, the adaptive RC filter circuit in the dynamic filter control module is composed of programmable capacitors and programmable resistors, used to change the filtering characteristics according to the received parameter adjustment instructions. The filter parameter calibration unit uses an FPGA chip to execute the parameter calibration algorithm. The filter parameter calibration unit is electrically connected to the core collaborative control module to receive parameter adjustment instructions. The filter parameter calibration unit is electrically connected to the adaptive RC filter circuit to output the filter parameter adjustment signal. The filter parameter calibration unit calibrates the parameters of the adaptive RC filter circuit every 50ms to compensate for the parameter offset caused by temperature drift.

[0059] This solution focuses on the hardware configuration and calibration mechanism of the dynamic filter control module to ensure stable filtering performance and adaptability to varying interference. The core of the adaptive RC filter circuit uses programmable capacitors and programmable resistors, rather than fixed-parameter RC components. The programmable capacitors can be controlled by digital signals to change their capacitance, and the programmable resistors similarly. Together, they enable dynamic adjustment of key parameters such as the filter cutoff frequency and damping coefficient, thereby altering the filtering characteristics to meet the filtering requirements under different interference scenarios. The filter parameter calibration unit uses an FPGA chip. FPGAs are characterized by high parallel processing speed and strong real-time performance, enabling efficient execution of parameter calibration algorithms and avoiding calibration delays caused by using an MCU. The FPGA is electrically connected to the core collaborative control module, allowing it to receive parameter adjustment commands from the core module in real time and then output specific parameter adjustment signals to the adaptive RC filter circuit via electrical connection, achieving a rapid response from command reception and signal output to parameter adjustment. Crucially, a calibration period of 50ms is set, which is much smaller than the time constant of the vehicle temperature change. This allows for timely detection of the offset of capacitance and resistance parameters caused by temperature drift. The offset is calculated by the calibration algorithm and the parameters of the programmable components are adjusted to ensure that the filter parameters are always maintained at the target value. This counteracts the impact of temperature drift on the filter performance and avoids incomplete interference filtering or loss of effective signal due to parameter offset.

[0060] The technical effects achieved by the above embodiments include: realizing dynamic adjustment and periodic calibration of filter parameters, offsetting the effects of temperature drift, maintaining stable filter performance, effectively filtering out interference, and improving the quality of positioning signals.

[0061] The existing positioning error correction lacks specific algorithm execution hardware, and there is no clear interface for error feedback. The corrected data cannot be reliably transmitted to the main controller of the navigation module, resulting in the inability to apply the correction results and making it difficult to improve positioning accuracy.

[0062] Based on this, the Kalman filter algorithm unit in the positioning error correction module adopts an embedded CPU to execute the positioning error correction algorithm. The error feedback unit adopts a CAN bus interface for communication with the navigation module main controller. The Kalman filter algorithm unit is electrically connected to the core collaborative control module to receive filtering parameters. The Kalman filter algorithm unit is electrically connected to the error feedback unit and outputs the calculated corrected positioning error to the error feedback unit. The error feedback unit is electrically connected to the navigation module main controller and sends the corrected positioning error to the navigation module main controller.

[0063] This solution clearly defines the hardware configuration and data transmission path of the positioning error correction module, ensuring effective error correction and applicability of the results. The Kalman filter algorithm unit utilizes an embedded CPU. Embedded CPUs offer moderate computing power and low power consumption, meeting the resource requirements of in-vehicle navigation modules. They can efficiently execute the Kalman filter algorithm, which requires adjusting the weights of the state matrix based on filtering parameters. The embedded CPU can use a pre-programmed algorithm to substitute the filtering parameters transmitted by the core collaborative control module into the algorithm model, iteratively correcting the original positioning error. This avoids the resource waste caused by using an FPGA or the insufficient computing power of the MCU. The error feedback unit uses a CAN bus interface instead of a standard UART interface. The CAN bus is a standard communication interface in the automotive field, characterized by strong anti-interference capabilities and stable transmission rates. This ensures reliable transmission of the corrected positioning error in the complex electromagnetic environment of an in-vehicle system, avoiding data loss or errors caused by interference with the UART interface. In terms of data flow, the Kalman filter algorithm unit first receives the filtering parameters from the core module via electrical connection, completes the error correction calculation, and then outputs the corrected positioning error to the error feedback unit via electrical connection. Finally, the error feedback unit communicates with the navigation module main controller via electrical connection and sends the corrected data to the main controller, so that the main controller can directly use the corrected positioning data output instead of the original error data, ensuring that the error correction result is effectively applied to the positioning output and improving the final positioning accuracy.

[0064] The technical effects achieved by the above embodiments include: clearly defining the error correction hardware and feedback interface, ensuring efficient correction calculation and reliable data transmission, and enabling the correction results to be applied to the main controller, thereby improving the positioning accuracy of the navigation module.

[0065] Existing mathematical models for multi-factor collaborative interference lack quantification, making it impossible to accurately assess the overall interference intensity. The parameters lack clear meaning, dimensions, and calculation sources, resulting in a lack of basis for subsequent adjustments to anti-interference parameters and a blind approach to interference response.

[0066] Based on this, the formula for calculating the comprehensive interference coefficient by the core collaborative control module is as follows:

[0067] K = E × γ 2 ×e (0.03×|T-25|) ×(1+1.2τ);

[0068] Where K represents the comprehensive interference coefficient, used to quantify the degree of multi-factor synergistic interference; E represents the comprehensive electromagnetic interference intensity, in dBμV / m, calculated by combining the electromagnetic interference signal collected by the electromagnetic interference detection unit with the temperature drift coefficient; γ represents the multipath reflection coefficient, ranging from 0 to 1, calculated by the multipath feature extraction unit through the satellite signal multipath delay difference; T represents the real-time temperature, in °C, collected by the temperature monitoring unit; τ represents the temperature drift coefficient of the integrated circuit core components, ranging from -0.15 to 0.2, calculated by the difference between the parameter value of the component at the real-time temperature T and the reference parameter value at 25 °C, specifically calculated as τ=(R T -R 25 ) / R 25 +(C T -C 25 ) / C 25 , where R T R is the resistance value at real-time temperature T. 25 C is the reference resistance value at 25℃. T C is the capacitance value at real-time temperature T. 25 is the reference value of capacitance at 25℃; e is the natural constant, with a value of 2.71828.

[0069] This scheme achieves quantitative assessment of multi-factor synergistic interference by constructing a comprehensive interference coefficient formula, with each parameter having a clear meaning, dimension, and source. The core of the formula is the calculation of the comprehensive interference coefficient K. K is dimensionless and only used to quantify the degree of synergistic interference from temperature, electromagnetic interference, and multipath interference; a larger K value indicates more severe interference. Here, E represents the comprehensive electromagnetic interference intensity, measured in dBμV / m (decibels per microvolt per meter). This unit is the standard dimension of electromagnetic interference intensity, directly reflecting the magnitude of the interference signal's field strength. The value of E is obtained by combining the original interference signal (e.g., 80 dBμV / m) collected by the electromagnetic interference detection unit with a temperature drift coefficient τ for correction, avoiding the influence of temperature-induced parameter shifts in the detection unit itself on the accuracy of E. γ is the multipath reflection coefficient, ranging from 0 to 1, dimensionless. γ = 0 indicates no multipath interference, and γ = 1 indicates that the multipath interference is comparable to the direct signal strength. Its value is calculated by the multipath feature extraction unit using the multipath delay difference of BeiDou / GPS dual-mode signals, ensuring reasonable quantification of multipath interference. T represents the real-time temperature in °C, directly collected by the temperature monitoring unit. The formula calculates the degree of temperature deviation from the 25 °C baseline using |T-25|, multiplies it by a coefficient of 0.03, and constructs an exponential term with the natural constant e as the base. This simulates the exponentially increasing trend of the interference synergistic effect as temperature rises. For example, when T = 85 °C, |T-25| = 60, and the exponential term ≈ e. 1.8The value is approximately 6.05, reflecting a significant increase in interference at high temperatures. τ is the temperature drift coefficient, ranging from -0.15 to 0.2, without dimensions. It is calculated by summing the relative deviations of the resistance and capacitance at the real-time temperature from the 25°C reference value, quantifying the degree of component parameter drift with temperature, and thus correcting the electromagnetic interference intensity E, ensuring that the calculation of K reflects the indirect impact of temperature drift on interference. The entire formula synergistically correlates three independent interference factors through multiplication, achieving accurate quantification of multi-factor interference and providing a clear basis for subsequent filter parameter adjustments.

[0070] The technical effects achieved by the above embodiments include: constructing a multi-factor collaborative interference quantification formula, clarifying the meaning and source of parameters, realizing accurate interference assessment, providing a reliable basis for subsequent anti-interference parameter adjustment, and avoiding blind interference response.

[0071] Existing filtering parameter adjustments lack mathematical models that correlate interference and signal-to-noise ratio. Cutoff frequency and damping coefficient have no clear dimensions or functions. Parameter adjustments are disconnected from actual interference scenarios, resulting in poor filtering performance and an inability to effectively filter out interference or lose effective signals.

[0072] Based on this, the formula for adjusting the filter parameters by the dynamic filter control module includes the filter cutoff frequency formula:

[0073]

[0074] And the formula for damping coefficient:

[0075] ξ=ξ0×(1+0.8K-0.2S);

[0076] Where f c This indicates the adjusted filter cutoff frequency, in kHz, used to determine the signal passband range of the adaptive RC filter circuit; f c0 The reference filter cutoff frequency at 25℃ is expressed in kHz and is the initial setting parameter of the adaptive RC filter circuit; K represents the comprehensive interference coefficient, calculated by the core collaborative control module; S represents the signal-to-noise ratio of the positioning signal, expressed in dB and acquired by the signal-to-noise ratio acquisition unit; ξ represents the adjusted damping coefficient, used to control the signal attenuation characteristics of the adaptive RC filter circuit; ξ0 represents the reference damping coefficient at 25℃ and is the initial setting parameter of the adaptive RC filter circuit.

[0077] This scheme achieves dynamic adjustment of filter parameters through two correlation formulas. These parameters are strongly correlated with interference and signal-to-noise ratio, and their dimensions and effects are clearly defined. The first is the formula for the filter cutoff frequency, f. c The adjusted cutoff frequency, measured in kHz (kilohertz), is the standard unit of frequency and directly determines the signal passband range of the adaptive RC filter circuit—f. cThe following signals can pass through, while the signals above are filtered out, ensuring that the filtering targets the interference frequency. c0 The 25℃ reference cutoff frequency is the initial circuit setting, ensuring a consistent adjustment reference. The numerator in the formula... This reflects the influence of the overall interference coefficient K: as K increases, the numerator decreases, and f... c Reduce and filter out higher frequency interference; denominator The effect of signal-to-noise ratio (S) is reflected: as S increases, the denominator increases, and f... c The reduction amplitude is decreased to avoid excessive filtering and loss of effective signal.

[0078] Secondly, there's the damping coefficient formula. ξ is the adjusted damping coefficient, dimensionless, used to control the signal attenuation characteristics of the RC filter circuit. A larger ξ attenuates signals exceeding the cutoff frequency more quickly, resulting in more thorough interference filtering, but the transition process may be slower. A smaller ξ attenuates more slowly, but the transition is smoother. ξ0 is the 25℃ reference damping coefficient, ensuring a consistent adjustment reference. In the formula, 1+0.8K reflects the influence of K: as K increases, ξ increases, enhancing interference attenuation. For example, when K=0.6, 0.8K=0.48, ξ=ξ0×1.48, accelerating attenuation. -0.2S reflects the influence of S: as S increases, ξ decreases, preventing excessive attenuation that could distort the effective signal. These two formulas work together to dynamically adapt the filter parameters to interference and signal quality, ensuring optimal filtering performance.

[0079] The technical effects achieved by the above embodiments include: the filtering parameters are dynamically adjusted according to interference and signal-to-noise ratio, balancing interference filtering and signal retention, improving the filtering effect, ensuring that the effective signal is lossless, and the interference is accurately filtered out.

[0080] Existing mathematical models for positioning error correction without associated filtering parameters lack logical calculation of weights and have unclear parameter dimensions. This disconnects correction from filtering, resulting in low correction accuracy and an inability to effectively reduce positioning errors.

[0081] Based on this, the formula for the positioning error correction module to correct the positioning error includes the positioning error correction formula:

[0082]

[0083] Cutoff frequency weighting formula:

[0084] W f =0.4 + 0.3K;

[0085] And the formula for the weighting of the damping coefficient:

[0086] W ξ = 0.2 + 0.5K - 0.1S;

[0087] Where ΔP represents the corrected positioning error in meters (m), and is the final positioning error value output to the main controller of the navigation module; P0 represents the original positioning error in meters (m), i.e., the uncorrected positioning error value of the navigation module; W f The cutoff frequency weight is used to adjust the degree of influence of the filter cutoff frequency on positioning error correction; f c Indicates the adjusted filter cutoff frequency; f c0 This indicates the reference filter cutoff frequency at 25°C; W ξ ξ represents the damping coefficient weight, used to adjust the degree of influence of the damping coefficient on the positioning error correction; ξ represents the adjusted damping coefficient; γ represents the multipath reflection coefficient; K represents the comprehensive interference coefficient; S represents the signal-to-noise ratio of the positioning signal; e is a natural constant with a value of 2.71828.

[0088] This scheme achieves precise correction of positioning errors through three correlation formulas. The correction process is deeply integrated with filtering parameters and interference factors, and the meaning and dimensions of the parameters are clearly defined. The core is the positioning error correction formula, where ΔP is the corrected positioning error in meters (m), which is the final output actual positioning error and directly determines the navigation accuracy; P0 is the original positioning error (e.g., 10m), which is the basic data for correction.

[0089] The two correction terms in the formula are related to the filter cutoff frequency and the damping coefficient, respectively, to ensure that the correction and the filtering effect are linked. The first correction term is W. f ×(f c / f c0 ) 2 Wf is the cutoff frequency weight (unitless), determined by W f =0.4 + 0.3K, W is calculated as K increases. f As K increases, for example, when K = 0.6, W f =0.4 + 0.18 = 0.58, the effect of increasing the cutoff frequency on the correction; (f c / f c0 ) 2 This is the square of the cutoff frequency relative to the reference value. As fc decreases, this term decreases, reducing the correction magnitude and preventing over-correction. The second correction term, W... ξ ×ξ×e (-0.5γ) :W ξ It is the damping coefficient weight, given by W ξ =0.2+0.5K-0.1S calculation, W as K increases ξ As S increases, W ξ Reduce the impact of interference and signal quality on the weights; as ξ increases, this term increases, and the correction magnitude increases; e (-0.5γ) To reflect the impact of multipath propagation, this term decreases as γ increases, resulting in a smaller correction magnitude and avoiding correction bias caused by multipath propagation. The two weighting formulas ensure that W... fand W ξ The correction term is dynamically adjusted based on K and S to adapt to the current interference and signal conditions. The entire correction logic is as follows: first, calculate W based on K and S. f W ξ Then, by substituting the filter parameters and multipath coefficient γ, the original error P0 is corrected to obtain the accurate ΔP, thus achieving deep synergy between correction, filtering, and interference, and avoiding blind correction.

[0090] The technical effects achieved by the above embodiments include: error correction is bound to filtering parameters and interference factors, the correction is accurate, the original positioning error is effectively reduced, the final positioning accuracy of the navigation module is improved, and the automotive-grade requirements are met.

[0091] The existing system lacks a strong anti-interference mode triggering mechanism. When interference is severe, the adjustment of filter parameters and weights has no clear range. The core collaborative control module lacks specific hardware, resulting in insufficient anti-interference capability and poor positioning stability under extreme interference.

[0092] Based on this, the core collaborative control module uses an ARM Cortex-M7 chip to execute collaborative control logic. The core collaborative control module receives and judges the value of the comprehensive interference coefficient K in real time. When the comprehensive interference coefficient K is greater than or equal to 0.6, the core collaborative control module sends a strong anti-interference mode command to the dynamic filtering and control module. The dynamic filtering and control module, according to the strong anti-interference mode command, increases the damping coefficient to 1.2-1.5 times ξ0 and decreases the filter cutoff frequency to f. c0 The weight is increased by 0.6-0.8 times, and simultaneously, the core collaborative control module sends a weight adjustment command to the positioning error correction module. The positioning error correction module increases the cutoff frequency weight W according to the weight adjustment command. f With damping coefficient weight W ξ To enhance anti-interference capabilities.

[0093] This solution clearly defines the triggering mechanism, parameter adjustment range, and core module hardware of the strong anti-interference mode to ensure rapid response under extreme interference. The core collaborative control module uses an ARM Cortex-M7 chip, which features high performance and low power consumption, efficiently executing collaborative control logic and adapting to the computing power and power consumption requirements of the vehicle navigation module. This avoids excessive power consumption due to high-performance CPUs or response delays caused by low-performance MCUs. The triggering condition for the strong anti-interference mode is set to K≥0.6. This threshold was derived from numerous vehicle interference experiments—when K≥0.6, the interference has reached an extreme level, and conventional adjustments cannot meet the anti-interference requirements, necessitating the activation of the strong mode. After the mode is activated, the core module sends instructions to the dynamic filter control module, specifying that the damping coefficient be adjusted to 1.2-1.5 times ξ0. Increasing the damping coefficient accelerates the attenuation of interference signals and enhances the filtering effect; the filter cutoff frequency is adjusted to f... c0The cutoff frequency is increased by 0.6-0.8 times, which expands the interference filtering range and ensures that high-frequency interference is completely filtered out. Simultaneously, the core module sends a weight adjustment command to the positioning error correction module, increasing W... f With W ξ This enhances the impact of filtering parameters on error correction, making the correction more adaptable to signal conditions under strong interference. The entire strong-mode logic formation process, from triggering and parameter adjustment to weight optimization, ensures that each module works together to enhance anti-interference capabilities under extreme interference, preventing localization failure.

[0094] The technical effects achieved by the above embodiments include: clearly defining the triggering and parameter adjustment of the strong anti-interference mode, rapidly enhancing anti-interference capability under extreme interference, maintaining the positioning stability of the navigation module, and avoiding a sharp drop in positioning accuracy.

[0095] The existing adaptive RC filter circuits lack clear mathematical logic for configuring programmable capacitors and damping resistors, and the component parameters are not related to the filter parameters, resulting in inaccurate component parameter configuration and failure to meet the target filtering performance requirements.

[0096] Based on this, the programmable capacitor of the adaptive RC filter circuit is adjusted according to the filter cutoff frequency f. c Configure the parameters; the configuration logic is as follows:

[0097] C prog =1 / (2π×R) fix ×f c );

[0098] Where C prog R is the programmable capacitor value. fix The fixed resistor value in the adaptive RC filter circuit is defined as follows: the damping resistor of the adaptive RC filter circuit is configured according to the adjusted damping coefficient ξ, and the configuration logic is as follows: Where R damp L is the resistance value of the damping resistor, and L is the inductance value in the adaptive RC filter circuit.

[0099] This solution utilizes two configuration formulas to achieve precise configuration of the programmable capacitor and damping resistor in the adaptive RC filter circuit, ensuring a perfect match between component parameters and target filter parameters. The first is the programmable capacitor configuration formula, C... prog The capacitance value is a programmable capacitor, measured in farads (F). In practical applications, it is expressed in microfarads (μF) or picofarads (pF). It is a key component parameter that determines the filter cutoff frequency.

[0100] Formula C prog =1 / (2π×R) fix ×f c The cutoff frequency formula f originates from the RC low-pass filter circuit. c=1 / (2πRC), the capacitor configuration value is obtained through mathematical transformation to ensure C prog Able to accurately achieve target f c Among them, R fix This refers to the fixed resistance value (in ohms, Ω) in the circuit, a preset value (e.g., 1kΩ) to avoid affecting the accuracy of capacitor configuration due to resistance variations; f c This is the target adjusted cutoff frequency (in kHz, which needs to be converted to Hz for calculation, e.g., 8kHz = 8000Hz), ensuring the calculated C... prog Accurate, such as R fix =1kΩ, f c =8kHz, then C prog =1 / (2×3.14×1000×8000)≈20nF.

[0101] Secondly, there is the formula for configuring the damping resistor, R. damp The resistance value of the damping resistor, measured in ohms (Ω), determines the damping coefficient ξ of the circuit. Formula Derived from the damping coefficient formula of an RLC circuit, the resistor configuration value is obtained after transformation to ensure R damp It can accurately achieve the target ξ.

[0102] Where L is the inductance value of the circuit, the unit is Henry H, the actual value is microhenry μH, and it is a preset value; C prog ξ is the pre-configured programmable capacitor value (in F); ξ is the target adjusted damping coefficient (unitless), and R is calculated by substituting these values. damp Accurate, for example: ξ = 0.848, L = 10μH = 10^-5H, C prog =20nF=2×10 - 8F, then

[0103] R damp = 2 × 0.848 × 1.414 × 10 - 6≈2.4Ω.

[0104] The two formulas ensure that the programmable component parameters correspond one-to-one with the target filter parameters, avoiding substandard filtering performance caused by configuration deviations.

[0105] The technical effects achieved by the above embodiments include: the programmable component parameters are precisely configured according to the filtering parameters, ensuring that the adaptive RC filter circuit achieves the target filtering performance, enhancing the interference filtering effect, and supporting subsequent error correction.

[0106] The existing error correction lacks a clear calculation order, there is no mechanism for feedback of corrected data, and the core module lacks baseline value update logic, resulting in low correction efficiency and slow response to subsequent adjustments in strong interference scenarios.

[0107] Based on this, after receiving the filtering parameters, the Kalman filter algorithm unit first calculates the cutoff frequency weight W according to the comprehensive interference coefficient K and the positioning signal signal-to-noise ratio S. f With damping coefficient weight W ξ Then the W f W ξ The error feedback unit calculates the corrected positioning error by substituting the filtering parameters into the positioning error correction formula. Simultaneously, it sends the corrected positioning error to the navigation module's main controller and sends it back to the core collaborative control module. The core collaborative control module updates the reference value for the next parameter adjustment based on the returned corrected positioning error. When the comprehensive interference coefficient K received three consecutive times is greater than or equal to 0.6, the core collaborative control module adjusts the 25°C reference filtering cutoff frequency f. c0 The value was updated to 0.8 times the original baseline value to optimize the response speed of parameter adjustments in subsequent strong interference scenarios.

[0108] This scheme clarifies the calculation order of error correction, the data feedback mechanism, and the baseline value update logic, thereby improving correction efficiency and response speed to strong interference. Firstly, the calculation order of the Kalman filter algorithm unit is as follows: it first receives the filtering parameters transmitted from the core module, then calls the weight formula, and combines it with K and S synchronously transmitted from the core module to calculate W. f and W ξ For example, when K = 0.6 and S = 8dB, Wf = 0.4 + 0.18 = 0.58, Wξ = 0.2 + 0.3 - 0.8 = -0.3. Here, we take a positive value of 0.1. Finally, WW... f W ξ f c Substituting ξ and γ into the positioning error correction formula, ΔP is calculated. This order ensures that the weight calculation is based on the latest K and S, and the correction calculation is based on the latest weights and filter parameters, avoiding correction deviations caused by reversing the order. Next is the data feedback from the error feedback unit: while sending ΔP to the navigation module's main controller, ΔP is also electrically transmitted back to the core collaborative control module. The core module can judge the current anti-interference processing effect based on the transmitted ΔP, and then update the reference value for the next parameter adjustment, forming an optimized loop of correction, feedback, and reference update, improving the accuracy of subsequent adjustments. Particularly crucial is the reference filter cutoff frequency f. c0 The update logic is as follows: When the core module detects K≥0.6 three times consecutively, the original 25℃ reference temperature f is updated. c0 The filter parameter adjustment has been increased by 0.8 times. This update makes the starting reference for the adjustment of filter parameters closer to the target value in subsequent strong interference scenarios, reducing the adjustment range and time, optimizing the response speed, and avoiding the increase in positioning error caused by the lag in parameter adjustment under continuous strong interference.

[0109] The technical effects achieved by the above embodiments include: clarifying the correction calculation order and data feedback, updating the benchmark value as needed, improving correction efficiency, responding faster to parameter adjustment under strong interference, and maintaining the positioning stability and accuracy of the navigation module.

[0110] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for anti-interference processing of positioning signals in an integrated circuit-type automotive navigation module, characterized in that, include: The positioning signal from the car navigation module is collected and the positioning signal is initially processed. The multi-dimensional interference sensing module, consisting of a temperature monitoring unit, an electromagnetic interference detection unit, a multi-path feature extraction unit, and a signal-to-noise ratio acquisition unit, collects real-time temperature, electromagnetic interference intensity, multi-path reflection coefficient, and signal-to-noise ratio of the positioning signal, respectively. The dynamic filter control module, which includes an adaptive RC filter circuit and a filter parameter calibration unit, adjusts the filter parameters of the adaptive RC filter circuit according to the interference parameters collected by the multi-dimensional interference sensing module. The positioning error is corrected by a positioning error correction module consisting of a Kalman filter algorithm unit and an error feedback unit, based on the filtering parameters output by the dynamic filtering control module. The core collaborative control module enables the electrical connection and collaborative control of the multi-dimensional interference sensing module, dynamic filtering and control module, and positioning error correction module. The core collaborative control module receives real-time temperature, electromagnetic interference intensity, multipath reflection coefficient, and positioning signal signal-to-noise ratio output by the multi-dimensional interference sensing module, and sends parameter adjustment instructions to the dynamic filtering and control module. The system receives the filtering parameters output by the dynamic filtering control module and sends them to the positioning error correction module. It also receives the corrected positioning error output by the positioning error correction module and feeds it back to the navigation module main controller. The feedback enables closed-loop control for anti-interference processing.

2. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 1, characterized in that, The temperature monitoring unit in the multi-dimensional interference sensing module uses a high-precision NTC sensor to collect real-time temperature. The electromagnetic interference detection unit uses an ultra-wideband antenna to detect electromagnetic interference signals in the 10kHz-3GHz frequency band to obtain the electromagnetic interference intensity. The multipath feature extraction unit uses a BeiDou / GPS dual-mode receiving module to calculate the multipath reflection coefficient by analyzing the multipath delay difference of BeiDou / GPS dual-mode satellite signals. The signal-to-noise ratio (SNR) acquisition unit uses a signal analyzer to collect the SNR of the positioning signal. The temperature monitoring unit, electromagnetic interference detection unit, multipath feature extraction unit, and SNR acquisition unit are all electrically connected to the core collaborative control module and transmit the parameters they collect to the core collaborative control module.

3. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 1, characterized in that, The adaptive RC filter circuit in the dynamic filter control module is composed of programmable capacitors and programmable resistors, used to change the filtering characteristics according to the received parameter adjustment instructions. The filter parameter calibration unit uses an FPGA chip to execute the parameter calibration algorithm. The filter parameter calibration unit is electrically connected to the core collaborative control module to receive parameter adjustment instructions. The filter parameter calibration unit is electrically connected to the adaptive RC filter circuit to output the filter parameter adjustment signal. The filter parameter calibration unit calibrates the parameters of the adaptive RC filter circuit every 50ms to compensate for parameter offset caused by temperature drift.

4. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 1, characterized in that, The Kalman filter algorithm unit in the positioning error correction module uses an embedded CPU to execute the positioning error correction algorithm. The error feedback unit uses a CAN bus interface for communication with the navigation module main controller. The Kalman filter algorithm unit is electrically connected to the core collaborative control module to receive filtering parameters. The Kalman filter algorithm unit is electrically connected to the error feedback unit and outputs the calculated corrected positioning error to the error feedback unit. The error feedback unit is electrically connected to the navigation module main controller and sends the corrected positioning error to the navigation module main controller.

5. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 1, characterized in that, The formula for calculating the comprehensive interference coefficient by the core collaborative control module is as follows: K=E×γ 2 ×e (0.03×|T-25|) ×(1+1.2τ); Where K represents the comprehensive interference coefficient, used to quantify the degree of multi-factor synergistic interference; E represents the comprehensive electromagnetic interference intensity, in dBμV / m, calculated by combining the electromagnetic interference signal collected by the electromagnetic interference detection unit with the temperature drift coefficient; γ represents the multipath reflection coefficient, ranging from 0 to 1, calculated by the multipath feature extraction unit through the satellite signal multipath delay difference; T represents the real-time temperature, in °C, collected by the temperature monitoring unit; τ represents the temperature drift coefficient of the integrated circuit core components, ranging from -0.15 to 0.2, calculated by the difference between the parameter value of the component at the real-time temperature T and the reference parameter value at 25 °C, specifically calculated as τ=(R T -R 25 ) / R 25 +(C T -C 25 ) / C 25 , where R T R is the resistance value at real-time temperature T. 25 C is the reference resistance value at 25℃. T C is the capacitance value at real-time temperature T. 25 is the reference value of capacitance at 25℃; e is the natural constant, with a value of 2.71828.

6. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 5, characterized in that, The formula used by the dynamic filter control module to adjust the filter parameters includes the filter cutoff frequency formula. And the formula for damping coefficient: ξ=ξ0×(1+0.8K-0.2S); Where f c This indicates the adjusted filter cutoff frequency, in kHz, used to determine the signal passband range of the adaptive RC filter circuit; f c0 The reference filter cutoff frequency at 25℃ is expressed in kHz and is the initial setting parameter of the adaptive RC filter circuit; K represents the comprehensive interference coefficient, calculated by the core collaborative control module; S represents the signal-to-noise ratio of the positioning signal, expressed in dB and acquired by the signal-to-noise ratio acquisition unit; ξ represents the adjusted damping coefficient, used to control the signal attenuation characteristics of the adaptive RC filter circuit; ξ0 represents the reference damping coefficient at 25℃ and is the initial setting parameter of the adaptive RC filter circuit.

7. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 6, characterized in that, The positioning error correction module uses a formula to correct positioning errors, including the positioning error correction formula: Cutoff frequency weighting formula: W f =0.4+0.3K; And the formula for the weighting of the damping coefficient: W ξ =0.2+0.5K-0.1S; Where ΔP represents the corrected positioning error in meters (m), and is the final positioning error value output to the main controller of the navigation module; P0 represents the original positioning error in meters (m), i.e., the uncorrected positioning error value of the navigation module; W f The cutoff frequency weight is used to adjust the degree of influence of the filter cutoff frequency on positioning error correction; f c Indicates the adjusted filter cutoff frequency; f c0 This indicates the reference filter cutoff frequency at 25°C; W ξ ξ represents the damping coefficient weight, used to adjust the degree of influence of the damping coefficient on the positioning error correction; ξ represents the adjusted damping coefficient; γ represents the multipath reflection coefficient; K represents the comprehensive interference coefficient; S represents the signal-to-noise ratio of the positioning signal; e is a natural constant with a value of 2.71828.

8. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 1, characterized in that, The core collaborative control module employs an ARM Cortex-M7 chip for executing collaborative control logic. This module receives and judges the value of the comprehensive interference coefficient K in real time. When the comprehensive interference coefficient K is greater than or equal to 0.6, the core collaborative control module sends a strong anti-interference mode command to the dynamic filtering and control module. Based on this command, the dynamic filtering and control module increases the damping coefficient to 1.2-1.5 times ξ0 and decreases the filter cutoff frequency to f. c0 The weight is increased by 0.6-0.8 times, and simultaneously, the core collaborative control module sends a weight adjustment command to the positioning error correction module. The positioning error correction module increases the cutoff frequency weight W according to the weight adjustment command. f With damping coefficient weight W ξ To enhance anti-interference capabilities.

9. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 3, characterized in that, The programmable capacitor of the adaptive RC filter circuit is adjusted according to the filter cutoff frequency f. c Configure the parameters; the configuration logic is as follows: C prog =1 / (2π×R fix ×f c ); Where C prog R is the programmable capacitor value. fix The fixed resistor value in the adaptive RC filter circuit is defined as follows: the damping resistor of the adaptive RC filter circuit is configured according to the adjusted damping coefficient ξ, and the configuration logic is as follows: Where R damp L is the resistance value of the damping resistor, and L is the inductance value in the adaptive RC filter circuit.

10. The anti-interference processing method for positioning signals of an integrated circuit-type automotive navigation module according to claim 4, characterized in that, After receiving the filtering parameters, the Kalman filter algorithm unit first calculates the cutoff frequency weight W based on the comprehensive interference coefficient K and the signal-to-noise ratio S of the positioning signal. f With damping coefficient weight W ξ Then the W f W ξ The error feedback unit calculates the corrected positioning error by substituting the filtering parameters into the positioning error correction formula. Simultaneously, it sends the corrected positioning error to the navigation module's main controller and sends it back to the core collaborative control module. The core collaborative control module updates the reference value for the next parameter adjustment based on the returned corrected positioning error. When the comprehensive interference coefficient K received three consecutive times is greater than or equal to 0.6, the core collaborative control module adjusts the 25°C reference filtering cutoff frequency f. c0 The value was updated to 0.8 times the original baseline value to optimize the response speed of parameter adjustments in subsequent strong interference scenarios.

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