A multi-frequency positioning module of a smart watch and an interference adaptive suppression method

Abstract

The application belongs to the technical field of intelligent wearable devices, in particular to a multi-frequency positioning module of a smart watch and an interference adaptive suppression method, the module comprising the following units: a multi-frequency GNSS antenna array, a radio frequency front-end unit, a multi-frequency cooperative interference monitoring unit, a hierarchical adaptive suppression unit, a baseband processing unit, a power consumption management unit, and a self-interference cancellation unit; a dual solution is formed through hardware module cooperation and intelligent algorithm design; on the hardware level, a self-interference cancellation unit is specially arranged, a passive filtering and active cancellation combined scheme is adopted, an internal module timing synchronization design is matched, internal interference generated by modules such as Bluetooth, Wi-Fi, and heart rate sensors is isolated in a targeted manner, and self-interference isolation is greatly improved; in cooperation with a cross-frequency band cooperative suppression mechanism, the defects of poor adaptability of traditional fixed parameter filtering or single suppression strategy are completely solved, and strong anti-interference effect is achieved.

Description

Technical Field

[0001] This invention belongs to the field of smart wearable device technology, specifically a multi-frequency positioning module for a smartwatch and an adaptive interference suppression method. Background Technology

[0002] With the rapid development of smart wearable technology and Global Navigation Satellite System (GNSS), smartwatches have evolved from simple timekeeping tools into portable terminals integrating positioning, navigation, health monitoring, and mobile communication. Multi-frequency GNSS positioning technology, by simultaneously receiving satellite signals from multiple frequency bands, can effectively reduce positioning errors caused by ionospheric delay and multipath effects. Compared with single-frequency positioning solutions, its positioning accuracy and environmental adaptability are significantly improved, making it one of the core configurations of mid-to-high-end smartwatches. However, in real-world usage scenarios, electromagnetic interference is prevalent and directly affects the quality of satellite signal reception. Therefore, interference suppression capability has become a key factor determining the performance of multi-frequency positioning modules.

[0003] This technology is widely applicable to various smartwatch positioning scenarios, including high-precision trajectory tracking in outdoor sports, real-time location feedback in child and elderly monitoring, accurate positioning guidance in emergency rescue, and scenarios with high positioning reliability requirements such as urban navigation and outdoor exploration. Furthermore, its core technology can be extended to other smart wearable devices such as smart bracelets and AR glasses, as well as mobile terminals requiring low-power, high-precision positioning, such as drones and shared bicycles, demonstrating broad application prospects.

[0004] Existing multi-frequency positioning solutions for smartwatches still have significant technical shortcomings: First, their anti-interference capabilities are insufficient. They mostly employ fixed-parameter filtering or single suppression strategies, which cannot adapt to complex and varied interference types such as narrowband, wideband, and pulse, easily leading to a sharp drop in positioning accuracy or even positioning failure. Second, it is difficult to balance power consumption and performance. Complex interference suppression algorithms can significantly increase hardware power consumption, which contradicts the limited battery capacity of smartwatches and shortens battery life. Third, self-interference handling is imperfect. The dense integration of modules such as Bluetooth, Wi-Fi, and heart rate sensors inside smartwatches makes them prone to internal interference. Existing solutions lack targeted suppression designs, further affecting positioning stability.

[0005] Therefore, the present invention provides a multi-frequency positioning module for a smartwatch and an adaptive interference suppression method. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a multi-frequency positioning module for a smartwatch, which includes the following units: Multi-frequency GNSS antenna array, radio frequency front-end unit, multi-frequency cooperative interference monitoring unit, hierarchical adaptive suppression unit, baseband processing unit, power consumption management unit, and self-interference cancellation unit; The multi-frequency GNSS antenna array is used to receive satellite navigation signals in at least two frequency bands, and the antenna array is a miniaturized design; The radio frequency front-end unit includes low-noise amplifiers, mixers, and filters for multiple frequency bands; The multi-frequency cooperative interference monitoring unit is connected to the radio frequency front-end unit and is used to collect the power spectrum of signals in each frequency band in real time, extract the time domain features and frequency domain features of the interference signal, and construct the interference feature vector. The hierarchical adaptive suppression unit, connected to the multi-frequency cooperative interference monitoring unit, includes a narrowband notch filter module, a broadband adaptive filter module, and a pulse blanking module. The baseband processing unit is connected to the hierarchical adaptive suppression unit and is used to demodulate and perform multi-frequency fusion positioning calculation on the signals of each frequency band after interference suppression. The power management unit is connected to each of the above units and dynamically adjusts the operating parameters of each unit according to the smartwatch battery level and CPU load. The self-interference cancellation unit is used to isolate interference signals generated by various modules inside the smartwatch.

[0008] Preferably, an adaptive interference suppression method for a multi-frequency positioning module of a smartwatch, applicable to the positioning module described above, includes the following steps: S1. Multi-frequency signal acquisition steps: The satellite navigation signals from at least two frequency bands in L1 / L2 / L5 are simultaneously received by a multi-frequency GNSS antenna array and transmitted to the radio frequency front-end unit for amplification, down-conversion and preliminary filtering. S2. Interference Feature Extraction Steps: The multi-frequency cooperative interference monitoring unit performs time-frequency analysis on the preprocessed signals of each frequency band, extracts the time-domain and frequency-domain features of the interference signal, and constructs the interference feature vector; the multi-frequency cooperative interference monitoring unit uses fast Fourier transform to realize frequency domain analysis. S3, Interference Intelligent Recognition Steps: The interference feature vector is input into a pre-trained deep learning model, which outputs information on interference type, interference intensity level, and frequency distribution. S4. Hierarchical suppression execution steps: Based on the interference identification results, the suppression module is dynamically selected and its parameters are adjusted: For narrowband interference, the narrowband notch module is activated, which is implemented using a second-order infinite impulse response filter. The notch center frequency and bandwidth are dynamically set according to the interference frequency distribution. For broadband interference, the broadband adaptive filtering module is activated, and the normalized minimum mean square algorithm is used to dynamically adjust the filter coefficients. For pulse interference, activate the pulse blanking module to detect the pulse position and set it to zero. S5. Multi-frequency fusion positioning steps: The signals of each frequency band after interference suppression are jointly calculated, and the final positioning result is obtained by a weighted fusion algorithm; the weight of each frequency band in the weighted fusion algorithm is dynamically adjusted according to the signal-to-noise ratio. S6. Low power consumption optimization steps: Real-time monitoring of battery level and CPU load; when battery level is below 20% or CPU load is above 80%, algorithm complexity is reduced and non-critical frequency band reception is turned off.

[0009] Preferably, the integrated multi-frequency radio frequency chip supports simultaneous reception of multi-band signals from at least two satellite navigation systems, and has built-in multi-band signal processing components to meet the low power consumption and miniaturized installation requirements of smartwatches.

[0010] Preferably, the multi-frequency cooperative interference monitoring unit integrates a spectrum analysis component, which can cover the operating frequency band corresponding to the satellite navigation signal, realize real-time acquisition of signal power spectrum and time-frequency domain analysis, and provide data support for interference feature extraction and identification.

[0011] Preferably, the adaptive filtering update formula is: in, This is the filter coefficient vector; Step size factor; This is an error signal; The input signal vector.

[0012] Preferably, the interference feature similarity calculation uses the cosine similarity formula: in, This represents the similarity value. This is a real-time interference feature vector; This refers to the standard interference feature vector in the database; The number of feature dimensions.

[0013] Preferably, the multi-frequency weighted fusion formula is: in, This is the final location result; For the first Frequency band weighting coefficient; For the first Frequency band positioning results; This refers to the number of frequency bands.

[0014] Preferably, the multi-frequency GNSS antenna array is a ceramic dielectric antenna or a flexible FPC antenna, integrated into the watch case or strap.

[0015] The beneficial effects of this invention are as follows: 1. The multi-frequency positioning module and interference adaptive suppression method for a smartwatch described in this invention form a dual solution through hardware module collaboration and intelligent algorithm design. At the hardware level, a dedicated self-interference cancellation unit is set up, adopting a scheme combining passive filtering and active cancellation, coupled with internal module timing synchronization design, to specifically isolate internal interference generated by modules such as Bluetooth, Wi-Fi, and heart rate sensors, significantly improving self-interference isolation. At the same time, the multi-frequency collaborative interference monitoring unit integrates spectrum analysis components, covering the satellite navigation operating frequency band, and can collect the power spectrum of multi-frequency band signals in real time and extract time-domain and frequency-domain features. Combined with the CNN-LSTM combined deep learning model, it accurately identifies complex interference types such as narrowband, wideband, and pulse, providing data support for the formulation of suppression strategies. The hierarchical adaptive suppression unit dynamically switches notch filtering, filtering, and blanking strategies based on the interference identification results, and with the cross-frequency band collaborative suppression mechanism, it completely solves the defects of poor adaptability of traditional fixed parameter filtering or single suppression strategies, achieving a strong anti-interference effect with a total suppression ratio ≥55dB.

[0016] 2. The multi-frequency positioning module and interference adaptive suppression method for smartwatches described in this invention construct a low-power operating system through hardware and software co-optimization. On the hardware side, an integrated multi-frequency radio frequency chip is used, possessing low noise and high gain characteristics, adapting to the miniaturization and low-power requirements of smartwatches, significantly reducing basic hardware power consumption. On the software side, the power management unit monitors battery level and CPU load in real time, dynamically adjusting algorithm complexity, number of frequency bands received, and filter order. When the battery is insufficient or the load is too high, power consumption is reduced by more than 30% while maintaining basic positioning accuracy by shutting down non-critical frequency bands and simplifying model inference accuracy. Simultaneously, the multi-frequency weighted fusion algorithm dynamically allocates frequency band weights, emphasizing the role of high-quality signals and weakening low-quality signal interference, avoiding wasted computing power due to invalid signal processing, further achieving a precise balance between performance and power consumption, and resolving the core contradiction between the limited battery capacity of smartwatches and the high power consumption of complex algorithms. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is the overall system architecture and data flow diagram of the smartwatch multi-frequency positioning module in this invention; Figure 2 This is a flowchart illustrating the core workflow of the spectrum analysis component in this invention. Figure 3 This is a flowchart of the multi-band synchronous monitoring and cross-band interference correlation analysis process in this invention; Figure 4 This is a flowchart of the interference feature vector construction and deep learning model input process in this invention; Figure 5 This is a flowchart of the interference identification result-hierarchical adaptive suppression strategy execution in this invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] like Figures 1 to 5 As shown in the embodiment of the present invention, a multi-frequency positioning module for a smartwatch includes the following units: Multi-frequency GNSS antenna array, radio frequency front-end unit, multi-frequency cooperative interference monitoring unit, hierarchical adaptive suppression unit, baseband processing unit, power consumption management unit, and self-interference cancellation unit; The aforementioned multi-frequency GNSS antenna array is used to receive satellite navigation signals in at least two frequency bands, and the antenna array is a miniaturized design; The aforementioned radio frequency front-end unit includes low-noise amplifiers, mixers, and filters for multiple frequency bands, used to amplify, down-convert, and pre-filter the received satellite navigation signals, and employs an integrated multi-frequency radio frequency chip; The aforementioned multi-frequency collaborative interference monitoring unit, connected to the aforementioned radio frequency front-end unit, is used to acquire the power spectrum of signals in each frequency band in real time, extract the time-domain and frequency-domain features of the interference signal and construct the interference feature vector. It identifies the interference type, intensity and frequency distribution through a pre-trained deep learning model. The aforementioned deep learning model is a combination model of convolutional neural network (CNN) and long short-term memory network (LSTM), with an interference identification accuracy of ≥95%. The aforementioned hierarchical adaptive suppression unit, connected to the aforementioned multi-frequency cooperative interference monitoring unit, includes a narrowband notch filter module, a broadband adaptive filter module, and a pulse blanking module. It dynamically selects and adjusts the suppression strategy based on the interference identification results to achieve cross-frequency band cooperative suppression with a total suppression ratio ≥55dB. The aforementioned baseband processing unit, connected to the aforementioned hierarchical adaptive suppression unit, is used to demodulate and perform multi-frequency fusion positioning calculations on the signals of each frequency band after interference suppression. It supports RTK differential positioning, and the positioning accuracy can reach the centimeter level when there is a differential signal, and the positioning accuracy is ≤2 meters when there is no differential signal. The aforementioned power management unit is connected to each of the aforementioned units and dynamically adjusts the operating parameters of each unit according to the smartwatch's battery level and CPU load. When the battery level is below 20% or the CPU load is above 80%, the algorithm complexity is reduced and non-critical frequency band reception is turned off. The aforementioned self-interference cancellation unit is used to isolate interference signals generated by various modules (Bluetooth, Wi-Fi, heart rate sensor, etc.) inside the smartwatch, with an isolation degree ≥35dB.

[0021] like Figures 1 to 5 As shown, an adaptive interference suppression method for a multi-frequency positioning module of a smartwatch is described. This method is applicable to the positioning module mentioned above and includes the following steps: S1. Multi-frequency signal acquisition steps: The satellite navigation signals from at least two frequency bands in L1 / L2 / L5 are simultaneously received by a multi-frequency GNSS antenna array and transmitted to the radio frequency front-end unit for amplification, down-conversion and preliminary filtering. S2. Interference Feature Extraction Steps: The multi-frequency cooperative interference monitoring unit performs time-frequency analysis on the preprocessed signals of each frequency band, extracts the time-domain features (amplitude, phase, pulse width) and frequency-domain features (center frequency, bandwidth, power spectral density) of the interference signal, and constructs the interference feature vector. The multi-frequency cooperative interference monitoring unit uses Fast Fourier Transform (FFT) to realize frequency domain analysis, with 1024 FFT points and a frequency resolution ≤1kHz. S3, Interference Intelligent Recognition Steps: The above interference feature vectors are input into a pre-trained deep learning model, which outputs interference type (narrowband / wideband / pulse / sweep), interference intensity level and frequency distribution information. S4. Layered suppression execution steps: Based on the interference identification results, the suppression module is dynamically selected and its parameters are adjusted: For narrowband interference, the narrowband notch module is activated, which is implemented using a second-order infinite impulse response (IIR) filter. The notch center frequency and bandwidth are dynamically set according to the interference frequency distribution. The notch bandwidth can be adjusted in the range of 10kHz-1MHz, and the suppression ratio is ≥40dB. For broadband interference, the broadband adaptive filtering module is activated, and the normalized least mean square (NLMS) algorithm is used to dynamically adjust the filter coefficients and step size factor. The value range is 0.01-0.1, and the convergence rate is ≤10ms; For pulse interference, the pulse blanking module is activated to detect the pulse position and set it to zero. The blanking time accuracy is ≤1. ; S5. Multi-frequency fusion positioning steps: The signals of each frequency band after interference suppression are jointly calculated, and the final positioning result is obtained through a weighted fusion algorithm. In the above weighted fusion algorithm, the weight of each frequency band is dynamically adjusted according to the signal-to-noise ratio (SNR). When SNR≥30dB, the weight coefficient is ≥0.8, and when SNR≤10dB, the weight coefficient is ≤0.2. S6. Low power consumption optimization steps: Real-time monitoring of battery level and CPU load; when battery level is below 20% or CPU load is above 80%, algorithm complexity is reduced, non-critical frequency band reception is turned off, and basic positioning accuracy is maintained at ≤5 meters.

[0022] like Figures 1 to 5 As shown, the above-mentioned integrated multi-frequency radio frequency chip supports the simultaneous reception of multi-band signals from at least two satellite navigation systems. It has a built-in multi-band signal processing component and features low noise and high gain characteristics, making it suitable for the low power consumption and miniaturized installation requirements of smartwatches. This integrated multi-band RF chip integrates low-noise amplifiers, mixers, bandpass filters, and intermediate frequency processing circuits across multiple frequency bands into a single chip package, significantly reducing the chip's footprint. This allows it to fit into the limited installation area inside a smartwatch case, avoiding signal attenuation and interference caused by the dispersed layout of multiple components. The low-noise characteristic effectively reduces noise introduction during signal amplification, ensuring the effective extraction of weak satellite signals, while the high-gain characteristic amplifies the received weak satellite signals to an amplitude range recognizable by subsequent processing units, balancing signal integrity and processing efficiency. Simultaneously, the chip employs a low-power manufacturing process and a dynamic power consumption adjustment mechanism, adaptively adjusting the operating current based on signal reception status. This maintains a low power consumption level even in multi-band simultaneous reception mode, preventing excessive power consumption in the RF front-end from reducing the smartwatch's battery life, perfectly addressing the core constraint of limited smartwatch battery capacity.

[0023] like Figures 1 to 5 As shown, the multi-frequency cooperative interference monitoring unit integrates a spectrum analysis component, which can cover the working frequency band corresponding to satellite navigation signals, realize real-time acquisition of signal power spectrum and time-frequency domain analysis, and provide data support for interference feature extraction and identification. This spectrum analysis component boasts wide-band coverage, fully covering the operating frequency bands of mainstream satellite navigation systems such as BeiDou, GPS, and Galileo, ensuring the capture of satellite signals and interference signals in each band without omission. During operation, the component synchronously acquires signals from each frequency band output by the RF front-end according to a preset sampling rate, converting the time-domain signal into frequency-domain power spectrum data. By analyzing the features of the power spectrum data, abnormal spectral components in the signal can be quickly identified, providing a preliminary assessment of the presence of interference signals. Simultaneously, the component supports joint time-frequency domain analysis, extracting both time-domain features (such as amplitude variations, pulse width, and phase jitter) and frequency-domain features (such as center frequency, bandwidth, and peak power spectral density), providing comprehensive data input for subsequent construction of multi-dimensional interference feature vectors. Compared to traditional single-band monitoring schemes, this integrated spectrum analysis component enables simultaneous monitoring of multi-band signals and cross-band interference correlation analysis, avoiding the one-sidedness of interference identification caused by single-band monitoring. This lays a solid foundation for deep learning models to accurately identify interference types, intensity, and frequency distribution, ensuring the targetedness and effectiveness of subsequent hierarchical adaptive suppression strategies.

[0024] like Figures 1 to 5 As shown, the above adaptive filtering update formula is: in, This is the filter coefficient vector; Step size factor; This is an error signal; The input signal vector; Compared to existing technologies using fixed-coefficient filtering or single-step-length adaptive filtering formulas, this formula adjusts the filter coefficients through real-time linkage between the error signal and the input signal. This allows for rapid tracking of the dynamic changes in the interference signal, avoiding the problems of slow convergence and large steady-state error in existing technologies. While ensuring the suppression effect, it improves the algorithm's adaptability to complex and variable interference scenarios, making it more suitable for the dynamic positioning needs of smartwatches.

[0025] like Figures 1 to 5 As shown, the cosine similarity formula is used to calculate the similarity of the above interference features: in, The similarity value is (0≤S≤1). This is a real-time interference feature vector; This refers to the standard interference feature vector in the database; The number of feature dimensions; Existing technologies often employ calculation methods such as Euclidean distance, which are easily affected by differences in feature dimension scale, leading to large interference recognition errors. This formula can effectively eliminate feature dimension scale interference, accurately quantify the matching degree between real-time interference and standard interference, improve the accuracy and stability of interference recognition, provide a reliable basis for the accurate execution of subsequent hierarchical suppression strategies, and solve the defect of existing technologies that are easily affected by noise in interference recognition.

[0026] like Figures 1 to 5 As shown, the above multi-frequency weighted fusion formula is: in, This is the final location result; For the first Frequency band weighting coefficient; For the first Frequency band positioning results; Number of frequency bands; Existing technologies mostly employ equal-weight fusion or fixed-weight fusion methods, which cannot adapt to the dynamic changes in signal quality across different frequency bands. They are prone to causing a decrease in overall positioning accuracy due to distortion of signals in a single frequency band. This formula achieves differentiated fusion of multi-frequency band positioning results through dynamic weight allocation, which can highlight the role of high-quality frequency band signals and weaken the interference of low-quality frequency band signals. This significantly improves the reliability and accuracy of positioning results in complex environments and is suitable for the multi-scenario positioning needs of smartwatches.

[0027] like Figures 1 to 5 As shown, the aforementioned multi-frequency GNSS antenna array is a ceramic dielectric antenna or a flexible FPC antenna, integrated into the watch case or strap.

[0028] Example 1: Urban canyon scene (narrowband interference) Test conditions: GPS L1 band narrowband interference exists (center frequency 1.575GHz, bandwidth 50kHz, strength 0dBm), no RTK differential signal; Test results: Interference rejection ratio = 45dB, positioning accuracy = 1.8 meters, operating current = 28mA, battery life = 8.9 hours.

[0029] Example 2: Indoor shopping mall scenario (broadband interference) Test conditions: Broadband interference exists (bandwidth 100MHz, center frequency 1.2GHz, intensity -10dBm), no RTK differential signal; Test results: Interference rejection ratio = 58dB, positioning accuracy = 2.0 meters, operating current = 32mA, battery life = 7.8 hours.

[0030] Example 3: Outdoor sports scenario (pulse interference) Test conditions: Pulse interference is present (pulse width 1) (Repetition frequency 1kHz, intensity 0dBm), with RTK differential signal; Test results: Interference rejection ratio = 42dB, positioning accuracy = 0.03 meters (centimeter level), operating current = 35mA, battery life = 7.2 hours.

[0031] Example 4: Industrial Plant Scenario (Mixed Interference) Test conditions: Narrowband + wideband + pulse mixed interference, no RTK differential signal; Test results: Interference rejection ratio = 55dB, positioning accuracy = 2.2 meters, operating current = 38mA, battery life = 6.6 hours.

[0032] Comparative Example 1: Existing single-frequency positioning + fixed notch filter scheme Test conditions: Same as in Example 1 (narrowband interference in urban canyons); Test results: Interference rejection ratio = 32dB, positioning accuracy = 5.6 meters, operating current = 35mA, battery life = 7.2 hours.

[0033] Comparative Example 2: Existing multi-frequency positioning + single adaptive filtering scheme Test conditions: Same as in Example 2 (indoor shopping mall broadband interference); Test results: Interference rejection ratio = 38dB, positioning accuracy = 4.2 meters, operating current = 45mA, battery life = 5.5 hours.

[0034] Test Result Analysis Comparative examples and comparative studies show that the interference suppression ratio of this invention is ≥42dB under different interference scenarios, which is 10-20dB higher than the prior art; the positioning accuracy is ≤2.2 meters (without RTK) or centimeter level (with RTK), which is more than 50% higher than the prior art; the operating current is ≤38mA, and the battery life is ≥6.6 hours, which is more than 30% lower in power consumption and more than 20% longer in battery life than the prior art. This fully demonstrates that this invention can effectively solve the shortcomings of the prior art and achieve synergistic optimization of high precision, strong anti-interference, and low power consumption. This invention achieves three core advantages through systematic innovation in multi-frequency collaborative interference monitoring, hierarchical adaptive suppression, low-power intelligent optimization, and self-interference cancellation: Significantly improved anti-interference capability: Dynamically adaptable suppression strategies are adopted for different types of interference, and cross-frequency band collaborative suppression ensures stable operation in complex interference scenarios, with a suppression ratio of ≥55dB, which is far superior to existing technologies; High and stable positioning accuracy: Multi-frequency fusion positioning combined with RTK differential technology achieves positioning accuracy of ≤2 meters in non-differential scenarios and centimeter-level in differential scenarios, meeting the needs of different scenarios; Low-power smartwatch adaptation: By dynamically adjusting algorithm complexity and frequency band reception strategy, power consumption is reduced by more than 30%, and battery life is significantly extended, solving the pain point of limited battery capacity in smartwatches.

[0035] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A multi-frequency positioning module for a smartwatch, characterized in that: This module includes the following units: Multi-frequency GNSS antenna array, radio frequency front-end unit, multi-frequency cooperative interference monitoring unit, hierarchical adaptive suppression unit, baseband processing unit, power consumption management unit, and self-interference cancellation unit; The multi-frequency GNSS antenna array is used to receive satellite navigation signals in at least two frequency bands, and the antenna array is a miniaturized design; The radio frequency front-end unit includes low-noise amplifiers, mixers, and filters for multiple frequency bands; The multi-frequency cooperative interference monitoring unit is connected to the radio frequency front-end unit and is used to collect the power spectrum of signals in each frequency band in real time, extract the time domain features and frequency domain features of the interference signal, and construct the interference feature vector. The hierarchical adaptive suppression unit, connected to the multi-frequency cooperative interference monitoring unit, includes a narrowband notch filter module, a broadband adaptive filter module, and a pulse blanking module. The baseband processing unit is connected to the hierarchical adaptive suppression unit and is used to demodulate and perform multi-frequency fusion positioning calculation on the signals of each frequency band after interference suppression. The power management unit is connected to each of the above units and dynamically adjusts the operating parameters of each unit according to the smartwatch battery level and CPU load. The self-interference cancellation unit is used to isolate interference signals generated by various modules inside the smartwatch.

2. An adaptive interference suppression method for a multi-frequency positioning module of a smartwatch, characterized in that: This method is applicable to the positioning module in claim 1 and includes the following steps: S1. Multi-frequency signal acquisition steps: The satellite navigation signals from at least two frequency bands in L1 / L2 / L5 are simultaneously received by a multi-frequency GNSS antenna array and transmitted to the radio frequency front-end unit for amplification, down-conversion and preliminary filtering. S2. Interference Feature Extraction Steps: The multi-frequency cooperative interference monitoring unit performs time-frequency analysis on the preprocessed signals of each frequency band, extracts the time-domain and frequency-domain features of the interference signal, and constructs the interference feature vector; the multi-frequency cooperative interference monitoring unit uses fast Fourier transform to realize frequency domain analysis. S3, Interference Intelligent Recognition Steps: The interference feature vector is input into a pre-trained deep learning model, which outputs information on interference type, interference intensity level, and frequency distribution. S4. Hierarchical suppression execution steps: Based on the interference identification results, the suppression module is dynamically selected and its parameters are adjusted: For narrowband interference, the narrowband notch module is activated, which is implemented using a second-order infinite impulse response filter. The notch center frequency and bandwidth are dynamically set according to the interference frequency distribution. For broadband interference, the broadband adaptive filtering module is activated, and the normalized minimum mean square algorithm is used to dynamically adjust the filter coefficients. For pulse interference, activate the pulse blanking module to detect the pulse position and set it to zero. S5. Multi-frequency fusion positioning steps: The signals of each frequency band after interference suppression are jointly calculated, and the final positioning result is obtained by a weighted fusion algorithm; the weight of each frequency band in the weighted fusion algorithm is dynamically adjusted according to the signal-to-noise ratio. S6. Low power consumption optimization steps: Real-time monitoring of battery level and CPU load.

3. The multi-frequency positioning module and interference adaptive suppression method for a smartwatch according to claim 1, characterized in that: The integrated multi-frequency radio frequency chip supports simultaneous reception of multi-band signals from at least two satellite navigation systems, and has built-in multi-band signal processing components to meet the low power consumption and miniaturized installation requirements of smartwatches.

4. The multi-frequency positioning module and interference adaptive suppression method for a smartwatch according to claim 1, characterized in that: The multi-frequency cooperative interference monitoring unit integrates a spectrum analysis component, covering the operating frequency band corresponding to satellite navigation signals, and realizes real-time acquisition and time-frequency domain analysis of signal power spectrum, providing data support for interference feature extraction and identification.

5. The interference adaptive suppression method for a multi-frequency positioning module of a smartwatch according to claim 2, characterized in that: The adaptive filtering update formula is as follows: in, This is the filter coefficient vector; Step size factor; This is an error signal; The input signal vector.

6. The interference adaptive suppression method for a multi-frequency positioning module of a smartwatch according to claim 2, characterized in that: The similarity of the interference features is calculated using the cosine similarity formula: in, This represents the similarity value. This is a real-time interference feature vector; This refers to the standard interference feature vector in the database; The number of feature dimensions.

7. The interference adaptive suppression method for a multi-frequency positioning module of a smartwatch according to claim 2, characterized in that: The multi-frequency weighted fusion formula is as follows: in, This is the final location result; For the first Frequency band weighting coefficient; For the first Frequency band positioning results; This refers to the number of frequency bands.

8. The multi-frequency positioning module and interference adaptive suppression method for a smartwatch according to claim 1, characterized in that: The multi-frequency GNSS antenna array is a ceramic dielectric antenna or a flexible FPC antenna, integrated into the watch case or strap.

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