Method for detecting and identifying a concealed GPS locator

Abstract

The present application relates to the technical field of GPS locator detection, and particularly relates to a detection and identification method for a hidden GPS locator; the present application aims at the industry pain point that traditional methods cannot detect due to the deep sleep of a wired dormant GPS locator, no RF signal output, and innovatively uses the 100KHz-700KHz low-frequency electromagnetic field signal continuously radiated by the DC-DC step-down switching power supply circuit in the GPS locator as the detection basis, so as to get rid of the dependence of traditional detection technology on the RF transmission signal of the equipment, and to realize stable and blind area-free static detection in the state that the vehicle is completely stationary and the GPS locator main control module and the Internet of Things module are deeply dormant, thereby eliminating the missed detection hidden danger of the dormant equipment in principle.

Description

Technical Field

[0001] This invention relates to the field of GPS locator detection technology, and specifically to a method for detecting and identifying hidden GPS locators. Background Technology

[0002] With the rapid popularization of the Internet of Things and positioning technology, GPS locators are widely used in scenarios such as vehicle anti-theft and item tracking.

[0003] Current detection methods mostly involve specialized technicians using detectors, or driving the vehicle into a shielded room and then bringing the detector inside, during which the vehicle is essentially stationary. Currently, commonly available hidden GPS trackers can be broadly categorized into three types based on their operating mode and power supply: The first type is the wireless sleep GPS tracker, powered by a built-in battery, employing an intermittent sleep and timed wake-up mode; the second type is the wired real-time online GPS tracker, directly connected to the vehicle's power supply for continuous power, constantly maintaining signal transmission and data upload; the third type is the wired sleep GPS tracker, also connected to the vehicle's power supply, but with a sleep / wake-up mechanism, making it the most concealed and difficult-to-detect type of tracker.

[0004] For the three types of GPS trackers mentioned above, the existing detection methods and instruments commonly used in the industry have obvious limitations: For wireless dormant GPS trackers, they can be detected by capturing their radio frequency signals when they wake up using conventional signal detectors; for wired real-time online GPS trackers, the device location can be quickly determined by detecting radio waves and radio frequency signals; however, for wired dormant GPS trackers, existing detection technologies and instruments may miss some. The core pain point of wired dormant GPS trackers is that they currently have extremely strong anti-detection capabilities. These devices are generally equipped with a speed limit setting function and a vibration sensor detection mechanism. When the vehicle is stationary, the device's main control chip and IoT communication module completely enter dormant mode and do not emit any radio frequency signals. The device only wakes up and uploads positioning data when the vehicle reaches the set speed and the vibration sensor detects the actual driving state of the vehicle, thereby avoiding detection by conventional radio frequency GPS detectors. Therefore, in routine inspection scenarios where the vehicle is stationary, existing technologies are completely unable to detect this type of wired dormant GPS tracker, resulting in the inability to eliminate the risk of vehicle privacy leakage in advance. This has become a technical problem that practitioners in the GPS tracker inspection industry urgently need to solve.

[0005] Existing detection technologies all focus on the radio frequency signals emitted by GPS locators when they are working, ignoring the inherent electromagnetic radiation characteristics of the internal power supply circuits of the devices. This makes it impossible to break through the anti-detection barriers of wired dormant GPS locators. There is currently no effective detection technology for this type of device in the industry, and a brand-new detection solution is urgently needed to fill the technological gap. Summary of the Invention

[0006] To overcome the shortcomings mentioned above, the present invention aims to provide a technical solution that can solve the above problems.

[0007] This invention addresses the aforementioned shortcomings of existing GPS locator detection technologies by providing a novel detection and identification technology for hidden GPS locators. It solves the industry pain point that existing technologies cannot detect wired dormant GPS locators when the vehicle is stationary. Simultaneously, it enables automatic identification and visualization of GPS locator models, filling a technological gap in the industry and improving the accuracy and efficiency of hidden GPS locator detection.

[0008] To achieve the aforementioned objectives, this invention discloses a novel detection and identification technology for concealed GPS locators. The core technology targets wired, dormant GPS locators for static detection, while simultaneously using electromagnetic spectrum fingerprinting to automatically identify the device model. The specific technical solution is as follows: A method for detecting and identifying a hidden GPS locator, used for detecting and identifying wired GPS locators, includes the following steps: Step 1: Select the magnetic field signal to be detected: The wired GPS locator contains a DC-DC step-down switching power supply circuit, a GPS main control chip, and an IoT module. The DC-DC step-down switching power supply circuit radiates a stable electromagnetic field signal of 100KHz-700KHz. Static detection is achieved by detecting the 100KHz-700KHz electromagnetic field signal radiated by the DC-DC step-down switching power supply circuit. Step 2, build a dedicated detection hardware system: Based on the electromagnetic field signal selected in Step 1, set up a high-frequency magnetic core inductor, an MCU module and a display module; wherein, the high-frequency magnetic core inductor is used to couple and pick up the target low-frequency electromagnetic field signal, the MCU module is used to receive the target low-frequency electromagnetic field signal collected by the high-frequency magnetic core inductor, and the display module is communicatively connected to the MCU module; Step 3, Real-time sensing and acquisition of electromagnetic signals: After the detection hardware system is powered on, it uses a high-frequency magnetic core inductor to sense electromagnetic field signals in the 100KHz-700KHz frequency band in the space at close range, and accurately acquires the 100KHz-700KHz electromagnetic field signals radiated by the DC-DC circuit; the sensed 100KHz-700KHz electromagnetic field signals are converted into analog electromagnetic field signals and input into the MCU module, waiting for subsequent ADC sampling and processing; Step 4, location investigation based on detected signal strength changes: The MCU module performs high-speed ADC sampling on the input analog magnetic field signal, converting the continuously changing analog electromagnetic signal into calculable and comparable digital level data; the MCU module analyzes the electromagnetic signal strength corresponding to the digital data in real time, and pushes the signal strength value and intensity change trend to the display module for visualization; the inspection personnel rely on the gradient difference of signal strength changes with detection distance and position during the detection process to gradually investigate and locate the precise installation position of the wired dormant GPS locator, completing the blind-spot-free location investigation in static scenarios.

[0009] Preferably, the MCU module uses an ESP32-P4 chip, which has an A / D pin to perform ADC sampling of the signal.

[0010] Preferably, the MCU module includes a main control chip and a sampling chip. The main control chip is an ESP32-P4 chip, and the sampling chip is an ADC1175 chip. The analog electromagnetic signal collected by the high-frequency magnetic core inductor is converted into digital signal by high-speed analog-to-digital conversion through the sampling chip, which converts the continuously changing analog electromagnetic signal into calculable and comparable digital level data and transmits it to the main control chip.

[0011] Preferably, after step 3, the GPS locator model is automatically identified based on electromagnetic spectrum fingerprint: the collected 100KHz-700KHz time domain signal is converted into a frequency domain signal by FFT fast Fourier transform, the unique spectrum fingerprint features of different GPS locators are extracted, and the device model is identified by comparison and matching with the pre-stored spectrum fingerprint database, and the device photo and matching similarity are displayed on the display module.

[0012] Preferably, the spectral fingerprint features include the oscillation frequency, harmonic frequency, main wave left-right jitter amplitude, and main wave up-down jitter amplitude of the DC-DC buck switching power supply circuit.

[0013] Preferably, the pre-stored spectrum fingerprint database includes collecting spectrum fingerprint data of GPS locators in the 100KHz-700KHz frequency band in advance, and classifying and storing the spectrum fingerprint features, actual photos of the devices, specific models, and parameter information of various GPS locators to complete the database construction; when the device is working, the built-in AI weight matching algorithm performs multi-dimensional weight comparison between the real-time collected spectrum fingerprints and the sample fingerprints in the database, automatically calculates the matching degree value, selects the best matching result, and realizes automatic identification of GPS locator models.

[0014] Preferably, the detection hardware system is further provided with a low-noise amplifier circuit, which is connected between the high-frequency magnetic core inductor and the MCU module. The low-noise amplifier circuit is used to amplify the gain and filter the 100KHz-700KHz electromagnetic field signal collected by the high-frequency magnetic core inductor to remove vehicle power frequency interference and stray electromagnetic interference, and then input the clean and effective analog electromagnetic signal into the MCU module.

[0015] Preferably, the low-noise amplifier circuit amplifies the analog electromagnetic field signal converted from the high-frequency magnetic core inductance by 100 to 1000 times.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the industry pain point of wired sleep GPS trackers being unable to be detected by traditional methods due to their deep sleep state and lack of radio frequency signal output. It innovatively employs a 100kHz-700kHz low-frequency electromagnetic field signal continuously radiated by the internal DC-DC step-down switching power supply circuit of the GPS tracker as the detection basis. This eliminates the dependence of traditional detection technologies on the device's radio frequency transmission signal, enabling stable, blind-spot-free static detection even when the vehicle is completely stationary and the GPS tracker's main control module and IoT module are in deep sleep state, fundamentally eliminating the risk of missed detection of sleep devices. Furthermore, it achieves weak low-frequency detection by configuring a high-frequency magnetic core inductor. The precise coupling and acquisition of magnetic field signals can be combined with 100-1000 times low-noise amplifier circuits and dedicated frequency band filtering to effectively filter out stray electromagnetic interference from vehicle power frequency and various original vehicle electrical appliances. This improves the acquisition accuracy and stability of weak background electromagnetic signals in complex vehicle environments. The MCU module relies on signal strength gradient changes to achieve multi-point scanning and precise source tracing and positioning, effectively improving the detection accuracy and efficiency of wired dormant GPS trackers in concealed locations. This technical solution effectively fills the technical gaps in existing technologies, such as the inability to statically detect wired dormant GPS trackers, short detection distance, weak anti-interference ability, and low detection efficiency.

[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2This is a circuit module connection block diagram of Embodiment 1 of the present invention; Figure 3 This is a circuit module connection block diagram of Embodiment 2 of the present invention; Figure 4 This is a circuit module connection block diagram of Embodiment 4 of the present invention; Figure 5 This is the circuit schematic diagram of Embodiment 4 of the present invention.

[0020] The reference numerals and names in the figure are as follows: 10 High-frequency magnetic core inductor, 20 Low-noise amplifier circuit, 30 MCU module, 31 Main control chip, 32 Sampling chip, 40 Display module, 50 DC-DC step-down switching power supply circuit. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1-2 This invention breaks through the traditional approach of radio frequency signal detection. By detecting the electromagnetic field signal radiated by the built-in DC-DC step-down switching power supply circuit 50 of the GPS locator, static detection of wired dormant GPS locators can be achieved.

[0023] Wired GPS trackers (including real-time online and sleep types) have internal chips that only support 3.3V or 5V low-voltage power supply, while vehicle power supplies are 12V (small cars) or 24V (large trucks) high-voltage DC power. Therefore, all wired GPS trackers must have a built-in DC-DC high-frequency buck switching power supply circuit. This circuit has a high-frequency oscillation range of 100KHz-700KHz and is responsible for stepping down the vehicle's high-voltage DC power to a usable low-voltage DC power supply. This DC-DC circuit has continuous operation characteristics. Even if the GPS tracker's main control chip and IoT communication module enter sleep mode, the DC-DC buck switching power supply circuit still maintains normal operation and continuously radiates electromagnetic field signals of a specific frequency. This invention uses this as the detection basis to achieve accurate detection of wired sleep GPS trackers when the vehicle is stationary.

[0024]

Example 1

[0025] Preferably, the low-noise amplifier circuit 20 amplifies the weak electromagnetic field signal collected by induction by 100-1000 times, which is suitable for the acquisition and preprocessing of weak DC-DC electromagnetic radiation signals in complex vehicle environments.

[0026] This solution abandons the traditional detection logic that relies on the radio frequency transmission signal of GPS locators. It innovatively utilizes the 100kHz-700kHz low-frequency electromagnetic field signal continuously radiated by the internal DC-DC step-down switching power supply circuit 50 of the wired dormant GPS locator to achieve detection. This overcomes the industry technical barrier that wired dormant GPS locators have no radio frequency signal when in dormant and cannot be detected. It can achieve blind-spot-free detection even when the vehicle is completely stationary and the device is in deep dormancy, fundamentally solving the problems of missed detections and false detections in traditional detection equipment. Simultaneously, this invention, combined with a high-frequency magnetic core inductor 10, accurately acquires weak electromagnetic signals in the target frequency band. Combined with a 100-1000x adjustable low-noise amplifier circuit 20, it completes signal gain and noise filtering, effectively avoiding interference from complex electromagnetic environments such as vehicle power frequency and original vehicle electrical systems, significantly improving the acquisition accuracy of weak background electromagnetic signals. The device boasts high efficiency, with a sampling range exceeding 20cm, enabling wide-area scanning of vehicles. Leveraging the high-speed ADC sampling and data processing capabilities of the ESP32-P4 chip, it rapidly converts analog electromagnetic signals into quantifiable digital signals and achieves precise source tracing and positioning through changes in signal strength gradient differences. It features fast detection response and high positioning accuracy. Furthermore, the invention's hardware architecture is simple and highly integrated, coupled with a visual display screen for real-time data display. It is easy to operate and highly adaptable, suitable for investigating concealed wired dormant GPS trackers in various small and large vehicles. This fills the technological gaps of existing technologies, which cannot statically detect dormant GPS trackers, suffer from poor detection stability, and have limited applicability. It reduces the difficulty of investigating vehicle privacy and security, improves the efficiency and coverage of investigating illegally concealed GPS trackers, and possesses strong practicality and industry promotion value.

[0027]

Example 2

[0028] This solution abandons the traditional detection logic that relies on the radio frequency transmission signal of GPS locators. Instead, it innovatively relies on the 100KHz-700KHz low-frequency electromagnetic field signal continuously radiated by the internal DC-DC step-down switching power supply circuit of the wired dormant GPS locator to achieve detection. This breaks through the industry technical barrier that wired dormant GPS locators have no radio frequency signal when in dormant and cannot be detected. It can complete blind-spot-free detection when the vehicle is completely stationary and the equipment is in deep dormancy, fundamentally solving the problems of missed detection and false detection of traditional detection equipment. This embodiment eliminates the traditionally essential low-noise amplifier circuit 20. Utilizing the high-sensitivity electromagnetic coupling characteristics of the high-frequency magnetic core inductor 10, it directly and accurately picks up weak background electromagnetic signals in the 100kHz-700kHz target frequency band, simplifying the overall hardware structure and reducing equipment size, cost, and the probability of hardware failure. Simultaneously, relying on the excellent signal sampling and digital filtering capabilities of the ESP32-P4 chip, it can directly perform noise reduction and optimization processing on the original weak electromagnetic signals, effectively avoiding interference from complex electromagnetic environments such as vehicle power frequency and original vehicle electrical systems. This ensures the accuracy and effectiveness of signal acquisition, with an effective detection scanning range exceeding 20cm, enabling large-scale vehicle scanning and inspection. The device leverages the high-speed ADC sampling and data processing capabilities of the ESP32-P4 chip to quickly convert analog electromagnetic signals into quantifiable digital signals, achieving precise source tracing and positioning through changes in signal strength gradient differences. It boasts fast detection response and high positioning accuracy. Compared to the solution that adds a low-noise amplifier circuit 20, this embodiment has a simpler hardware architecture, higher integration, and stronger stability. It eliminates additional problems such as signal distortion, amplification noise, circuit temperature drift, and component loss caused by the amplifier circuit. At the same time, it is equipped with a visual display screen to realize real-time data display. It is easy to operate and highly adaptable. It can be adapted to the investigation of concealed wired dormant GPS trackers in various small and large vehicles. It fills the technical gaps in the existing technology, which cannot statically detect dormant GPS, has a complex detection device structure, poor stability, and limited applicable scenarios. It significantly reduces the difficulty of vehicle privacy and security investigation, effectively improves the investigation efficiency and coverage of illegally concealed GPS trackers, and has strong practicality and industry promotion value.

[0029]

Example 3

[0030] Preferably, the spectral fingerprint features include the oscillation frequency, harmonic frequency, main wave left-right jitter amplitude, and main wave up-down jitter amplitude of the DC-DC buck switching power supply circuit 50.

[0031] Preferably, the pre-stored spectrum fingerprint database includes collecting spectrum fingerprint data of GPS locators in the 100KHz-700KHz frequency band in advance, and classifying and storing the spectrum fingerprint features, actual photos of the devices, specific models, and parameter information of various GPS locators to complete the database construction; when the device is working, the built-in AI weight matching algorithm compares the real-time collected spectrum fingerprints with the sample fingerprints in the database in multiple dimensions, automatically calculates the matching degree value, selects the best matching result, and realizes the automatic identification of GPS locator models.

[0032] Preferably, if the match is successful, the display module 40 immediately displays the photo, model and similarity percentage of the corresponding GPS locator; if the match fails, it displays the message "No known model GPS locator was identified".

[0033] In this invention, the ESP32-P4 chip has efficient data processing capabilities, which can quickly complete operations such as FFT transformation, ADC sampling, and spectrum fingerprint matching, ensuring the real-time performance of detection and identification; the display module 40 supports touch operation, which can realize functions such as zooming in and out of the spectrum, real-time viewing of signal strength, and previewing of device photos, improving the ease of operation.

[0034] By further adding intelligent GPS locator identification technology based on electromagnetic spectrum fingerprinting, the limitations of traditional detection equipment—which can only locate suspicious points and cannot identify device types—are overcome. Through FFT (Fast Fourier Transform) of the collected 100kHz-700kHz time-domain electromagnetic signals, the time-domain waveform signal is converted into a more stable and recognizable frequency-domain signal. This accurately extracts unique spectral fingerprint features specific to different GPS locator models, such as the DC-DC circuit oscillation frequency, harmonic frequency, and the amplitude of vertical and horizontal jitter of the main wave. Relying on a dedicated fingerprint database built from pre-collected spectrum data, device photos, and model parameters of a massive amount of mainstream GPS locators on the market, combined with a built-in AI multi-dimensional weighted matching algorithm, real-time fingerprint comparison and analysis are performed. This allows for rapid calculation of parameter matching degrees and selection of the optimal matching result, achieving automated, intelligent, and accurate identification of hidden GPS locator models. Simultaneously, the display module 40 provides a visual display of the device's features. It provides model number, physical photo, and matching similarity percentage. It automatically outputs a non-recognition prompt when a match is abnormal. The human-computer interaction is intuitive and clear, and the recognition results are reliable. Relying on the computing power of the ESP32-P4 chip, it can simultaneously and in parallel complete ADC sampling, FFT transformation, spectrum fingerprint comparison, and data refresh output, effectively ensuring the real-time, synchronous, and efficient nature of positioning detection and model recognition. Combined with convenient operation functions such as touch screen zoom and data preview, it enhances the intelligence and ease of operation of device screening. It can not only accurately identify dormant hidden GPS locators but also quickly trace the device model, providing accurate data support for privacy screening, evidence collection and verification, and risk assessment. It effectively solves the industry shortcomings of traditional screening equipment, such as single function, inability to trace device origin, poor recognition accuracy, and low intelligence, further improving the all-scenario, high-precision, and intelligent dormant GPS screening system, significantly enhancing the practicality and market promotion value of the equipment.

[0035]

Example 4

[0036] This technical solution adopts a dual-chip architecture consisting of the ESP32-P4 main control chip 31 and the external high-speed ADC1175 sampling chip 32. This replaces the traditional single-chip architecture that relies solely on the ESP32-P4's built-in ADC for sampling, computation, and control. It offers multiple core technological advantages for electromagnetic signal acquisition, spectral fingerprinting, and weak signal detection in the 100kHz-700kHz frequency band of DC-DC circuits. The traditional single ESP32-P4 chip architecture, limited by the low sampling rate of its built-in ADC, cannot fully cover the 100kHz-700kHz target detection frequency band and suffers from distortion, frequency loss, and waveform truncation issues when acquiring electromagnetic signals in the 600kHz-700kHz high-frequency band. Furthermore, the built-in ADC has low sampling accuracy and high response delay, making it prone to losing subtle spectral fingerprint features such as harmonic frequencies of DC-DC circuits and jitter amplitude of the main wave. This results in data distortion in subsequent FFT frequency domain transformation and a decrease in the accuracy of AI fingerprint matching. At the same time, a single chip needs to simultaneously carry out multiple tasks such as signal sampling, algorithm calculation, AI matching, human-computer interaction, and data storage, which consumes too much computing power and can easily cause problems such as task congestion, calculation lag, sampling interruption, and data parsing errors. Moreover, the built-in ADC is susceptible to electromagnetic crosstalk from the chip itself and peripheral circuits, resulting in high sampling noise and low signal-to-noise ratio. In addition, the large sampling delay and data update lag not only make it difficult to adapt to the complex electromagnetic interference environment in vehicles, but also cause distortion in signal strength gradient data acquisition, reducing the accuracy of positioning and investigation. This solution utilizes an independent ADC1175 high-speed sampling chip 32, which overcomes the speed and accuracy bottlenecks of built-in ADCs. It achieves distortion-free, full-coverage acquisition of alternating electromagnetic signals across the 100kHz-700kHz frequency band, accurately capturing the subtle spectral characteristics unique to each device. This provides complete, accurate, and high-precision raw data for spectrum database comparison and precise AI weight matching, fundamentally ensuring the accuracy and stability of GPS locator model identification. Simultaneously, the dual-chip architecture achieves layered decoupling of hardware functions. The main control chip 31 is dedicated to high-speed analog-to-digital sampling and signal conversion, while the sampling chip 32 focuses on data processing, spectrum analysis, AI matching, device control, and human-machine interaction. The two chips perform their respective functions without interference, thus decoupling... It solves the problems of single-chip computing power overload, operation lag, and data errors, and greatly improves the stability of device operation. In addition, the independent external sampling chip 32 can effectively avoid electromagnetic crosstalk of the main control circuit, and the sampling signal-to-noise ratio is higher. With the optional low-noise amplifier circuit 20, it can accurately distinguish weak effective signals from vehicle environment power frequency and stray interference signals, improve the weak signal detection capability in complex scenarios, and reduce the probability of missed or false judgments. At the same time, with the extremely low sampling, conversion, and transmission delay of ADC1175, it can capture the dynamic strength changes of spatial electromagnetic signals in real time and output continuous and accurate signal gradient data, effectively improving the sensitivity and accuracy of device positioning and investigation, and realizing blind-spot-free accurate investigation of hidden GPS locators.

[0037] [Compare with Example 1] Detection methods for wired dormant GPS locators mainly fall into four categories: manual physical inspection, radio frequency signal detection, activation / wake-up detection, and nonlinear node detection. All of these methods have significant limitations, making it difficult to achieve stable, accurate, and low-cost static detection. Specifically: The basic approach to manual physical inspection is to leverage the fact that wired GPS devices require power to locate extra wiring or unknown electronic modules by visually inspecting, touching, and disassembling interior components along the vehicle's power lines, fuse boxes, OBD interfaces, under the steering wheel, under the seats, and in the trunk lining. Disadvantages include: high reliance on experience, extremely low efficiency; concealed installations such as those hidden in bumper layers or wiring harnesses are almost impossible to detect; disassembly and reassembly can easily damage the vehicle's interior and wiring; and there is a high rate of misjudgment and missed detections. The basic idea behind radio frequency (RF) signal detection is to use a wideband RF detector to scan communication frequency bands such as 2G / 4G / NB-IoT, capture the RF signals emitted when the locator wakes up to upload data, and locate the source based on the signal strength. The drawback is that it relies entirely on the locator actively emitting signals. With wired sleep GPS, when the vehicle is stationary, the main control and communication modules enter deep sleep mode, emitting no RF signals, and the detector has no signal to capture, inevitably leading to missed detections.

[0038] The basic idea behind activation-wake detection is to trigger a dormant GPS device to exit sleep mode and enter transmission mode through vehicle vibration, movement, or a dedicated activation signal, and then use an RF detector to capture a brief signal window. Disadvantages include: requiring vehicle movement or external activation commands, making static detection impossible; uncontrollable wake-up timing; extremely short window period; low detection efficiency; and unstable success rate.

[0039] The basic idea of ​​nonlinear node detection is to use a nonlinear node detector to emit a fundamental wave signal and detect the second / third harmonics generated by nonlinear components such as diodes and transistors inside the GPS device, thereby identifying hidden electronic devices. Disadvantages include: expensive, bulky, and inconvenient to carry; susceptible to interference from other electronic modules in the vehicle, resulting in a high false alarm rate; and inability to distinguish device types or identify specific models.

[0040] The various traditional GPS detection technologies described in Comparative Example 1 all suffer from core shortcomings, including outdated detection principles, limited detection conditions, and a lack of intelligence. Firstly, traditional detection methods rely on GPS locators actively emitting radio frequency signals or on manual physical inspection, failing to recognize the hardware characteristic of wired dormant GPS locators that have no radio frequency output in their dormant state. This makes detection impossible in critical scenarios such as stationary vehicles or deeply dormant devices, resulting in inherent blind spots and unavoidable missed detections. Secondly, manual inspection methods are inefficient, highly destructive, and extremely dependent on operator experience, exhibiting poor stability and coverage, and lacking the conditions for standardized and widespread use. Thirdly, while nonlinear node detection technology can detect passive electronic devices, it lacks device differentiation capabilities, cannot identify GPS locator models, has weak anti-interference capabilities, high equipment costs, and poor portability. Fourthly, none of the technical solutions in Comparative Example 1 incorporate low-frequency magnetic field acquisition, signal amplification, ADC quantization acquisition, FFT frequency domain analysis, or spectral fingerprint comparison mechanisms, making it impossible to capture and extract features from weak DC-DC background electromagnetic signals, and lacking the technical foundation for static detection and intelligent identification.

[0041] [Compare with Example 2] Currently, electromagnetic field signal testing conducted in the industry for DC-DC buck switching power supply circuits is primarily for testing the power supply's performance and electromagnetic compatibility compliance. It does not cover any applications involving equipment traceability or covert device detection. Specific testing objectives mainly include the following: Electromagnetic Compatibility (EMC) Compliance Testing: DC-DC switching power supplies rely on high-frequency switching oscillations to achieve voltage conversion. During operation, they generate electromagnetic radiation and interference noise in a fixed frequency band, making them a major source of EMC for electronic equipment. Conventional EMC testing primarily follows automotive electronics EMC standards, detecting power supply radiation intensity, harmonic interference, and stray electromagnetic field parameters to identify and address radiation exceeding limits. This ensures that the power module and its associated equipment comply with EMC specifications, preventing interference with the normal operation of the vehicle's original precision electronic control and communication equipment.

[0042] Power supply fault and performance debugging and testing: By collecting electromagnetic field signals in the 100KHz-700KHz operating frequency band of DC-DC power supplies, and analyzing abnormal changes in oscillation frequency, waveform jitter, and harmonic parameters, power supply circuit faults can be quickly located, including abnormal switching transistors, inductor losses, filter circuit failures, and excessive output ripple. This is used for power supply product R&D debugging, factory quality inspection, and after-sales fault repair to ensure the stability and reliability of power supply.

[0043] Equipment anti-interference performance verification: In complex vehicle electromagnetic environments, various electrical devices will generate stray electromagnetic signals. By testing the anti-interference capability and signal stability of the DC-DC power supply's electromagnetic field, the adaptability of the power module under complex working conditions is verified, avoiding power supply voltage regulation failure, equipment power failure, abnormal operation and other faults caused by external electromagnetic interference, and improving the environmental adaptability of the whole equipment.

[0044] Circuit optimization and iterative upgrade: By accurately collecting electromagnetic field fingerprint data of DC-DC power supplies under different operating conditions, the impact of circuit layout, magnetic devices, and filter structure on electromagnetic radiation is analyzed. This provides data support for power supply circuit noise reduction, shielding optimization, and structural iteration, achieving the optimization goal of reducing electromagnetic radiation and improving power supply efficiency.

[0045] The traditional DC-DC electromagnetic field detection technology described in Comparative Example 2 suffers from significant shortcomings, including fixed applications, limited application scenarios, extremely short detection distance, and lack of feature recognition capabilities. Firstly, existing DC-DC electromagnetic detection only serves power supply EMC compliance testing, fault debugging, and circuit optimization, consistently treating DC-DC high-frequency electromagnetic radiation as an interference source and defect indicator, never using it as a detection basis or identification feature for concealed electronic devices, resulting in a simplistic application approach. Secondly, traditional detection is only suitable for close-range contact testing of circuit boards, lacking a dedicated high-frequency magnetic core inductor coupling structure and multi-stage low-noise gain amplification mechanism. It can only collect signals at extremely close range to the circuit under test, with an extremely short detection distance, making it unsuitable for large-scale, multi-point vehicle inspection scenarios and lacking specific features for fault identification. The design is based on the scenario; third, traditional detection only focuses on whether the overall radiation of the power supply exceeds the standard and whether the waveform is abnormal. It does not perform dedicated signal acquisition and noise reduction amplification for the target narrowband frequency band of 100KHz-700KHz, and does not have the ability to acquire weak background signals in a refined manner; fourth, existing technologies do not extract, build databases and compare the spectral characteristics of different power supply circuits in a differentiated manner, and do not use subtle fingerprint differences such as oscillation frequency, harmonic distribution and waveform jitter to achieve equipment classification and identification. They can only evaluate the performance of the power supply, and cannot achieve equipment traceability, model determination and hidden equipment location functions. They are completely unsuitable for the application scenario of illegal GPS detection of vehicles.

[0046] In summary, Comparative Example 1 generally suffers from the fundamental flaw of being unable to detect in static conditions and completely failing in dormant scenarios. Comparative Example 2 suffers from the application flaw of having an extremely short detection distance, being unable to perform range scanning, and being only suitable for laboratory scenarios. Neither of these existing technologies can achieve the detection effect of "no blind spots when the vehicle is stationary, range scanning, and high-precision gradient positioning" achieved by Embodiment 1 of this invention. They also cannot solve the industry pain points of missed detections, inefficiency, and limited scenarios in existing dormant GPS detection.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A method for detecting and identifying a hidden GPS locator, characterized in that, The method for detecting and identifying wired GPS locators includes the following steps: Step 1, select the magnetic field signal to be detected: The wired GPS locator has a DC-DC step-down switching power supply circuit (50), a GPS main control chip and an Internet of Things module. The DC-DC step-down switching power supply circuit (50) radiates a stable electromagnetic field signal of 100KHz-700KHz. Static detection is achieved by detecting the 100KHz-700KHz electromagnetic field signal radiated by the DC-DC step-down switching power supply circuit (50). Step 2, build a dedicated detection hardware system: Based on the electromagnetic field signal selected in Step 1, set up a high-frequency magnetic core inductor (10), an MCU module (30) and a display module (40); wherein, the high-frequency magnetic core inductor (10) is used to couple and pick up the target low-frequency electromagnetic field signal, the MCU module (30) is used to receive the target low-frequency electromagnetic field signal collected by the high-frequency magnetic core inductor (10), and the display module (40) is connected to the MCU module (30) for communication. Step 3, real-time sensing and acquisition of electromagnetic signals: After the detection hardware system is powered on, it uses a high-frequency magnetic core inductor (10) to sense the electromagnetic field signal in the 100KHz-700KHz frequency band in the space at close range. It accurately acquires the 100KHz-700KHz electromagnetic field signal radiated by the DC-DC step-down switching power supply circuit (50). It converts the sensed 100KHz-700KHz electromagnetic field signal into an analog electromagnetic field signal and inputs it into the MCU module (30) for subsequent ADC sampling and processing. Step 4, based on the detected signal strength changes, perform location investigation: The MCU module (30) performs high-speed ADC sampling on the input analog magnetic field signal, converting the continuously changing analog electromagnetic signal into calculable and comparable digital level data; the MCU module (30) analyzes the electromagnetic signal strength corresponding to the digital data in real time, and pushes the signal strength value and intensity change trend to the display module (40) for visualization display in real time; the detection personnel rely on the gradient difference of the signal strength changing with the detection distance and position during the detection process to gradually investigate and lock the precise installation position of the dormant wired GPS locator, and complete the blind-spot-free location investigation in the static scene.

2. The method for detecting and identifying a hidden GPS locator according to claim 1, characterized in that, The MCU module (30) uses an ESP32-P4 chip. The ESP32-P4 chip has an A / D pin, which is used to complete the ADC sampling of the signal.

3. The method for detecting and identifying a hidden GPS locator according to claim 1, characterized in that, The MCU module (30) includes a main control chip (31) and a sampling chip (32). The main control chip (31) is an ESP32-P4 chip, and the sampling chip (32) is an ADC1175 chip. The analog electromagnetic signal collected by the high-frequency magnetic core inductor (10) is converted into digital signal by the sampling chip (32) at high speed, and the continuously changing analog electromagnetic signal is converted into calculable and comparable digital level data and transmitted to the main control chip (31).

4. The method for detecting and identifying a hidden GPS locator according to claim 1, characterized in that, After step 3, the GPS locator model is automatically identified based on electromagnetic spectrum fingerprint: the collected 100KHz-700KHz time domain signal is converted into a frequency domain signal by FFT fast Fourier transform, the unique spectrum fingerprint features of different GPS locators are extracted, and the device model is identified by comparison and matching with the pre-stored spectrum fingerprint database. The device photo and matching similarity are displayed on the display module (40).

5. The method for detecting and identifying a hidden GPS locator according to claim 4, characterized in that, The spectral fingerprint features include the oscillation frequency, harmonic frequency, main wave left-right jitter amplitude, and main wave up-down jitter amplitude of the DC-DC step-down switching power supply circuit (50).

6. The method for detecting and identifying a hidden GPS locator according to claim 4, characterized in that, The pre-stored spectrum fingerprint database includes pre-collected spectrum fingerprint data of GPS trackers in the 100KHz-700KHz frequency band. The spectrum fingerprint features, physical photos of the devices, specific models, and parameter information of various GPS trackers are classified, associated, and stored to complete the database construction. When the device is working, the built-in AI weight matching algorithm performs multi-dimensional weight comparison between the real-time collected spectrum fingerprints and the sample fingerprints in the database, automatically calculates the matching degree value, selects the best matching result, and realizes automatic identification of GPS tracker models.

7. The method for detecting and identifying a hidden GPS locator according to claim 1, characterized in that, The detection hardware system is also equipped with a low-noise amplifier circuit (20). The low-noise amplifier circuit (20) is connected between the high-frequency magnetic core inductor (10) and the MCU module (30). The low-noise amplifier circuit (20) is used to amplify the gain and filter the 100KHz-700KHz electromagnetic field signal collected by the high-frequency magnetic core inductor (10). After eliminating vehicle power frequency interference and stray electromagnetic interference, the clean and effective analog electromagnetic signal is input into the MCU module (30).

8. The method for detecting and identifying a hidden GPS locator according to claim 7, characterized in that, The low-noise amplifier circuit (20) amplifies the analog electromagnetic field signal converted by the high-frequency magnetic core inductor (10) by 100 to 1000 times.

Images