Vehicle-mounted video interference suppression method and system based on EMC (Electro Magnetic Compatibility) optimization

Abstract

The invention relates to the technical field of vehicle-mounted electronics, in particular to a vehicle-mounted video interference suppression method and system based on EMC optimization, and the method comprises the steps: building a transmission reference model; comparing the model with a composite signal on a transmission link in real time, extracting a holographic electromagnetic wave interference component through holographic differential processing, analyzing an amplitude-phase transfer function, and generating a reverse holographic electromagnetic wave through phase conjugate inversion; reverse holographic electromagnetic waves are coupled and injected into a link through a distributed holographic modulation array, and coherent cancellation is carried out on the reverse holographic electromagnetic waves and original interference; residual interference is monitored at the downstream of the link, and the offset effect is dynamically optimized through an adaptive weight updating algorithm. According to the invention, channel noise in a transmission link is eliminated from the source through active cancellation, the technical problem that a video signal is submerged by noise before reaching a receiving end in a strong electromagnetic interference environment is solved, and the suppression effect under complex and changeable working conditions is ensured through a self-adaptive closed-loop system. And the transmission reliability and fidelity of the vehicle-mounted video signal are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics technology, specifically to an automotive video interference suppression method and system based on EMC optimization. Background Technology

[0002] In modern automotive electronic systems, reliable signal transmission between different subsystems is required to achieve various advanced functions. These transmissions typically involve high-frequency baseband signals and rely on specific physical transmission media, such as twisted-pair cables or coaxial cables. However, inside vehicles, especially in new energy vehicles, there is significant electromagnetic interference generated by inverters and switching power supplies. This interference couples into the transmission path through radiation or conduction, creating severe channel noise and drastically reducing the signal-to-noise ratio at the receiving end. Existing technologies primarily address this challenge by processing signals after the receiver or by adding general-purpose filtering devices to the transmission lines.

[0003] However, existing technologies for addressing this problem generally suffer from a core technical deficiency: a lack of systematic design for electromagnetic interference suppression of the entire transmission system—including the transmitting circuit, transmission medium, and receiving circuit—as a whole. Existing solutions fail to proactively guarantee the interference immunity of the transmission medium itself. For example, measures relying on the receiver for signal recovery will completely fail when channel noise is excessive and the useful signal energy is submerged by noise, because the receiver front-end cannot reliably demodulate the original signal from the noise. Furthermore, simply adding general filtering or shielding measures in isolation along the transmission line often fails to effectively prevent interference coupling because it does not systematically consider the impedance characteristics, common-mode rejection, and grounding strategies of the entire transmission path, leaving the channel itself vulnerable and unreliable.

[0004] Therefore, this invention proposes an in-vehicle video interference suppression method and system based on EMC optimization. Summary of the Invention

[0005] The purpose of this invention is to provide an EMC-optimized method and system for suppressing video interference in vehicles. By extracting interference signals in real time in the signal transmission link and generating an inverse signal with opposite phase to actively cancel them out, and by combining a closed-loop adaptive algorithm to continuously optimize the suppression effect, this invention solves the problem mentioned in the background that the prior art lacks systematic active protection for the transmission medium, resulting in the signal being submerged by noise before reaching the receiving end in a strong electromagnetic interference environment and thus unable to be effectively recovered.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An EMC-optimized method for suppressing in-vehicle video interference includes: During in-vehicle video transmission, a transmission reference model for the video signal is established; the transmission reference model is compared in real time with the composite signal carrying the transmission link, which includes the ideal video signal and interference; through holographic differential processing, the holographic electromagnetic wave interference component of the coupling interference is extracted, and the amplitude-phase transfer function of the interference component on the transmission medium is analyzed. Based on the amplitude-phase transfer function, a distributed holographic modulation array deployed along the transmission link is driven by phase conjugate inversion to generate inverse holographic electromagnetic waves in real time. The inverse holographic electromagnetic wave is used as an active cancellation signal and is coupled into the transmission link through the distributed holographic modulation array, so that the inverse holographic electromagnetic wave and the holographic electromagnetic wave interference component are coherently superimposed and canceled on the transmission link; At the downstream residual interference monitoring point of the transmission link, the residual interference components after cancellation are iteratively extracted, and based on the residual interference components, the generation of the inverse holographic electromagnetic wave is dynamically optimized through an adaptive weight update algorithm to form an adaptive closed loop for active EMC protection of the video transmission signal.

[0007] Preferably, the transmission reference model for the video signal specifically includes: obtaining the S-parameters of the complete transmission link through a vector network analyzer and converting them into a digital transfer function with linear time-invariant characteristics of the link; performing a convolution operation between the video source signal and the transfer function to generate an ideal signal waveform that pre-incorporates the inherent signal attenuation, phase shift, and dispersion effects of the physical medium.

[0008] Preferably, the holographic differential processing specifically includes: using digital cross-correlation technology to sample and synchronize the composite signal and the reference model; after synchronization, the processing unit performs binary subtraction on each sampling point, and the difference is the holographic electromagnetic wave interference component that fully preserves the temporal characteristics of the interference transient and phase noise.

[0009] Preferably, the analysis of the amplitude-phase transfer function of the interference component on the transmission medium specifically includes: dividing the time-domain interference signal into multiple overlapping analysis frames, applying a window function to each frame to smooth the boundaries and suppress spectral leakage; performing a fast Fourier transform on the windowed data frames, and calculating the amplitude spectrum representing the energy of each frequency component and the phase spectrum representing the phase from the complex results obtained by the transform. The amplitude spectrum and the phase spectrum together constitute the amplitude-phase transfer function of the interference within the time window.

[0010] Preferably, the phase conjugate inversion specifically includes: performing a conjugate operation on the complex frequency point values ​​of the amplitude-phase transfer function, wherein the conjugate operation keeps the amplitude of each frequency component unchanged, and reverses the phase by 180 degrees to generate a target spectrum with the same energy distribution as the interference but opposite phase; performing an inverse fast Fourier transform on the target spectrum and resynthesizing it into a real-valued time-domain control waveform, wherein the time-domain control waveform is a digital precursor of an inverse holographic electromagnetic wave.

[0011] Preferably, the distributed holographic modulation array specifically includes: a time-domain control waveform being distributed to multiple injection nodes arranged at predetermined intervals along the cable path; within each node, an independent digital-to-analog converter and a variable gain amplifier convert the digital waveform into an adjustable analog signal; and the gain of each node being independently controlled by an adaptive weight update processor, so that the array forms a spatially weighted non-uniform cancellation field to cancel distributed interference that penetrates at multiple points along the cable.

[0012] Preferably, the coupling injection specifically includes: the coupling injection is a non-contact energy injection method based on near-field magnetic coupling, and the injection node includes a clamping induction structure made of ferrite core; when the control waveform drives the clamping induction structure, the excited controlled alternating magnetic field penetrates the cable insulation layer in a non-invasive manner, and induces a cancellation signal with the opposite phase to the original interference on the cable conductor according to the law of electromagnetic induction.

[0013] Preferably, the monitoring method for the downstream residual interference monitoring point specifically includes: performing high-impedance sensing on the transmission link, and physically located after the last injection node of the distributed holographic modulation array, to pick up the analog residual signal; and performing analog-to-digital conversion to generate a digital residual interference signal.

[0014] Preferably, the adaptive weight update algorithm specifically includes: the adaptive weight update algorithm is a normalized least mean square algorithm that is iteratively optimized in the digital domain; the digital residual interference signal is used as the real-time error input, and the holographic electromagnetic wave interference component is used as the reference input; based on the error input and the reference input, the internal weight vector is continuously iteratively adjusted, and the weight vector defines the independent gain of each injection node in the distributed holographic modulation array, and the spatial weighted amplitude of the inverse holographic electromagnetic wave injected into the transmission link is dynamically corrected.

[0015] An EMC-optimized in-vehicle video interference suppression system includes: Transmission reference model storage module: Used to store the transmission reference model of video signals; Holographic differential processing module: connected to the transmission reference model memory and transmission link, used to compare the composite signal carried by the transmission reference model and the transmission link in real time, and extract the holographic electromagnetic wave interference component; Transfer function analysis module: connected to the holographic differential processor, analyzes the amplitude-phase transfer function of the holographic electromagnetic wave interference component on the transmission medium; Phase conjugate inversion processing module: connected to the transfer function analysis unit, performs phase conjugate inversion based on the amplitude-phase transfer function, and generates control parameters for the inverse holographic electromagnetic wave; Distributed holographic modulation array module: Deployed along the transmission link and connected to the phase conjugate inversion processor, generating inverse holographic electromagnetic waves according to the control parameters and coupling them into the transmission link; Residual interference monitoring module: Located downstream of the transmission link, it extracts the canceled residual interference components; Adaptive weight update processing module: connected to the residual interference monitoring unit and the distributed holographic modulation array module, runs the adaptive weight update algorithm, and dynamically optimizes the control parameters of the inverse holographic electromagnetic wave.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves precise and real-time coherent cancellation of dynamic electromagnetic interference coupled into the transmission link without altering the original video signal. It employs a method that precisely establishes a video signal transmission reference model incorporating the S-parameters of the transmission link, capable of predicting signal attenuation and phase shift; utilizes synchronous holographic differential processing based on digital cross-correlation technology to compare and extract clean interference components that retain complete time-domain characteristics in real time; and analyzes the amplitude-phase transfer function of the interference based on Fast Fourier Transform and performs phase conjugate inversion to generate inversely canceled waveforms with precisely opposite phases. This method eliminates channel noise at its source, completely solving the problem in traditional technologies where low signal-to-noise ratios prevent signal recovery at the receiver, ensuring high fidelity of the video signal during physical transmission.

[0017] 2. This invention utilizes a distributed holographic modulation array consisting of multiple independent injection nodes deployed at predetermined intervals along the video cable path; high-impedance sensing monitoring points downstream of the array's injection range to non-destructively pick up and digitize residual weak interference signals; and a high-speed closed-loop feedback processing method employing a normalized minimum mean square adaptive weight update algorithm with residual interference as real-time error input. This achieves efficient and robust dynamic suppression of complex electromagnetic interference that penetrates multiple points along the cable and whose spectrum and intensity continuously change. This system-level design ensures that protection is no longer limited to a single node, but forms an intelligent, self-optimizing "active shielding" protective zone, greatly enhancing adaptability and protection against strong interference sources such as inverters inside new energy vehicles.

[0018] 3. This invention achieves efficient and flexible system deployment and integration without cutting or altering the physical structure and core electrical characteristics of the original video transmission cable. It employs a clamping induction structure based on a ferrite core for non-contact near-field magnetic coupling energy injection, digitally distributes the time-domain control waveform generated by the central processing unit to each injection node, and precisely converts digital commands into analog cancellation fields within each node using independent digital-to-analog converters and variable-gain amplifiers. This non-intrusive approach significantly simplifies engineering installation and maintenance, fundamentally avoiding secondary problems such as signal reflection and impedance mismatch that may be introduced by adding equipment, ensuring the high reliability and stability of the entire system. Attached Figure Description

[0019] Figure 1 This is a flowchart of a vehicle video interference suppression method based on EMC optimization proposed in this invention application; Figure 2 This is a schematic diagram of the structure of an EMC-optimized vehicle video interference suppression system proposed in this invention application; Figure 3 This is a flowchart of an adaptive weight update algorithm proposed in this invention application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0021] This invention provides an EMC-optimized method for suppressing in-vehicle video interference, the technical solution of which is as follows: An EMC-optimized method for suppressing in-vehicle video interference, the specific implementation steps of which include: S1. During the in-vehicle video transmission process, a transmission reference model for the video signal is first established. The transmission reference model is compared in real time with the composite signal actually carried by the transmission link. Through holographic differential processing, the holographic electromagnetic wave interference component of the coupling interference is extracted from the composite signal, and then the amplitude-phase transfer function of the interference component on the transmission medium is analyzed.

[0022] S2. Based on the amplitude-phase transfer function obtained by analysis, the phase conjugate inversion technique is used to process it, and the result is used to drive the distributed holographic modulation array deployed along the transmission link, so that it can generate inverse holographic electromagnetic waves with the opposite characteristics to the interference signal in real time.

[0023] S3. The generated inverse holographic electromagnetic wave is used as an active cancellation signal and coupled into the video signal transmission link through a distributed holographic modulation array. In this link, the inverse holographic electromagnetic wave and the original holographic electromagnetic wave interference components are coherently superimposed, thereby achieving a phase-opposite cancellation effect and suppressing interference.

[0024] S4. Set up residual interference monitoring points downstream of the transmission link to iteratively extract the residual interference components that still exist after cancellation. Then, based on these residual interference components, dynamically optimize the generation process of the inverse holographic electromagnetic wave through an adaptive weight update algorithm to form an adaptive closed-loop control system, thereby providing continuous and efficient EMC active protection for the video transmission signal.

[0025] Example 1: This embodiment provides a specific application of an EMC-optimized in-vehicle video interference suppression method, referencing... Figure 1 .

[0026] Furthermore, during the in-vehicle video transmission process, a transmission reference model for the video signal is first established. The transmission reference model is compared in real-time with the composite signal actually carried by the transmission link. Through holographic differential processing, the holographic electromagnetic wave interference component of the coupling interference is extracted from the composite signal. Then, the amplitude-phase transfer function of the interference component on the transmission medium is analyzed. Corresponding to step S1 above, the specific process is as follows: To establish a transmission reference model, a comprehensive frequency sweep measurement was performed on the complete physical transmission link from the vehicle-mounted camera to the display unit, including all connectors and wiring harnesses, using a vector network analyzer. The measurement covered a wide frequency band from 1MHz to 500MHz, acquiring the link's scattering parameters, primarily transmission and reflection characteristics. To establish the FIR model, the frequency domain transfer function represented by the S21 parameters was utilized. First, the complex frequency domain data of the transmission characteristics (including amplitude and phase) was processed using an inverse fast Fourier transform combined with a window function to obtain an estimate of the link's approximate time-domain impulse response h(t). Subsequently, this estimated impulse response h(t) or its corresponding frequency response was used as the design target, and a high-order finite impulse response digital filter model was calculated using the Parks-McClellan algorithm. The coefficients of this filter constitute the digital transfer function, accurately characterizing the link's inherent linear time-invariant characteristics such as signal attenuation, phase delay, and frequency dispersion. The FIR filter was set to order 512 to accurately reproduce the amplitude-frequency response and group delay variations of the link throughout the entire operating frequency band, ensuring high fidelity of the model. When the vehicle is running, the clean digital signal stream from the video source is convolved with the coefficients of the digital filter in real time to generate an ideal signal waveform that is accurately simulated.

[0027] By establishing a transmission reference model that accurately incorporates the link's S-parameters, we can accurately predict the inherent signal attenuation and distortion of the physical medium, providing a high-fidelity ideal signal reference for subsequent differential processing.

[0028] Holographic differential processing is performed, sampling and digitizing the actual composite signal on the same physical transmission link at high resolution using a 12-bit, 1GSPS analog-to-digital converter. High-speed digital cross-correlation is then performed to calculate the precise time delay between the reference model signal and the digitized composite signal. By finding the peak of the correlation function and using a parabolic interpolation algorithm, sub-sampling-point level synchronization accuracy is achieved, yielding a precise fractional delay value. Based on this precise delay A fractional-order delay filter (e.g., a filter based on a Farrow structure or Lagrange interpolation) is applied in real time to the reference model signal stream to generate a reference waveform that is precisely aligned in time with the digitized composite signal stream. Subsequently, the processing unit performs a binary subtraction operation on each pair of synchronized sampling points. The ideal signal components are precisely canceled out in the operation, and the final difference is the holographic electromagnetic interference component that has been completely separated, preserving all transient response and phase noise details.

[0029] By performing holographic differential processing after synchronization, the pure interference signal can be extracted without loss and completely, ensuring that the subsequently generated cancellation signal is highly matched with the real interference in the time domain. This is a prerequisite for achieving high-precision cancellation.

[0030] A holographic electromagnetic interference extraction method based on a reference model is proposed. By constructing a digital twin model of the physical link and performing high-precision synchronization and differential operations with the real-time transmitted composite signal, the pure interference signal can be extracted without loss and completely.

[0031] The transfer function H(s) is obtained by measuring the linear time-invariant characteristics of the channel. In real-time operation, the known, interference-free source signal s(t) is convolved with the impulse response h(t) of the channel (the time-domain representation of H(s)) to generate the interference-free expected received signal. Meanwhile, the composite signal actually transmitted in the physical channel, containing interference n(t), is... High-speed sampling is performed. High-precision time synchronization technology is used to ensure… and The digital samples are precisely aligned on the time axis. Finally, subtraction is performed point-by-point in the time domain: This allows for the direct separation of the complete interference signal n(t) without any filtering or transformation damage.

[0032] Interference is extracted non-invasively without altering the original signal path or content, avoiding the phase distortion or amplitude attenuation that traditional bandpass or bandstop filters may cause to the useful signal. The extracted interference component is holographic, meaning it retains all the time-domain characteristics of the original interference signal, including aperiodic transient impulses and complex phase modulation information, providing an ideal, distortion-free template for subsequently generating a high-fidelity inverse cancellation signal.

[0033] Furthermore, based on the amplitude-phase transfer function obtained analytically, it is processed using phase conjugate inversion technology, and this result drives a distributed holographic modulation array deployed along the transmission link, enabling it to generate inverse holographic electromagnetic waves with characteristics opposite to those of the interference signal in real time. Corresponding to step S2 above, the specific process is as follows: The interference component transfer function is analyzed by buffering and segmenting the extracted time-domain interference signal stream into a series of continuous and overlapping data frames. Each frame contains 1024 sampling points, and a 50% overlap rate is set between frames. This overlap ensures that any transient events in the signal are completely captured in the central region of the analysis window without being fragmented. To suppress spectral leakage caused by frame truncation, a Blackman-Harris window function is applied to each data frame for smoothing. The window function provides more than 90 dB of sidelobe suppression, which is crucial for resolving harmonic components with significant differences in strength. A Fast Fourier Transform is performed on each windowed data frame to convert it from a time-domain signal to a frequency-domain complex spectrum. From this complex spectrum, the amplitude and phase spectra at each discrete frequency point are calculated, which together constitute the accurate amplitude-phase transfer function of the interference signal.

[0034] By performing transfer function analysis on the interference using windowed FFT, the frequency domain characteristics of complex interference signals were accurately quantified, providing clear target parameters for subsequent phase inversion.

[0035] To perform phase conjugate inversion, every complex frequency value in the transfer function is iterated. For each complex value of the form a + bi, a conjugate operation is performed to obtain its conjugate value a - bi, keeping the real part unchanged and inverting the imaginary part. This ensures that the amplitude of the frequency components remains unchanged, while their phases are precisely reversed by 180 degrees. After the conjugate operation is completed for all frequency points, a target spectrum with the same energy distribution as the original interference, but with all frequency components having precisely opposite phases, is generated. An inverse fast Fourier transform is then performed on this target spectrum to reassemble it from the frequency domain into a real-valued time-domain control waveform.

[0036] Through phase conjugate inversion and inverse transformation, a digital cancellation waveform that perfectly matches the interference in terms of energy and frequency composition but is precisely opposite in phase can be efficiently generated, providing an ideal signal source for achieving coherent cancellation.

[0037] Furthermore, the generated inverse holographic electromagnetic wave is used as an active cancellation signal and coupled into the video signal transmission link through a distributed holographic modulation array. In this link, the inverse holographic electromagnetic wave coherently superimposes with the original holographic electromagnetic wave interference component, thereby achieving a phase-opposite cancellation effect and suppressing interference. Corresponding to step S3 above, the specific process is as follows: To achieve distributed signal generation, the generated time-domain control waveform is digitally distributed to multiple injection nodes deployed along the cable path. At each individual injection node, the digital waveform is converted into a high-fidelity analog voltage signal by a 14-bit, 1GSPS digital-to-analog converter, accurately reproducing the complex shape of the waveform. The amplitude of this analog signal is precisely modulated in real time according to control parameters provided by an adaptive weight update process. In this way, each node can generate an analog cancellation signal with precisely adjustable amplitude, enabling the entire array to form a spatially weighted non-uniform cancellation field.

[0038] By deploying a distributed holographic modulation array with independently controllable gain, a spatially weighted non-uniform cancellation field was constructed, which can effectively cope with complex distributed interference of varying intensity at multiple points along the cable.

[0039] To achieve non-contact coupling injection, an amplitude-modulated analog signal is used to drive a clamping induction structure. At the core of this structure is an openable magnetic core made of manganese-zinc ferrite. During installation, this core is conveniently clamped to the outside of the video transmission cable without altering the cable's insulation. When the control waveform drives this induction structure, a controlled and rapidly changing alternating magnetic field is generated according to Ampere's law. This alternating magnetic field penetrates the cable's insulation shielding layer in a non-invasive near-field coupling manner and, according to Faraday's law of electromagnetic induction, directly induces a canceling signal current in the cable's internal signal conductor that is precisely out of phase with the original interference.

[0040] By using a non-contact injection method based on near-field magnetic coupling, signal injection can be performed without damaging the original cable structure and impedance characteristics, greatly simplifying engineering installation and ensuring signal integrity after system integration.

[0041] A distributed, spatially weighted active cancellation field construction method is proposed, which deploys multiple independently controllable injection nodes along the transmission medium path to form a non-uniform reverse electromagnetic field that can accurately match the spatial distribution of infiltrating interference sources along the way.

[0042] To address distributed interference intrusion along transmission cables, this method deploys a linear array of N independently controllable injection points in space. The core principle is that the output signal amplitude of each injection point is determined by an independent weighting coefficient. (i=1, 2, ..., N) control. This set of weighting coefficients constitutes a weighting vector W. By adjusting the values ​​of each element in vector W in real time, the spatial amplitude distribution of the final inverse cancellation field formed after the cancellation signals generated at each injection point are coherently superimposed on the cable is variable. This method can dynamically shape a non-uniform cancellation field profile that matches the actual energy distribution of the interference along the cable, accurately concentrating the cancellation energy in the physical section where the interference intrusion is most severe. This method achieves targeted spatial suppression of interference, rather than traditional global, uniform suppression. It can effectively address the problem of unevenly distributed interference intrusion along the cable caused by multiple interference sources or complex coupling paths in the vehicle environment, significantly improving energy utilization efficiency and the depth of interference suppression in local sections. The spatial adaptive capability of this method greatly enhances its robustness in complex, real electromagnetic environments.

[0043] Furthermore, residual interference monitoring points are set up downstream of the transmission link to iteratively extract the residual interference components that still exist after cancellation. Then, based on these residual interference components, the generation process of the inverse holographic electromagnetic wave is dynamically optimized through an adaptive weight update algorithm, forming an adaptive closed-loop control system. This provides continuous and efficient active EMC protection for the video transmission signal. Corresponding to step S4 above, the specific process is as follows: To perform real-time monitoring of residual interference, a high-impedance sensing operation is performed at the very end of the distributed holographic modulation array's range. This operation uses a sensing probe with an input impedance greater than 1 megohm and an input capacitance less than 1 picofarad to pick up the weak analog residual signal remaining on the cable after coherent cancellation, using a purely bypass listening method. This high-impedance characteristic ensures that the monitoring itself does not cause any secondary damage to the integrity of the high-speed video signal. The picked-up analog residual signal is amplified by 20dB through a low-noise preamplifier to improve the signal-to-noise ratio, and then sampled and digitized at high resolution to generate a digital residual interference signal stream.

[0044] By deploying high-impedance sensing monitoring points downstream, a non-destructive, real-time assessment of the cancellation effect was achieved, providing accurate error feedback signals for adaptive closed-loop control.

[0045] To achieve adaptive weight updates, the digitized residual interference signal stream is used as the real-time error input, and an iterative optimization normalized minimum mean square algorithm is executed, aiming to minimize the mean square value of the residual interference signal. In each iteration cycle, the algorithm calculates an optimal update based on the current error signal (i.e., the digitized residual interference signal stream) and the corresponding reference signal (i.e., the holographic electromagnetic interference component), and uses this update to adjust a weight vector. This updated weight vector is distributed to each injection node of the distributed holographic modulation array, directly controlling the variable gain amplifier at each node to finely adjust the analog signal amplitude. This "monitor-calculate-correct" process cycles continuously at a very high rate, forming a stable and efficient adaptive closed-loop control.

[0046] By employing an adaptive weight update algorithm that uses residual interference as error input, the system possesses self-optimization and dynamic tracking capabilities, enabling it to continuously adapt to changing electromagnetic environments and maintain optimal interference suppression performance.

[0047] An adaptive closed-loop EMC protection method based on residual error is proposed. The residual interference after active cancellation is used as the real-time error signal of the system. Through iterative optimization algorithm, the generation parameters of the reverse cancellation waveform are continuously and dynamically corrected, which has the ability to learn and adapt to the environment.

[0048] A classic feedback control closed loop is constructed, where a sensor located downstream of the cancellation region continuously monitors the residual interference signal and uses it as the real-time error signal e(t). This error signal is fed back to the adaptive algorithm. A normalized minimum mean square stochastic gradient descent method is employed to minimize the mean square value E[e²(t)] of the error signal e(t). In each iteration cycle, the algorithm uses the current error signal e(t) and the reference signal to calculate the gradient estimate of the cost function with respect to the weight vector W. Then, the algorithm makes small adjustments to the weight vector W along the negative gradient direction. This continuous iterative process, driven by minimizing the output error, allows the spatial distribution and intensity of the cancellation field to be automatically and continuously optimized.

[0049] This method endows the entire interference suppression scheme with adaptive capabilities to time-varying and non-stationary interference environments. It does not require prior knowledge of the precise characteristics of all potential interference sources; instead, it autonomously tracks the dynamic changes in frequency, amplitude, and phase of interference signals through real-time online learning and optimization. This ensures that a high level of interference suppression performance is maintained under different vehicle operating conditions (acceleration, deceleration, load changes), achieving long-term, robust active EMC protection.

[0050] By establishing a reference model for differential processing and combining it with phase conjugate inversion technology, a fundamental shift from passive defense to active elimination is achieved. This method can accurately generate inverse cancellation signals "tailor-made" for real-time interference, eliminating channel noise at its source and solving the core technical problem of useful signals being submerged before reaching the receiver under strong interference. At the same time, this method constructs an intelligent closed-loop feedback system through downstream monitoring and adaptive algorithms, enabling it to dynamically track and self-optimize, continuously adapting to the ever-changing electromagnetic environment of vehicles under various complex operating conditions. Thus, it provides an unprecedented active EMC protection solution for vehicle video transmission that combines high precision, strong robustness, and high adaptability.

[0051] Example 2: This embodiment provides a specific application of an EMC-optimized in-vehicle video interference suppression system in a high-definition surround-view imaging system for a pure electric vehicle. (Refer to...) Figure 2 .

[0052] The application of this system stems from specific challenges. In this pure electric vehicle, the drive motor inverter generates strong broadband electromagnetic interference during vehicle acceleration and regenerative braking, with frequencies ranging from hundreds of kHz to tens of MHz. This interference couples through a radiation path to the LVDS video transmission cable connecting the camera below the right rearview mirror to the central domain controller. This interference causes noticeable ripples, snow-like noise, and even brief image tearing in the side view image displayed on the driver's monitor. The interference is most severe under rapid acceleration conditions, directly affecting the reliability and safety of advanced driver assistance functions such as lane departure warning and automatic parking.

[0053] To address this issue, the interference suppression system of this invention is deeply integrated into the vehicle. The transmission reference model storage module, holographic differential processing module, transfer function analysis module, phase conjugate inversion processing module, and adaptive weight update processing module are all integrated into a single ECU located near the central domain controller. The distributed holographic modulation array module consists of five physically separate clamped injection nodes, deployed non-equally spaced along the LVDS cable from near the inverter to before the firewall entering the domain controller. The residual interference monitoring module is installed downstream of the last injection node, adjacent to the domain controller's input port.

[0054] The system's workflow begins upon vehicle startup. The ECU's transmission reference model storage module first loads the digital transfer function (DJF) for the specific camera-to-domain controller link, pre-measured and fixed during production line calibration. Once the camera starts transmitting a high-definition video stream, the ECU's holographic differential processing module receives the interference-contaminated composite video signal from the domain controller input in real time. This signal is then synchronized and differentially processed with the ideal reference model generated through the DJF calculation, continuously extracting clean inverter interference signal samples. The extracted interference signal is immediately sent to the transfer function analysis module and the phase conjugate inversion processing module. These two modules collaboratively analyze and synthesize the signal within microseconds, generating a digital control waveform with a phase precisely opposite to the interference signal. This waveform is then sent to the distributed holographic modulation array module, where five injection nodes generate a controlled reverse magnetic field based on this waveform and inject it non-contactly into the LVDS cable, coherently canceling the original interference. During vehicle operation, the entire system enters an adaptive closed-loop operation state. The residual interference monitoring module continuously detects any remaining weak interference on the cable after cancellation. When the driver suddenly depresses the accelerator pedal, the inverter load increases dramatically, causing a momentary change in its interference spectrum and amplitude, resulting in increased residual interference. The residual interference monitoring module immediately detects this change and sends the error signal to the adaptive weight update processing module. Upon detecting the abrupt change in error, the normalized least mean square algorithm running in this module quickly adjusts its internal weights and instructs the phase conjugate inversion processing module to generate a corrected control waveform that better matches the characteristics of the new interference. The entire adaptive adjustment process is completed within milliseconds.

[0055] The system's application effect is remarkable. Through the above workflow, regardless of whether the vehicle is traveling at a constant speed, accelerating slowly, or decelerating sharply, the surround-view image from the right-side camera seen by the driver on the central control screen remains clear and stable, without any visible noise or distortion. The system's active and adaptive interference suppression capability fundamentally ensures the integrity of the video signal at the physical layer, providing high-quality and reliable visual data input for subsequent ADAS functions. Example

[0056] This embodiment provides a detailed explanation of parameter selection in signal processing algorithms.

[0057] When establishing a transmission reference model, the measurement frequency range of the S-parameters must fully cover the bandwidth of the video signal and all potentially strong interference frequencies. For in-vehicle high-definition video, this range is typically set from 1MHz to 500MHz to ensure that high-order harmonic interference generated by inverters, switching power supplies, etc., can be captured. When converting the S-parameters to a digital transfer function, the order of the FIR filter used must be high enough to accurately fit the complex frequency response details of the link, including subtle amplitude fluctuations and group delay variations. Using filters of order 256 to 1024 can achieve amplitude fitting errors of less than 0.1dB and picosecond-level phase fitting accuracy over a wide bandwidth, which is crucial for generating a high-fidelity reference model.

[0058] In FFT analysis, the length of the data frame directly affects the frequency resolution and processing latency, which is a core performance trade-off. This embodiment uses 1024 sampling points as one frame. This choice ensures sufficient frequency resolution (up to tens of kHz at typical sampling rates) to clearly identify the harmonics of the inverter's switching frequency while keeping the single-frame processing latency at the microsecond level, meeting the stringent real-time response requirements of automotive applications. The specific type of window function is selected based on the interference characteristics. For periodic interference with complex spectral structures containing multiple harmonics of varying amplitudes, a Blackman-Harris window with sidelobe suppression exceeding 90dB is selected. This choice effectively prevents the spectral leakage energy of strong harmonic components from overwhelming weaker adjacent harmonic components, thereby ensuring the system can accurately profile the entire interference spectrum with a high dynamic range.

[0059] Example 4: This embodiment provides a detailed explanation of the adaptive weight update algorithm. (Refer to...) Figure 3 The adaptive weight update process is shown.

[0060] This algorithm not only utilizes the current residual error signal, but also continuously calculates the short-term and long-term average power of the error signal in parallel using two moving average filters with different time constants. The short-term average power reflects the interference intensity over the last few tens of microseconds, while the long-term average power represents the relatively stable interference baseline over the past few seconds.

[0061] The algorithm's adaptive mechanism is triggered by a specific logic. When the system detects that the ratio of short-term average power to long-term average power exceeds a preset threshold, it determines that a drastic change has occurred in the external electromagnetic environment, such as a driver suddenly accelerating rapidly. At this time, the algorithm automatically switches the NLMS step size factor to a larger preset value. This larger step size factor sacrifices some steady-state accuracy but gains extremely fast convergence speed, allowing the system's weight vector to quickly adjust to the vicinity of the new optimal solution within a few iterations, thus rapidly adapting to the new strong interference environment. When the error power remains stable at a low level, i.e., the aforementioned power ratio falls below the threshold, the algorithm gradually and smoothly reduces the step size factor. Although a smaller step size factor converges more slowly, it can significantly reduce the system's steady-state mean square error, thereby achieving more refined and thorough suppression of residual interference. This mechanism of dynamically adjusting the step size factor enables the system to exhibit strong robustness and adaptability when facing sudden operating conditions such as a surge in inverter harmonics during vehicle acceleration and deceleration.

[0062] Example 5: This embodiment provides a detailed description of the physical layout and collaborative working mechanism of the distributed holographic modulation array.

[0063] The spatial layout of the injection nodes in the array is not a simple equidistant arrangement, but rather an optimized design based on empirical data from electromagnetic simulation analysis and EMC pre-testing of specific vehicle models. In wiring harnesses near known strong interference sources, such as drive motor inverters or high-voltage DC converters, the deployment density of injection nodes increases significantly, with the physical spacing potentially reduced to 15 cm. This dense deployment aims to concentrate and cancel energy, creating a high-intensity coherent destructive field in the area. In wiring harness sections far from major interference sources and in relatively mild electromagnetic environments, the deployment spacing can be appropriately increased to 45 cm to save costs and reduce system complexity.

[0064] At the collaborative working level, the adaptive weight update processor, in each iteration cycle, does not simply adjust a total gain, but optimizes a weight vector with a dimension equal to the number of injected nodes. Each element in the vector This corresponds to the gain coefficient of the i-th node. This means that the system's adaptive process is not only an adjustment in the time dimension but also a reshaping of the spatial dimension. When the main coupling path of the interference changes due to a slight shift in the physical position of the harness relative to the interference source, the algorithm can automatically reduce the weight coefficients of nodes near the original path and increase the weight coefficients of nodes near the new path by analyzing the changing trend of the residual error. This process achieves a dynamic weight allocation of the cancellation energy in the physical space, enabling the cancellation field to accurately and adaptively focus on the main interference injection point. This mechanism ensures that the system can not only cope with changes in the interference signal over time but also effectively compensate for the dynamic changes in the spatial distribution of the interference, thereby achieving precise targeted suppression of the interference source.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for in-vehicle video interference suppression based on EMC optimization, characterized in that, The application relates to a video signal transmission reference model and a method for active EMC protection of a video signal transmission. In the process of vehicle video transmission, a transmission reference model of a video signal is established; Real-time comparison is made between the transmission reference model and a composite signal containing an ideal video signal and interference borne by a transmission link, a holographic electromagnetic interference component of coupled interference is extracted through holographic differential processing, and an amplitude-phase transfer function of the interference component on the transmission medium is analyzed; Based on the amplitude-phase transfer function, a distributed holographic modulation array arranged along the transmission link is driven through phase conjugate inversion to generate reverse holographic electromagnetic waves in real time; The reverse holographic electromagnetic waves are coupled into the transmission link as active cancellation signals through the distributed holographic modulation array, so that the reverse holographic electromagnetic waves and the holographic electromagnetic interference component are coherently superimposed and cancelled on the transmission link; Residual interference components after cancellation are iteratively extracted at a residual interference monitoring point downstream of the transmission link, and the generation of the reverse holographic electromagnetic waves is dynamically optimized through an adaptive weight updating algorithm based on the residual interference components, so that an adaptive closed loop is formed to actively protect the video transmission signal from EMC.

2. The EMC-optimized in-vehicle video interference suppression method according to claim 1, characterized in that, The transmission reference model of the video signal specifically comprises: S parameters of a complete transmission link are acquired through a vector network analyzer and are converted into a digital transfer function of linear time-invariant characteristics of the link; a video source signal is convoluted with the transfer function to generate an ideal signal waveform in which signal attenuation, phase shift and dispersion effects inherent to a physical medium are taken into account in advance.

3. The EMC-optimized in-vehicle video interference suppression method of claim 1, wherein, The holographic differential processing specifically comprises: A digital cross-correlation technology is used to sample and synchronize the composite signal and the reference model; after synchronization, a processing unit performs binary subtraction on each sampling point, and the difference is a holographic electromagnetic interference component which completely retains time-domain characteristics of interference transients and phase noise.

4. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The analysis of the amplitude-phase transfer function of the interference component on the transmission medium specifically comprises: A time-domain interference signal is divided into multiple overlapping analysis frames, a window function is applied to each frame to smooth the boundary and suppress frequency spectrum leakage; fast Fourier transform is performed on the windowed data frame, and from the complex results obtained through the transform, an amplitude spectrum representing the energy of each frequency component and a phase spectrum representing the phase are calculated, and the amplitude spectrum and the phase spectrum jointly constitute the amplitude-phase transfer function of the interference in a time window.

5. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The phase conjugate inversion specifically comprises: Conjugate operation is performed on complex frequency point values of the amplitude-phase transfer function, the conjugate operation keeps the amplitude of each frequency component unchanged, and reverses the phase by 180 degrees to generate a target spectrum with consistent energy distribution but opposite phase of the interference; inverse fast Fourier transform is performed on the target spectrum to recombine a real-valued time-domain control waveform, and the time-domain control waveform is a digital precursor of the reverse holographic electromagnetic wave.

6. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The distributed holographic modulation array specifically comprises: The time-domain control waveform is distributed to multiple injection nodes arranged along the cable path at a predetermined interval. Inside each node, an independent digital-to-analog converter and a variable gain amplifier convert the digital waveform into an adjustable analog signal. The gain of each node is independently controlled by an adaptive weight update processor, allowing the array to form a spatially weighted non-uniform cancellation field that cancels out the distributed interference penetrating the cable at multiple points.

7. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The coupling injection specifically includes: The coupling injection is a non-contact energy injection method based on near-field magnetic coupling. The injection node includes a clamping inductive structure composed of a ferrite core. When the control waveform drives the clamping inductive structure, the excited controlled alternating magnetic field penetrates the cable insulation layer in a non-invasive manner and induces a cancellation signal on the cable conductor that is opposite in phase to the original interference according to the law of electromagnetic induction.

8. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The monitoring method of the downstream residual interference monitoring point specifically includes: High-impedance sensing is performed on the transmission link and physically after the last injection node of the distributed holographic modulation array to pick up the analog residual signal, and analog-to-digital conversion is performed to generate a digital residual interference signal.

9. The EMC-optimized in-vehicle video interference mitigation method of claim 1, wherein, The adaptive weight update algorithm specifically includes: The adaptive weight update algorithm is a normalized least mean square algorithm that is iteratively optimized in the digital domain. The digital residual interference signal is used as real-time error input, and the holographic electromagnetic interference component is used as reference input. Based on the error input and the reference input, the internal weight vector is continuously iteratively adjusted. The weight vector defines the independent gain of each injection node in the distributed holographic modulation array, dynamically correcting the spatial weighting amplitude of the reverse holographic electromagnetic wave injected into the transmission link.

10. An EMC-optimized in-vehicle video interference suppression system, characterized by, It includes: Transmission reference model storage module: for storing the transmission reference model of the video signal; Holographic differential processing module: connected to the transmission reference model storage and the transmission link, for real-time comparison of the transmission reference model and the composite signal carried by the transmission link, and extraction of the holographic electromagnetic interference component; Transfer function analysis module: connected to the holographic differential processor, analyzes the amplitude-phase transfer function of the holographic electromagnetic interference component on the transmission medium; Phase conjugate inversion processing module: connected to the transfer function analysis unit, performs phase conjugate inversion based on the amplitude-phase transfer function, and generates control parameters of the reverse holographic electromagnetic wave; Distributed holographic modulation array module: deployed along the transmission link and connected to the phase conjugate inversion processor, generates the reverse holographic electromagnetic wave according to the control parameters and couples it into the transmission link; Residual interference monitoring module: set downstream of the transmission link to extract the residual interference component after cancellation; Adaptive weight update processing module: connected to the residual interference monitoring unit and the distributed holographic modulation array module, runs the adaptive weight update algorithm, and dynamically optimizes the control parameters of the reverse holographic electromagnetic wave.

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