An EMI optimization process for marine communication and navigation systems

Abstract

This invention discloses an EMI optimization method for ship communication and navigation systems, which include a ship communication system and a ship navigation system. The method includes the following steps: after receiving a signal with EMI interference, the ship communication and navigation system uses FFT to convert the received time-domain signal into a frequency-domain signal, thereby identifying the interference signal, and eliminating the interference signal using a notch filter and a Raman filter. The technical solution of this invention can accurately remove interference frequencies without affecting the original communication signal, improving the clarity of AIS / VHF communication and ensuring the communication stability between LNG ships and ports, as well as other vessels.

Description

Technical Field

[0001] This invention relates to the field of ship communication, and in particular to an EMI optimization method for ship communication and navigation systems. Background Technology

[0002] When LNG carriers are navigating and operating at sea, their communication and navigation systems are subject to various noise interferences, including: environmental noise (waves, wind noise, rain noise), mechanical noise (engines, pumps, compressors, turbines, etc.), and electromagnetic interference (EMI) (radio, satellite communications, signals from nearby vessels). These noises can degrade communication quality, affect navigation accuracy, and even cause errors in critical system data. Fourier transform (FFT) can effectively analyze the noise spectrum and suppress it through signal processing techniques.

[0003] In the integrated automation system (IAS) and communication and navigation systems of LNG carriers, electromagnetic interference (EMI) suppression is crucial. While traditional filtering methods (low-pass, high-pass, band-pass, notch filtering, etc.) are effective, they have limitations in the complex marine environment. In the LNG carrier's electrical system, 50Hz / 60Hz power frequency noise and its harmonics (150Hz, 300Hz, etc.) significantly impact communication and navigation. Simultaneously, harmonic interference (up to kHz levels) generated by variable frequency drive (VFD) equipment can affect VHF signals. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an EMI optimization processing method for ship communication and navigation systems, which can accurately remove interference frequencies without affecting the original communication signal, improve the clarity of AIS / VHF communication, and ensure the communication stability between LNG ships and ports and other vessels.

[0005] Based on the above-mentioned objectives, the present invention provides the following technical solution: An EMI optimization method for a ship communication and navigation system, the ship communication and navigation system including a ship communication system and a ship navigation system, the method comprising the following steps: After receiving signals with EMI interference, the ship's communication and navigation system uses FFT to convert the received time-domain signal into a frequency-domain signal, thereby identifying the interference signal and eliminating it through notch filters and Raman filters.

[0006] Furthermore, the ship's communication system receives AIS / VHF communication signals with EMI interference, uses FFT to convert the received time-domain signal into a frequency-domain signal, identifies the frequency of the EMI interference signal through spectrum analysis, and then uses a notch filter to precisely suppress the frequency of the EMI interference signal, filtering out the EMI interference signal. Finally, the AIS / VHF communication signal with the EMI interference signal filtered out is converted back into a time-domain signal through IFFT.

[0007] Furthermore, the ship navigation system receives GNSS signals with EMI interference, uses FFT to convert the received time-domain signal into a frequency-domain signal, identifies and detects the frequency of the EMI interference signal using a spectrum analyzer, and uses a Raman filter to predict and correct errors in the GNSS signal with EMI interference based on the detected EMI interference signal frequency. After correction, the filtered GNSS signal is fused with the INS signal.

[0008] Furthermore, the method for fusing the filtered signal with the INS signal includes: converting the INS signal into a frequency domain signal using FFT, and then weighting and aligning the converted INS signal and the filtered GNSS signal on the corresponding axes. The weight allocation for different frequency points is dynamically adjusted based on the reliability of the GNSS signal and the INS signal in the corresponding frequency bands. The frequency bands are divided into low-frequency bands (0-0.1Hz), mid-frequency bands (0.1-1Hz), and high-frequency bands (>1Hz) according to the ship's navigation characteristics. The corresponding weights for the GNSS signal and the INS signal are (0.7, 0.3), (0.5, 0.5), and (0.3, 0.7), respectively. Weighted fusion is performed on each frequency point.

[0009] Furthermore, a notch filter can remove signals of a specific frequency while allowing signals of other frequencies to pass through without loss, following the transfer function: , Among them, design function , ( (), z is a complex variable of Z, It is dimensionless. It is the normalized angular frequency to be trapped and , That is the frequency you actually want to filter out. is the sampling frequency, r is the pole radius, and BW is the bandwidth. The closer the pole radius is to 1, the wider the bandwidth.

[0010] Furthermore, the Raman filter can recursively derive the minimum mean square error estimate in noisy dynamic systems, and its discrete linear model is as follows: , , This discrete linear model can be normalized into a five-step recursive model: S1. Verification Status: ; S2. Retested covariance: ; S3. Perform KALMAN gain again: ; S4. Further verify the status: ; S5. Further test the covariance: ; in In the text, the subscript k represents the system state vector at time k. Given the known external input control vector. Let F be the actual observed vector, and let F be two-dimensional. Let B be the dynamic state transition matrix. The control input matrix, H is Mapping the states to the observation matrix, Q is... The process noise covariance matrix, R is p The observation noise covariance matrix, for matrix, for The KALMAN gain matrix, For process noise, The process noise in the process variance is where In Q, n is the number of one-dimensional variables. Observation noise The dimension is p, and I is the identity matrix. .

[0011] Based on the above technical solutions, this invention has the following advantages compared to existing technologies: 1. This invention obtains the spectrum of AIS (Automatic Identification System) / VHF (Very High Frequency) signals through FFT (Fast Fourier Transform), locates the EMI (Electromagnetic Interference) frequency, calculates the center frequency of EMI interference, and dynamically adjusts the bandwidth of the notch filter to suppress EMI without affecting normal communication, accurately suppressing EMI interference without damaging the effective signal; 2. This invention analyzes the GNSS error spectrum through FFT, identifies abnormal frequency bands in the GNSS (Global Navigation Satellite System) signal (such as high-frequency noise from power frequency signals and VHF harmonics), accurately compensates for GNSS errors using a Kalman filter, and enhances the stability of the LNG ship navigation system by fusing inertial navigation system (INS) signals. Attached Figure Description

[0012] Figure 1 This is a flowchart of the process of Embodiment 1 of the present invention; Figure 2 This is a simulated spectrum diagram of Embodiment 1 of the present invention; Figure 3 This is a flowchart of the process of Embodiment 2 of the present invention; Figure 4 This is a simulated spectrum diagram of Embodiment 2 of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is described below with reference to specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0014] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms and should not be construed as indicating or implying relative importance. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0016] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical connection or internal connection between two components. They can be direct connection or indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0017] To better understand the technical solution of the present invention, the present invention will be described in detail below with reference to specific embodiments.

[0018] An EMI optimization method for a ship communication and navigation system, the ship communication and navigation system including a ship communication system and a ship navigation system, the method comprising the following steps: After receiving signals with EMI interference, the ship's communication and navigation system uses FFT to convert the received time-domain signal into a frequency-domain signal, thereby identifying the interference signal and eliminating it through notch filters and Raman filters.

[0019] The FFT is an algorithm for efficiently computing the Discrete Fourier Transform (DFT). The DFT transforms a computational time series x[n] of length N into its frequency domain representation X[k]. Where x[n] is the time-domain signal (discrete sequence), X(k) is the frequency-domain representation, N is the sequence length, and j is the imaginary unit. FFT reduces computational complexity by utilizing the symmetry and periodicity in DFT computation. Reduce to Nowadays, mathematical operations can be quickly performed using computer code such as Python.

[0020] IFFT is the inverse process of FFT, used to convert a frequency domain signal X(k) into a time domain signal x[n]. Mathematically, it corresponds to the Discrete Fourier Transform (IDFT). .

[0021] Example 1, such as Figure 1The diagram illustrates the processing flow of AIS / VHF (Automatic Identification System / VHF signal) signals after EMI interference. First, the ship preprocesses the AIS / VHF communication signal, which may be affected by radio interference or EMI from other vessels. Then, FFT is used to identify the EMI frequencies, i.e., the signal spectrum, and to pinpoint the specific frequency components of the EMI interference, such as 150Hz and 300Hz. Next, a notch filter is used for precise suppression of the identified EMI frequencies, filtering out interference signals within a specific filtering range while preserving as much of the original AIS / VHF signal as possible. The signal after Notch filtering is then transformed back to the time domain using inverse FFT (IFFT), achieving EMI interference suppression, restoring the original AIS / VHF signal to normal, and optimizing the ship's communication quality. The notch filter can remove signals of specific frequencies, while allowing signals of other frequencies to pass without loss, following the transfer function: , Among them, design function , ( (), z is a complex variable of Z, It is dimensionless. It is the normalized angular frequency to be trapped and , That is the frequency you actually want to filter out. Here, r is the sampling frequency, r is the pole radius, and BW is the bandwidth. The closer the pole radius is to 1, the wider the bandwidth. (Notch filtering) This represents two adjustable variables: where the trap is located and how narrow r is.

[0022] like Figure 2 The image shows a computer-simulated spectrum of the FFT and NOTCH filters. The vertical axis represents the radiation intensity, with units of dBμV / m. 2 This is 1 million microvolts per square meter, and the horizontal axis represents the frequency unit in Hertz (Hz). The dashed line in this graph shows the radio waves, specifically the VHF interference frequency at 150 Hz and the various EMI frequencies at 300 Hz in ships, determined by computer simulation using this method. The solid line represents the radiation intensity that the computer can sample in the 0-5000 Hz range after passing through a Notch filter. The graph shows that 1.75 μV / m can be extracted in the region approaching 1700 Hz. 2 The radiation intensity. Then, using our invention, the required peak value can also be extracted in the VHF international standard frequency band of 30-300, which is usually the radio frequency band, and at the same time, this invention can be used to filter out noise to obtain a higher quality VHF band.

[0023] Example 2, as Figure 3As shown, a method combining FFT and Raman filtering is used to describe the processing of GNSS signals after receiving EMI interference. The ship receives and preprocesses GNSS signals affected by EMI interference from sources such as radio, ground equipment, or the space environment. Then, FFT analysis is used to analyze the spectral distribution of the interfered GNSS signal, detecting the specific frequency of the EMI interference to ensure the accuracy of subsequent filtering. Next, Raman filtering is used to predict and correct errors in the GNSS signal. This significantly enhances the signal's resistance to interference in the dynamic maritime environment, making it particularly suitable for continuous EMI interference during navigation. Finally, GNSS / INS fusion improves the stability and accuracy of the navigation system, maintaining accurate positioning even in environments with severe EMI interference during navigation.

[0024] like Figure 3 The image shows a computer-simulated spectrum of the FFT and KALMAN filters. The vertical axis represents the radiation intensity, with units of dBμV / m. 2 This is 1 million microvolts per square meter, and the horizontal axis represents frequency in Hertz (Hz). The dashed line in this graph shows the radio waves determined by the computer simulation using this method, namely the VHF interference frequency at 150Hz and the various EMI (electromagnetic interference) frequencies at 300Hz in ships. The difference is that the solid line in this graph represents the radiation intensity that the computer can sample in the 0-5000 Hz range after passing through a Raman filter. From the graph, we can see two differences compared to a notch filter: firstly, noise in the low-frequency range is not completely eliminated; secondly, the peak height is significantly reduced. This indicates that the simulated peak value is smoothed, meaning the desired frequency band is spread or dispersed across a wider band. Figure 3 The difference lies in the presence of significant EMI interference peaks in the low-frequency region, while the signal is not significantly affected by EMI in the high-frequency region (1000Hz), and the main peak remains clearly visible. The main signal component (around 2000Hz) is well preserved, indicating that the FFT and Raman filter's ability to process signal interference effectively removes EMI interference while retaining the main information of the original signal.

[0025] Raman filtering utilizes a recursive algorithm to reduce the impact of noise by estimating the system's state. It combines a system prediction model with actual measurements, minimizing the error variance by calculating the optimal estimate. The low-frequency residual noise in the spectrum is due to the recursive algorithm mentioned earlier. Such optimization algorithms cannot be computed infinitely by a computer; achieving a perfectly stable state is currently impossible. Therefore, the noise signal cannot be completely smoothed out in the computer simulation of the spectrum, resulting in... Figure 4The slight fluctuations in signal strength are a side effect of the KALMAN filter's pursuit of noise immunity and robustness. This sacrifices some signal strength for higher noise immunity, which is also a method of its intelligent suppression and prediction optimization. The recursive nature of the Raman filter means that it doesn't require a large amount of storage for measurement data; only the current estimate and error covariance matrix are needed, making computation very efficient. Furthermore, because it only requires the current data, it also has good adaptability, automatically adjusting the filter parameters according to the current noise level.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. An EMI optimization method for ship communication and navigation systems, characterized in that, The ship communication and navigation system includes a ship communication system and a ship navigation system, and the method includes the following steps: After receiving signals with EMI interference, the ship's communication and navigation system uses FFT to convert the received time-domain signal into a frequency-domain signal, thereby identifying the interference signal and eliminating it through notch filters and Raman filters.

2. The EMI optimization method for ship communication and navigation systems according to claim 1, characterized in that, The ship's communication system receives AIS / VHF communication signals with EMI interference. It uses FFT to convert the received time-domain signal into a frequency-domain signal, identifies the frequency of the EMI interference signal through spectrum analysis, and then uses a notch filter to precisely suppress the frequency of the EMI interference signal, thus filtering out the EMI interference signal. Finally, the AIS / VHF communication signal with the EMI interference signal filtered out is converted back into a time-domain signal using IFFT.

3. The EMI optimization method for ship communication and navigation systems according to claim 1, characterized in that, The ship navigation system receives GNSS signals with EMI interference, uses FFT to convert the received time-domain signal into a frequency-domain signal, identifies and detects the frequency of the EMI interference signal using a spectrum analyzer, and uses a Raman filter to predict and correct the error of the GNSS signal with EMI interference based on the detected EMI interference signal frequency. After correction, the filtered GNSS signal is fused with the INS signal.

4. The EMI optimization method for ship communication and navigation systems according to claim 3, characterized in that, The method for fusing the filtered signal with the INS signal includes: converting the INS signal into a frequency domain signal using FFT, and then weighting and aligning the converted INS signal and the filtered GNSS signal on the corresponding axes. The weight allocation for different frequency points is dynamically adjusted based on the reliability of the GNSS signal and the INS signal in the corresponding frequency bands. The frequency bands are divided into low frequency band (0-0.1Hz), mid frequency band (0.1-1Hz), and high frequency band (>1Hz) according to the ship's navigation characteristics. The corresponding weights of the GNSS signal and the INS signal are (0.7, 0.3), (0.5, 0.5), and (0.3, 0.7), respectively. Weighted fusion is performed on each frequency point.

5. The EMI optimization method for ship communication and navigation systems according to claim 1, characterized in that, Notch filters can remove signals of a specific frequency while allowing signals of other frequencies to pass through without loss, following the transfer function: ， Among them, design function , ( (), z is a complex variable of Z, It is dimensionless. It is the normalized angular frequency to be trapped and , That is the frequency you actually want to filter out. is the sampling frequency, r is the pole radius, and BW is the bandwidth. The closer the pole radius is to 1, the wider the bandwidth.

6. The EMI optimization method for ship communication and navigation systems according to claim 1, characterized in that, The Raman filter can recursively derive the minimum mean square error estimate in noisy dynamic systems. Its discrete linear model is as follows: ， ， This discrete linear model can be normalized into a five-step recursive model: S1. Verification Status: ； S2. Retested covariance: ； S3. Perform KALMAN gain again: ； S4. Further verify the status: ； S5. Further test the covariance: ； in In the text, the subscript k represents the system state vector at time k. Given the known external input control vector. Let F be the actual observed vector, and let F be two-dimensional. Let B be the dynamic state transition matrix. The control input matrix, H is Mapping the states to the observation matrix, Q is... The process noise covariance matrix, R is p The observation noise covariance matrix, for matrix, for The KALMAN gain matrix, For process noise, The process noise in the process variance is where In Q, n is the number of one-dimensional variables. Observation noise The dimension is p, and I is the identity matrix. .

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