A multi-channel wavelet denoising system and method based on ZYNQ

Abstract

The present invention discloses a ZYNQ-based multi-channel wavelet denoising system and method. The present invention integrates two heterogeneous chips, FPGA-PL side and ARM-PS side, and uses the parallelism of PL side hardware processing and the convenience of PS side software processing to implement baseband parallel channel wavelet denoising processing of a navigation receiver.

Description

Technical Field

[0001] The present invention belongs to the field of Beidou terminal products, and specifically relates to a ZYNQ-based multi-channel wavelet noise reduction system and method, which is used to improve the positioning capability of Beidou terminals in complex environments and improve the applicability of Beidou products in complex environments. Background Art

[0002] The BeiDou Navigation Satellite System (BDS) is an infrastructure that provides high-precision positioning, navigation, and timing services around the clock, in all weather conditions, to users worldwide. The BDS has been widely adopted across various industries, profoundly changing people's lives and work. As critical infrastructure, the power system has an unshirkable responsibility to promote the application of the BDS.

[0003] For navigation satellite signals, including those from the Beidou system, regional bursts of ionospheric scintillation can cause them to exhibit non-stationary and non-Gaussian characteristics. This can cause rapid fluctuations in the amplitude and phase of the signals reaching the navigation receiver, severely impacting the accuracy and stability of the receiver's baseband tracking loop. Traditional methods for improving the accuracy and robustness of baseband tracking loops are ineffective for non-stationary scintillation signals. Wavelet denoising has good adaptability and can effectively eliminate the non-Gaussian noise introduced by scintillation.

[0004] In the field of navigation satellite signal baseband processing, baseband processing is typically divided into hardware (FPGA / ASIC) and software. The hardware processing portion consists of a fixed number of parallel channels, subject to a maximum channel count limit. Baseband processing with a large number of channels also requires high real-time processing capabilities from the software processing portion. For some multi-mode, multi-frequency, high-precision navigation receivers, the baseband processing often involves hundreds of channels. Wavelet noise reduction is performed on each channel, making both hardware and software implementations very resource-intensive and making real-time baseband processing difficult. Summary of the Invention

[0005] The purpose of the present invention is to provide a ZYNQ-based multi-channel wavelet denoising system and method to solve the problem of large software and hardware resource occupation and difficulty in real-time processing of multi-channel wavelet denoising of navigation satellite signals. The present invention occupies few software and hardware resources and does not affect baseband real-time processing.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A ZYNQ-based multi-channel wavelet noise reduction system includes a PL end and a PS end. The PL end uses an FPGA chip, and the PS end uses an ARM chip. The FPGA chip includes a time-division multiplexing control module, a data acquisition module, and several wavelet noise reduction processing modules. The ARM chip includes a threshold calculation module. The input end of the data acquisition module is connected to the navigation signal baseband data output channel; wherein:

[0008] Data acquisition module: used to collect baseband data of the navigation signal baseband data output channel and transmit it to the wavelet noise reduction processing module;

[0009] Time division multiplexing control module: used to control the transmission of baseband data to the wavelet noise reduction processing module through time division multiplexing;

[0010] Threshold calculation module: used to perform threshold calculation on the baseband data of each navigation signal baseband data output channel to obtain the noise threshold, and transmit the noise threshold to the wavelet noise reduction processing module;

[0011] Wavelet noise reduction processing module: used to perform noise reduction on the received baseband data according to the noise threshold.

[0012] Furthermore, the data acquisition module and the threshold calculation module are connected using an AXI interface.

[0013] Furthermore, the threshold calculation module and the wavelet noise reduction processing module are connected using an AXI interface.

[0014] Furthermore, a cache module is provided between the data acquisition module and the threshold calculation module.

[0015] Furthermore, the threshold solution includes median calculation, square calculation, square root calculation and logarithm calculation.

[0016] Furthermore, the number of the wavelet noise reduction processing modules is m / n, where m is the number of navigation signal baseband data output channels, and n is the number of channels that each wavelet noise reduction processing module can process. If m / n is an integer, the integer is recorded as the number of wavelet noise reduction processing modules; otherwise, the nearest integer greater than m / n is taken as the number of wavelet noise reduction processing modules.

[0017] Furthermore, the calculation method for the number of channels n that each wavelet noise reduction processing module can process is as follows: n is equal to the ratio of the frequency of the PL end working clock to the baseband data sampling rate. If the ratio is an integer, then n is equal to the integer; otherwise, n is equal to the nearest integer smaller than the ratio.

[0018] A multi-channel wavelet denoising method based on ZYNQ, comprising:

[0019] Collect baseband data of the navigation signal baseband data output channel;

[0020] Perform threshold calculation on the baseband data of each navigation signal baseband data output channel to obtain a noise threshold;

[0021] The control baseband data is transmitted in a time division multiplexing manner, and noise reduction processing is performed on the transmitted baseband data in combination with the noise threshold.

[0022] Furthermore, the threshold solution includes median calculation, square calculation, square root calculation and logarithm calculation.

[0023] Furthermore, after collecting baseband data of the navigation signal baseband data output channel, the baseband data is cached, and a threshold value is solved for the cached baseband data to obtain a noise threshold value.

[0024] Compared with the prior art, the present invention has the following beneficial technical effects:

[0025] The multi-channel wavelet denoising system of the present invention leverages hardware parallel processing and time-division multiplexing to improve resource utilization on the processor (PL) side. Conventionally, mathematical calculations of noise thresholds performed by PL-side hardware are cumbersome, and solving the noise threshold for a particular channel only requires a single calculation before reuse. However, implementing this calculation in PL-side hardware only uses logic resources once, resulting in low resource utilization. Therefore, the present invention implements threshold calculations in PS-side software, making ARM more suitable for mathematical calculations and improving the real-time performance of multi-channel wavelet denoising.

[0026] The number of channels processed by the multi-channel wavelet noise reduction system of the present invention is no longer fixed. If the working clock of the PL end is n times the data sampling rate, then logically one wavelet noise reduction processing module can process n channels of data. If there are m channels in the physical structure, then logically only m / n wavelet noise reduction units are needed. Usually, the sampling rate of the navigation signal baseband data is 25MHz, while the working clock of the PL end is as high as several hundred megahertz. Assuming a 200MHz working clock, then physically only one wavelet noise reduction process is used. Through pipeline processing, the baseband data of eight channels can be logically processed in time-division multiplexing, greatly improving the utilization rate of hardware resources on the PL end. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is the flow chart of wavelet denoising process;

[0029] Figure 2 This is a schematic diagram of the structure of a multi-channel wavelet denoising system;

[0030] Figure 3 This is the architecture diagram of carrier tracking based on wavelet denoising. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] The ZYNQ-based multi-channel wavelet noise reduction system of the present invention (i.e., the ZYNQ platform) integrates two heterogeneous chips: FPGA (referred to as "PL side") and ARM (referred to as "PS side"), and uses the parallelism of PL side hardware processing and the convenience of PS side software processing to implement baseband parallel channel wavelet noise reduction processing of the navigation receiver.

[0034] The baseband data acquisition and wavelet denoising processing of the navigation receiver are completed by the PL side, and the PS side is used to calculate the noise threshold required for wavelet denoising. The baseband data wavelet denoising processing process is as follows: Figure 1 shown.

[0035] Usually, for each additional data channel, a wavelet noise reduction process needs to be added, and wavelet noise reduction requires a large number of multipliers. It is time-consuming to implement wavelet noise reduction processing by PS-side software, which affects the real-time processing performance; if wavelet noise reduction processing is implemented by PL-side hardware, it will occupy a lot of hardware resources when the number of channels is large. The present invention improves the resource utilization of the PL side through time division multiplexing. Usually, the sampling rate of the navigation signal baseband data is 25MHz, while the working clock of the PL side is as high as several hundred megahertz. Assuming a 200MHz working clock, only one wavelet noise reduction process is used physically, and the baseband data of 8 channels can be logically processed through time division multiplexing through pipeline processing. The structure of the multi-channel wavelet noise reduction unit is as follows: Figure 2 shown.

[0036] Wavelet noise reduction threshold calculation involves finding the median, squaring, square root, and logarithm. Implementing the mathematical calculations for noise thresholds in PL hardware is complex, and the noise threshold for a particular channel only needs to be solved once before it can be reused. Implementing this in PL hardware only uses logic resources once, resulting in low resource utilization. Therefore, threshold calculations are implemented in ZYNQ PS software, as ARM processors are more suitable for mathematical calculations. The PS and PL communicate using an AXI interface. Baseband data collected by the PL is cached in a DDR3 buffer module via the AXI interface. The PS reads the baseband data from the DDR3 buffer module and calculates the threshold. The resulting result is then transmitted to the PL via the AXI interface.

[0037] Example

[0038] Beidou receiver carrier tracking architecture based on wavelet noise reduction Figure 3 shown.

[0039] Ionospheric scintillation can cause rapid fluctuations in the amplitude and phase of the navigation signal, making the carrier tracking based on the IQ branch integration results non-stationary, nonlinear and non-Gaussian. As a result, the traditional carrier tracking algorithm based on discriminator and Kalman filter cannot effectively mitigate the impact of ionospheric scintillation.

[0040] Wavelet denoising has excellent adaptability for non-stationary, non-Gaussian scintillation signals, effectively characterizing their non-stationary characteristics, such as edges, peaks, and discontinuities. By adding the wavelet denoising system of the present invention after the correlator in a conventional tracking loop, the non-Gaussian noise introduced by ionospheric scintillation can be effectively filtered out, reducing the adverse effects of scintillation on the loop discriminator and improving the accuracy and robustness of the tracking loop.

[0041] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A multi-channel wavelet denoising system based on ZYNQ, characterized in that: It includes a PL end and a PS end, wherein the PL end adopts an FPGA chip, and the PS end adopts an ARM chip. The FPGA chip includes a time division multiplexing control module, a data acquisition module, and several wavelet noise reduction processing modules. The ARM chip includes a threshold calculation module. The input end of the data acquisition module is connected to the navigation signal baseband data output channel; wherein: Data acquisition module: used to collect baseband data of the navigation signal baseband data output channel and transmit it to the wavelet noise reduction processing module; Time division multiplexing control module: used to control the transmission of baseband data to the wavelet noise reduction processing module through time division multiplexing; Threshold calculation module: used to perform threshold calculation on the baseband data of each navigation signal baseband data output channel to obtain the noise threshold, and transmit the noise threshold to the wavelet noise reduction processing module; Wavelet noise reduction processing module: used to perform noise reduction on the received baseband data according to the noise threshold; The threshold solution includes median calculation, square calculation, square root calculation and logarithm calculation; The number of the wavelet noise reduction processing modules is m / n, where m is the number of navigation signal baseband data output channels, and n is the number of channels that each wavelet noise reduction processing module can process. If m / n is an integer, then the integer is recorded as the number of wavelet noise reduction processing modules; otherwise, the nearest integer greater than m / n is taken as the number of wavelet noise reduction processing modules. The calculation method for the number of channels n that each wavelet noise reduction processing module can process is as follows: n is equal to the ratio of the frequency of the PL end working clock to the baseband data sampling rate. If the ratio is an integer, then n is equal to the integer; otherwise, n is equal to the nearest integer smaller than the ratio.

2. The multi-channel wavelet denoising system based on ZYNQ according to claim 1, characterized in that: The data acquisition module and the threshold calculation module are connected using an AXI interface.

3. The multi-channel wavelet denoising system based on ZYNQ according to claim 1, characterized in that: The threshold calculation module and the wavelet noise reduction processing module are connected using an AXI interface.

4. The multi-channel wavelet denoising system based on ZYNQ according to claim 1, characterized in that: A cache module is provided between the data acquisition module and the threshold calculation module.

5. A ZYNQ-based multi-channel wavelet denoising method, applied to a ZYNQ-based multi-channel wavelet denoising system according to any one of claims 1 to 4, characterized in that: include: Collect baseband data of the navigation signal baseband data output channel; Perform threshold calculation on the baseband data of each navigation signal baseband data output channel to obtain a noise threshold; The control baseband data is transmitted in a time-division multiplexing manner, and noise reduction processing is performed on the transmitted baseband data in combination with the noise threshold; The threshold solution includes median calculation, square calculation, square root calculation and logarithm calculation. After collecting baseband data of the navigation signal baseband data output channel, the baseband data is cached, and the threshold solution is performed on the cached baseband data to obtain the noise threshold.

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