Neural Network Bit Allocation for SAR Raw Data Quantization
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Solution Overview
Problem
Conventional BAQ quantization methods for SAR raw data require large amounts of data for bit rate regulation and a priori information, leading to increased computational complexity and storage requirements, especially in satellite-based systems with limited resources.
Innovation Solution
A data-driven model using a neural network is generated to dynamically determine bit rates for quantizing SAR raw data blocks, eliminating the need for explicit rules and a priori information by training the network with bit rate information from known raw data sets, allowing for adaptive bit rate allocation based on parameters like standard deviation and signal-to-quantization noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adaptive BAQ quantization with dynamic bit rates is used, then image quality is improved, but data storage and computational complexity increase
Solution Approach 1:
The patent changes the parameter of bit rate dynamically for different data blocks based on local signal characteristics. By adjusting the bit rate parameter according to the standard deviation and signal-to-noise ratio of each block, the system achieves adaptive compression that maintains image quality while optimizing data representation.
Solution Approach 2:
The patent performs preliminary calculation of bit rate parameters for each data block before actual quantization. By pre-determining the appropriate bit rate for each block based on its statistical properties, the system avoids complex real-time decisions during quantization, thereby reducing overall computational complexity.
2Manufacturing precision
If a priori information and look-up tables are used for adaptive quantization, then quantization performance is improved, but storage requirements and data transmission increase
Solution Approach 1:
The system performs self-service by calculating bit rate parameters directly from the incoming SAR raw data using simple statistical measures (standard deviation, signal-to-noise ratio). This eliminates the need for external look-up tables or pre-stored a priori information, allowing the quantization process to adapt autonomously to each data block's characteristics.
Solution Approach 2:
The patent extracts only the essential statistical parameters (standard deviation and signal-to-noise ratio) from each data block to determine bit rate allocation. By taking out only these critical features rather than storing or transmitting complete a priori information, the system achieves adaptive quantization with minimal data storage and transmission requirements.
Data Source
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AI summary
The invention relates to a method for the computer-aided generation of a data-driven model for the processing of digital SAR raw data, which comprises the backscattered radar echoes of radar pulses (RP), wherein the radar pulses (RP) were emitted by one or more transmitters of at least one moving object (SA) and the radar echoes were received by one or more receivers of the at least one object (SA), wherein the SAR raw data contains a raw data set (RD) for each receiver consisting of a plurality of data samples (SP), wherein, in the course of processing the SAR raw data, data blocks (DB) of data samples (SP) of the raw data set (RD) are quantized separately with a bit rate (BR) assigned to the respective data block (DB) using a bit rate information (BM).wherein the bit rate information (BM) specifies the assigned bit rate (BR) for each data block (DB) and the data-driven model is provided for determining the bit rate information (BM), wherein the generation of the data-driven model comprises the following steps: - generation of training data (TD) by generating bit rate information (BM') for a plurality of previously known raw data sets (RD') based on a rule which depends on values of at least one parameter (P) obtained from at least the respective previously known raw data set (RD'); - training a neural network (NN) with the training data (TD), wherein the trained neural network (NN) represents the generated data-driven model.