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Low energy consumption underwater acoustic data compression and reconstruction method

A technology of data compression and low energy consumption, which is applied in the fields of underwater acoustics and underwater acoustic signal processing, can solve the problems of low restoration accuracy and high energy consumption of underwater acoustic data compression, and achieves less requirements for underwater acoustic environment parameters, low energy consumption and low energy consumption. Compression and recovery, optimizing the effect of iterative calculations

Active Publication Date: 2018-01-05
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to avoid the shortcomings of the existing technology, the present invention proposes a low-energy-consumption underwater acoustic data compression and reconstruction method, which overcomes the problems of high energy consumption and low recovery accuracy in the prior art for underwater acoustic data compression.

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  • Low energy consumption underwater acoustic data compression and reconstruction method

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specific Embodiment

[0060] The present invention will be further described now in conjunction with accompanying drawing. refer to figure 1 , using a linear frequency modulated signal (Linear Frequency Modulated, LFM, also known as frequency sweep) as the transmission signal. The specific expression is:

[0061]

[0062] where r represents the frequency modulation rate, and the instantaneous frequency f(t) of x(t) at time t is f c +rt, the bandwidth of the signal can be expressed as B=rT. The parameters are set as follows: signal length is 4sec, center frequency f c = 1.2kHz, bandwidth B = 0.6kHz, sampling rate f s = 24kHz. From figure 1 It can be seen that the frequency sweep signal exhibits non-sparse characteristics in both the time domain and the frequency domain, so the existing compressive sensing algorithm cannot be directly used for compression and recovery.

[0063] In order to evaluate the performance of the algorithm, the compression ratio (Compression ratio, CR) is used, which...

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Abstract

The invention relates to a low energy consumption underwater acoustic data compression and reconstruction method. An M*N-dimensional matrix randomly formed by elements 0 and 1 is used as a sampling matrix Phi to compress underwater acoustic data, and then an N*N dictionary matrix Psi is built by combining (Daubechies, Db) Db-20 wavelet filter. In the method, a wavelet dictionary matrix is used forsparsity expression of the underwater acoustic signal for the first time, and a sparse solution in a frame with sparsity expression built is resolved by using an AL0 norm constraint algorithm; and aresult of the sparse solution and the dictionary matrix Psi are used, so as to recover the compressed underwater acoustic signal finally. The method has obvious implementation effect in compression and recovery simulation of a sweep signal, and converts signals with non-sparse time frequency effectively, and based on the method, a corresponding optimizing algorithm is designed and is applied to compression and recovery of non-sparse signals for the first time. The defect that the compressed sensing algorithm cannot be used directly at present is overcome.

Description

technical field [0001] The invention relates to a method for compressing and reconstructing underwater acoustic data with low energy consumption, especially for compressing and restoring underwater acoustic signals with non-sparse structure characteristics in time domain and frequency domain, and is suitable for underwater acoustic communication and acoustic data monitoring in the ocean It belongs to the field of underwater acoustics and underwater acoustic signal processing. Background technique [0002] In order to realize low-energy transmission of a large amount of collected underwater acoustic data, the present invention uses a wavelet filter bank to construct a sparse transformation matrix, so as to transform non-sparse underwater acoustic signals, including time domain and frequency domain, into non-sparse features that can Sparsely represented domains are represented. At present, the methods for data compression mainly include wavelet compression and compressed sens...

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Application Information

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IPC IPC(8): H03M7/30
Inventor 伍飞云杨坤德段睿孙权
Owner NORTHWESTERN POLYTECHNICAL UNIV
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