Piecewise linear mapping algorithm for SAR raw data compression

Abstract

When the saturation degree (SD) of space-borne SAR raw data is high, the performance of conventional block adaptive quantization (BAQ) deteriorates obviously. In order to overcome the drawback, this paper studies the mapping between the average signal magnitude (ASM) and the standard deviation of the input signal (SDIS) to the A/D from the original reference. Then, it points out the mistake of the mapping and introduces the concept of the standard deviation of the output signal (SDOS) from the A/D. After that, this paper educes the mapping between the ASM and SDOS from the A/D. Monte-Carlo experiment shows that none of the above two mappings is the optimal in the whole set of SD. Thus, this paper proposes the concept of piecewise linear mapping and the searching algorithm in the whole set of SD. According to the linear part, this paper gives the certification and analytical value of k and for nonlinear part, and utilizes the searching algorithm mentioned above to search the corresponding value of k. Experimental results based on simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is in heavy SD.synthetic aperture radar (SAR), raw data, saturation, compression, block adaptive quantization (BAQ)

A study of spaceborne SAR raw data compression error based on a statistical model of quantization interval transfer probability

“…iii) Calculate σ s and σ n using the equations SN R in = 10 lg(σ 2 s /σ 2 n ) and P = σ 2 s +σ 2 n , and then calculate the standard deviation of signal and noise before ADC respectively using the formula below [22]:…”

“…According to the theory of BAQ [3,22], raw data is normalized by the standard deviation of input signal (SDIS) to the ADC. So during the total noise power calculation, normalizing the output signal from the ADC is equivalent to normalizing input signal to the ADC and then, quantizing the normalized input signal by normalized ADC, with decision levels and output levels normalized by SDIS.…”

A Multichannel, Multipulse, Multiweight Block-Adaptive Quantization (3MBAQ) Algorithm Based on Space-Borne Multichannel SAR Doppler Domain A-BAQ

“…The mapping relationship [5,10,29] between the means of the absolute values and the standard deviations under the standard Gaussian distribution is given in Equation (4), and the mapping curve between the means of the sampled signal amplitude and the standard deviations of the input signal is shown in Figure 2. In addition, in order to reduce the complexity of calculating the standard deviation of blocks, BAQ calculates the amplitude means of the block signal and then looks up the table to obtain the standard deviations of the blocks.…”

“…The mapping relationship [5,10,29] between the means of the absolute values and the standard deviations under the standard Gaussian distribution is given in Equation ( 4), and the mapping curve between the means of the sampled signal amplitude and the standard deviations of the input signal is shown in Figure 2.…”

A universal adaptive vector quantization algorithm for space-borne SAR raw data

“…In order to overcome this problem, this paper firstly reviews the relationship between the standard deviation of the input signal (SDIS) and the average signal magnitude (ASM) [14,15], and then gives the multivariate statistical model of raw data and utilizes the distortion function of multidimensional space as criterion. Finally, the adaptive code book design algorithm is proposed according to the joint probability density function of the input data.…”