Method for estimating cycle slope and starting frequency of hyperbolic frequency modulated signals

A technology of signal period and starting frequency, applied in the field of signal processing, can solve problems such as increasing the amount of calculation, increasing the difficulty and complexity of calculation, and limiting engineering practicability.

Inactive Publication Date: 2014-03-26
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Both the maximum likelihood method and the nonlinear least squares method need to solve nonlinear equations, but for hyperbolic FM signals, the nonlinear equations that these two methods need to solve do not have analytical solutions, so numerical solutions need to be used, which is Increased the difficulty o

Method used

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  • Method for estimating cycle slope and starting frequency of hyperbolic frequency modulated signals
  • Method for estimating cycle slope and starting frequency of hyperbolic frequency modulated signals
  • Method for estimating cycle slope and starting frequency of hyperbolic frequency modulated signals

Examples

Experimental program
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Effect test

Embodiment 1

[0090] Example 1: First, perform parameter initialization, set the short-time window length M=128, the short-time window moving step L=32, and the weight correction factor δ 1 =1, maximum iteration threshold k=10 and precision control index ε=10 -3 , to calculate the total number of short-time windows The number of initialization window moves i=1, and the number of iterations k=1.

[0091] Then, move the time window, use the maximum line spectrum method to estimate the instantaneous frequency estimation value of the data sequence in each short time window, and obtain the instantaneous frequency estimation value sequence f i ,i=1,2,…,50, as shown in Table 1:

[0092] f 1

f 2

f 3

f 4

f 5

f 6

f 7

f 8

f 9

f 10

304

128

240

288

656

656

240

240

256

256

f 11

f 12

f 13

f 14

f 15

f 16

f 17

f 18

f 19

f 20

256

256

256

256

256

256 ...

Embodiment 2

[0097] Embodiment 2: First, perform parameter initialization, set short-time window length M=256, short-time window moving step L=64, weight correction factor δ 1 =1, maximum iteration threshold k=10 and precision control index ε=10 -3 , to calculate the total number of short-time windows The number of initialization window moves i=1, and the number of iterations k=1.

[0098] Then, move the time window, use the maximum line spectrum method to estimate the instantaneous frequency estimation value of the data sequence in each short time window, and obtain the instantaneous frequency estimation value sequence f i ,i=1,2,...,25; next step, for f i ,i=1,2,...,25 take the reciprocal to get g i ,i=1,2,…,25, and for g i ,i=1,2,...,25 to perform sliding median filtering to get

[0099] Finally, the iterative calculation is carried out by the improved weighted least squares linear fitting method, and the estimated value of the period slope of the hyperbolic FM signal is estimat...

Embodiment 3

[0100] Example 3: Initialize the parameters first, set the short-time window length M=256, the short-time window moving step L=64, and the weight correction factor δ 1 =1, the maximum iteration threshold k=10000 and the precision control index ε=10 -6 , to calculate the total number of short-time windows The number of initialization window moves i=1, and the number of iterations k=1.

[0101] Then, move the time window, use the maximum line spectrum method to estimate the instantaneous frequency estimation value of the data sequence in each short time window, and obtain the instantaneous frequency estimation value sequence f i ,i=1,2,...,25; next step, for f i ,i=1,2,...,25 take the reciprocal to get g i ,i=1,2,…,25, and for g i ,i=1,2,...,25 to perform sliding median filtering to get

[0102] Finally, the iterative calculation is carried out by the improved weighted least squares linear fitting method, and the estimated value of the period slope of the hyperbolic FM s...

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Abstract

The invention discloses a method for estimating the cycle slope and the starting frequency of hyperbolic frequency modulated signals. The method comprises the following steps of first, acquiring a data sequence, second, initializing parameters, third, calculating the power spectrum of data in the ith short time window, fourth, estimating the instantaneous frequency fi of the data in the ith short time window by using a maximum gammagraphy method, fifth, estimating the zero crossing point interval gi = 1/fi of the data in the ith short time window, sixth, judging whether the data of all the short time windows are processed, executing the third step if not processed, otherwise executing the seventh step, seventh, sliding median filtering is conducted on a zero crossing point interval estimating sequence {gi, i = 1, 2, ..., I}, eighth, calculating the kth time interation weight, ninth, judging whether an interation stopping condition is met or not, if not, executing the eighth step again, otherwise executing the tenth step, and tenth, calculating the cycle slope and the starting frequency. According to the method, complex calculating and parametric search are of no need, the stability is strong, and quick and high-precision estimation of the parameters can be achieved.

Description

technical field [0001] The invention belongs to the field of signal processing, and in particular relates to a method for estimating the period slope and initial frequency of a hyperbolic FM signal. Background technique [0002] The hyperbolic FM signal has Doppler invariance, which makes it especially suitable for detecting high-speed small targets, which makes the hyperbolic FM signal widely used in the fields of underwater acoustics and radar. The cycle slope and the initial frequency are two basic parameters that characterize the frequency characteristics of the linear hyperbolic FM signal. If these two parameters can be estimated, the obtained hyperbolic FM signal can be recovered under the condition of known signal pulse width , which is of great significance to underwater acoustic countermeasures and radar countermeasures, so its estimation problem is an important research content in the field of underwater acoustic and radar signal processing. [0003] At present, t...

Claims

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Application Information

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IPC IPC(8): G01S7/00G01H17/00
CPCG01H17/00G01S7/00
Inventor 方世良姚帅王晓燕韩宁王莉
Owner SOUTHEAST UNIV
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