KR0134252B1 - Improved least mean square method - Google Patents

Abstract

The present invention relates to an improved method of LS, by finding an end position of a long ghost farthest from the start position of the removable sun ghost, and performing the SMS only within the range where the sun ghost and the long ghost exist. It is improved to calculate the filter coefficient at high speed.

Description

Improved LMS Method

1 is a schematic block diagram of a conventional ghost removal device.

FIG. 2 is a detailed block diagram of the filter unit and filter coefficient calculation unit of FIG.

3 is a flow chart illustrating the operation of the improved method of SMS in accordance with the present invention.

4 is a diagram illustrating a range of performing an SMS in accordance with the present invention.

* Explanation of symbols for main parts of the drawings

10: AD converter 20: reference signal extractor

30: filter coefficient calculation unit 40: filter unit

41: delay unit 42: FIR filter

43: IIR filter 50: DA converter

The present invention relates to an LS method, and more particularly, to an improved LS method capable of quickly performing an SMS.

In general, a video signal from a broadcasting station is reflected by an obstacle such as a mountain or a building while being received by a receiving antenna, or reflected without an impedance matching of a transmission path, thereby forming a multipath channel (MULTIPLE CHANNEL). For this reason, the reflected video signal in addition to the original video signal appears on the monitor of the television is called Ghost.

FIG. 1 is a schematic block diagram of a conventional ghost elimination device, which includes an AD converter 10, a reference signal extractor 20, a filter coefficient calculator 30, a filter 40, and a DA converter 50. It is composed of

In the figure, the AD converter 10 converts an input video signal into a digital signal, and the reference signal extractor 20 converts a ghost removal reference signal inserted into a predetermined portion of the video signal converted into a digital signal. Extract. The filter coefficient calculator 30 calculates and transmits the filter coefficients using the pre-stored ghost elimination reference signal and the ghost elimination reference signal extracted by the reference signal extractor 20, and the DA converter 50 filters the filter coefficients. The converted video signal is converted into an analog signal again.

2 is a detailed block diagram of the filter coefficient calculation unit 30 and the filter unit 40. As can be seen from the figure, the filter unit 40 includes a delay unit 41 for delaying the original video signal to remove the line ghost, a FIR filter 42 for removing the near ghost and the line ghost, and a long ghost. It consists of an IIR filter 43 and filters the input video signal using the filter coefficient transmitted from the filter coefficient calculation unit 30 and outputs it to the DA converter 50.

Hereinafter, the principle of removing the ghost generated in the transmission path will be described in more detail with reference to FIGS. 1 and 2.

When a broadcast station inserts and sends a ghost removal reference signal to a predetermined portion of the video signal, the ghost removal apparatus inputs a video signal, converts the digital signal from the AD converter 10 to a digital signal, and outputs it to the filter unit 40, and then extracts the reference signal. A predetermined portion into which the ghost elimination reference signal is inserted is extracted from the video signal converted into the digital signal by (20).

The filter coefficient calculation unit 30 compares the ghost removal reference signal stored in advance with the ghost removal reference signal extracted by the reference signal extraction unit 20 to calculate a filter coefficient that can remove the ghost generated in the transmission path. Ghost generated in the transmission path by transmitting to the FIR filter 42 and the IIR filter 43 in the 40, and filtering the input video signal using the filter coefficients transmitted by the two filters 42 and 43. Is removed.

On the other hand, there are two methods for obtaining a filter coefficient having an inverse characteristic of channel characteristics: a sequential correction method and a batch correction method.

First, the batch correction method has a short convergence time and no problem of convergence, while the calculation amount is large and the error in the steady state is relatively large. For example, in the case of using FFT (FAST FOURIER TRANSFORM), which is one of the batch correction methods, it depends on the DSP (DIGITAL SIGNAL PROCESSOR) used, but it corresponds to the channel characteristics within 1 second of data input time and 0.1-0.3 seconds. It is possible to calculate the filter coefficient for ghost removal and send it to the filter section.

Secondly, the sequential correction method is small in computation and simple in formula, easy to implement, and small in steady-state error, but has a convergence problem and takes a lot of time. For example, when using the LMS (LEAST MEAN SQUARE) method, which is one of the sequential correction methods, the filter coefficient is calculated for 1 second data input time, 2-4 seconds for short, and 10 seconds for long. Time is required.

If the LMS method is expressed by a formula, it is as follows.

From here,

In Equation (1), x [n] is a ghost removal reference signal input to the ghost elimination apparatus through the transmission path, and d [n] is an original ghost elimination reference signal previously stored in the ghost elimination apparatus. . And a0... aN-1 is a filter coefficient of the FIR filter whose number of filter coefficients is N1, and b1... bN2 is the filter coefficient of the IIR filter whose filter coefficient is N2. In addition, PG is the number of filter coefficients for removing pre-ghost from the FIR filter, and μ is a gain constant (GAIN CONSTANT) related to convergence stability and speed.

Typically, the period of one line in a video signal is 1 / 15.734264 KHz = 63.55556 μS, and the sampling period of the AD converter 10 is four times the frequency of the subcarrier Fsc = 3.579545 MHz, and 1 / (3.579545E6 × 4) = Sampling one line at 0.06984127871 μS yields 910 samples of discretization data. The discretized ghost removal reference signal (a2-a1 + 1 shown in FIG. 4A) becomes 336 samples.

The conventional LMS method takes a long time since n performs LMS on all 910 data from 0 to 909 in Equation 1 above.

Accordingly, the present invention has been made in view of the above-described problems, and an object thereof is to provide an improved LS method for performing LMS only on ghosts.

In order to achieve the above object, the present invention, in the LS method for repeatedly updating the filter coefficients using the sampled data to calculate the filter coefficients for removing the ghost, the start position and end of the ghost removal reference signal Checking the position and finding the position of the removable ghost, detecting the position of the long ghost existing at the outermost position from the end position of the ghost removal reference signal, and the outermost from the position of the sun ghost. It provides an improved LMS (LMS) method consisting of performing the LMS to the position of the long ghost present in.

Other objects and various advantages of the present invention will become more apparent from the preferred embodiments of the invention described below with reference to the accompanying drawings by those skilled in the art.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

3 is a flowchart showing the operation of the LMS corresponding to the rapidly changing ghost according to the present invention, which will be described in more detail with reference to FIGS.

First, as shown in FIG. 4A, among the 910 sampling data, the start position a1 and the end position a2 of the ghost elimination reference signal A (sample number 336) are checked (step 310), and as shown in FIG. 4B. As such, the removable positions a1-d1 of the sunghost are detected (step 320).

In general, the range of LMS performance of the sunghost is obtained by subtracting 32 taps (TAB) from the start position of the ghost removal reference signal.

Then, it is checked whether there is a long ghost (step 33), and the end position of the long ghost existing at the outermost position is detected from the end position of the ghost removal reference signal (step 340), as shown in FIG. 4C as an example. In the case of a long ghost, the end position of the long ghost is a2 + d2.

Thus, in the improved LMS method according to the present invention, the starting position of the LMS performance is a1-d1 and the ending position is a2 + d2, as can be seen from FIG. 4b. (Step 350), LMS is performed within this range (a1-d1 to a2 + d2) (step 360).

As described above, according to the present invention, since the update section of the LMS is adaptively determined according to the position of the ghost generated differently according to the characteristics of the channel, the filter coefficient can be calculated at a higher speed.

Claims (1)

In the LS method of repeatedly updating the filter coefficients using sampled data to calculate a filter coefficient for removing ghosts, the start and end positions of the ghost elimination reference signal are checked and then the Finding a position, and detecting a position of a long ghost existing at the outermost position from an end position of the ghost removal reference signal; And performing an LMS from the position of the sun ghost to the position of the long ghost which is located at the outermost side.