DSP digital noise immunity module for airborne communication equipment

This paper introduces a digital anti-noise module based on a dedicated DSP chip and a unique software anti-noise algorithm, which achieves a voice clarity of not less than 98 in a 120-decibel noise environment. This module has been successfully used in airborne communication equipment in our country.

Abstract: This paper introduces a digital anti-noise module based on a dedicated DSP chip and a unique software anti-noise algorithm, which achieves a voice clarity of not less than 98 in a 120-decibel noise environment. This module has been successfully used in airborne communication equipment in our country.


The current third-generation anti-noise products in China use dynamic noise reduction (DNR) technology. DNR technology dynamically adjusts the output voice switch by changing the voice peak value, so as to achieve the purpose of noise reduction. Although it is a better anti-noise analog processing technology at present, it also has some limitations, including light note dropping and strong noise trailing; the noise reduction effect is more focused on low frequencies; the noise reduction is completely realized by hardware circuits, Debugging and maintenance are more troublesome and other issues. Due to these problems, the mass promotion and application of analog DNR noise reduction products is limited. With the rapid development of digital signal processing technology, products with digital anti-noise technology supported by digital signal processors and their related algorithms are emerging. The digital anti-noise module proposed in this paper is to use modern digital signal processing (DSP) technology and its high-speed real-time processing and operation characteristics, and use corresponding software algorithms to process the voice and noise in a high-noise environment, and complete the high-noise environment. Voice communication function.

The performance benefits of this module include:

a) The software adopts an adaptive filtering algorithm. The noise suppression of the digital anti-noise processor is generally more than 50 decibels, and the output voice is stable, without missing words and noise tailing.

b) Digital anti-noise processor equalizes noise reduction in the entire voice band (300~3000Hz).

c) The digital anti-noise processor can meet different anti-noise requirements by changing the software algorithm, which is convenient for product upgrading.

d) Hardware cost is lower than analog DNR products.

e) Using software encryption technology, the product is not easy to be infringed and imitated, which is beneficial to protect the interests of the manufacturer.

Main indicator requirements and overall solution ideas

As part of the JK-DP10 digital anti-noise processing module, this digital anti-noise processing module is mainly used for communication in noisy environments such as airborne communication terminal equipment. The noise reduction performance is as follows: when a 3mV, 2-second intermittent sine wave signal is added to the input of the module (the frequencies are 300Hz, 700Hz, 1000Hz, 1500Hz, 2000Hz, 2500Hz, 3000Hz) and a 3mV, 120dB continuous white noise signal is added, the module The difference between the output levels is not less than 50dB.

First of all, we must choose a suitable DSP device. It is required to have low power consumption, high-speed data operation and throughput (above 40 MIPS), including A/D, D/A, and Flash memory (16KB). Then an effective noise model is established, and an adaptive filtering structure and related software algorithms are designed. Next, the electromagnetic compatibility (EMC) of the digital anti-noise processor is designed, and the anti-noise microphone device that can adapt to 120dB environmental noise is selected. The combination of DSP hardware and related software algorithms enables the digital anti-noise processor to achieve a voice clarity of not less than 98 in a 120dB high-noise environment.

DSP digital noise immunity module for airborne communication equipment

figure 1

Software and hardware design

Main working principle

This processor mainly completes the high-definition communication function of speech in high-noise environment. The voice signal and ambient noise are input to the preamplifier stage through the MIC. The function of the preamplifier stage is to amplify the voice and ambient noise to the amplitude that the A/D in the dedicated DSP chip can recognize, so that the A/D can convert the signal normally. After the analog signal is converted into a 12-bit digital signal through A/D, it enters the operation unit of the DSP. The DSP completes the measurement of the surrounding noise in the first 3 seconds and establishes a mathematical model, and then processes the voice and noise according to the given algorithm. , send the processing result to the D/A through the data bus, and then send it to the post-stage amplifier after smoothing and filtering. The function of the post-stage amplifier is to meet the input requirements of the associated equipment.

DSP chip selection

The JK-DP10 digital anti-noise processor designed in this paper has higher requirements on the digital signal processor chip. The chip must not only have strong real-time processing performance, but also high computing speed and data throughput; it also requires low power consumption. Reduce product volume. Therefore, one of the TMS320C5XX series DSP chips is selected as the processing chip, and externally equipped with high-speed A/D, D/A and 32KB Flash for program loading.

Software algorithm scheme

The digital anti-noise processor is implemented by an adaptive filter. The adaptive filter has the ability to track the changes of signal and noise, so that the characteristics of the filter also change with the change of signal and noise, so as to achieve the optimal filtering effect.

The characteristic change of the adaptive filter is realized by the adaptive algorithm by adjusting the filter weight coefficient. In general, an adaptive filter consists of two parts, one is the filter structure, and the other is an adaptive algorithm for adjusting the filter coefficients. The structure of the adaptive filter adopts the FIR structure. For the processing of in-band white noise, the classical LMS algorithm cannot achieve the optimal noise reduction effect. It is necessary to use the autocorrelation characteristics and power spectral density characteristics of the noise to make appropriate adjustments on the basis of the LMS algorithm to achieve the best noise reduction effect. Noise reduction effect.

Figure 1 shows the DSP implementation structure of the digital anti-noise microphone group.

The original input signal d(n) includes signal and noise, and x(n) is the reference noise input. This adaptive filter essentially completes the noise estimation in d(n), and subtracts the estimated value y(n) from the original channel to achieve the result of noise cancellation. Of course, the estimated value y(n) is different from the original input. Signals are not simple algebraic subtraction, but have a set of corresponding software algorithms, such as power spectrum analysis of correlated power.

In Figure 1, the adaptive filter adopts a horizontal structure, and the output y(n) of the filter is expressed as:


y(n)= ∑ Wi (n- i)

i =0

N is the order of the filter.

software design

The complexity of an adaptive filter implementation is usually measured by the number and order of multiplications it requires. The data throughput and data processing speed of the DSP chip of the adaptive filter system based on DSP are also very important. This digital anti-noise processor adopts a 120-order adaptive digital filter, and selects a DSP chip with an operation speed of 40MIPS as the main processor. Because the DSP chip contains A/D and D/A and 16KB flash memory, these On-chip resources make the implementation of adaptive filters more efficient.

According to the autocorrelation characteristics and power density of the noise, in addition to using the LMS algorithm in the FIR filter with the traditional symmetrical transverse structure, the software also estimates the power spectral density of the noise and signal, that is, the 16 values ​​of the sampling code Perform square accumulation to find its average power value, compare it with the power value of the previous sample point, and divide the difference after the comparison with the set noise threshold value. If the result is greater than 1, adjust the weight coefficient of the filter to change. Small, the signal output amplitude becomes larger, if the result is less than or equal to 1, the weight coefficient of the filter becomes larger, and the signal output amplitude becomes smaller.

Customized dedicated anti-noise DSP chip

After the debugging work is completed, it is handed over to a company specializing in the production of DSP chips to make a special DSP chip with anti-noise function. After actual measurement, the power consumption of the whole machine is not more than 70mA, and the pins of the DSP chip are reduced to 64 pins, which greatly reduces the area of ​​the printed circuit board. Since the software code is masked in the chip at one time, the trouble of writing the code every time is avoided, and the workload of debugging is reduced. Under normal circumstances, the module can be completed with only 3 debugging points, which greatly reduces the debugging cost and is conducive to mass production.


The digital anti-noise module uses DSP chips and uses adaptive technology, which not only improves the anti-noise performance of communication products, but also reduces production costs. The module has been successfully applied to airborne communication equipment in my country.

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