1、 Clarify the core application scenarios and signal characteristics

This is a prerequisite for selection, as the signal differences in different scenarios directly determine the key parameters of the filter.

To determine the signal type and frequency range, it is necessary to first clarify the properties of the two input signals: whether they are low-frequency signals (such as bioelectric signals 0.5-100Hz, temperature sensor signals), intermediate frequency signals (such as modulation signals corresponding to industrial control 4-20mA, audio signals 20Hz-20kHz), or high-frequency signals (such as RF sampling signals, high-speed data bus signals). At the same time, it is necessary to distinguish between useful signal frequencies and interference signal frequencies: for example, in vibration monitoring, the characteristic frequency of equipment failure may be between 500Hz-2kHz, while interference may be power frequency 50/60Hz or high-frequency noise; The useful frequency range of electroencephalogram (EEG) signals is 0.5-70Hz, with interference mostly from power frequency and electromyographic noise (>100Hz). For dual channel, it is also necessary to confirm whether the two signals are homologous differential signals (such as differential sensor inputs, which require common mode suppression) or independent signals (such as vibration signals from two different measuring points, which require independent filtering).

According to the Nyquist sampling theorem, the sampling rate should be greater than twice the highest frequency of the signal. In practical applications, it is usually taken as 2.5-4 times to ensure the filtering effect. The synchronous sampling capability of a dual channel filter is crucial: if strict time alignment is required for two signals (such as phase difference measurement, vector analysis), a filter that supports dual channel synchronous sampling must be selected to avoid sampling delay between channels; If low synchronization requirements are needed, asynchronous sampling models can be chosen.

Signal amplitude and dynamic range specify the amplitude range of the signal (such as mV level, V level) and whether there are large dynamic range fluctuations (such as impact signals in industrial scenarios). This determines the input range and number of bits of the filter (such as 12 bit, 16 bit, 24 bit ADC integrated filters). The higher the number of bits, the stronger the resolution of weak signals.

2、 Focus on the core performance indicators of the filter

The performance of digital dual channel filters directly affects the signal processing effect, and the following indicators need to be focused on:

The core types of matched digital filters include low-pass, high pass, band-pass, and bandstop, which need to be selected according to interference suppression requirements:

To preserve low-frequency useful signals and filter out high-frequency interference, choose a low-pass filter (such as bioelectric signal processing);

To preserve high-frequency features and filter out low-frequency drift, choose a high pass filter (such as vibration and shock signals);

To extract specific frequency band signals while filtering out high and low frequency interference, choose a bandpass filter (such as RF signal demodulation, fault characteristic frequency extraction);

To selectively suppress a fixed interference frequency (such as power frequency 50/60Hz), choose a band stop filter (also known as a trap).

The dual channel filter needs to support two independent configuration filter types (such as one low-pass, one band-pass) or two identical configurations, which need to match the application requirements.

Passband and stopband indicators

Passband cutoff frequency (fp): It is necessary to accurately cover the frequency range of the useful signal, and the smaller the attenuation (Ap) within the passband, the better. Generally, Ap ≤ 1dB is required to avoid distortion of the useful signal;

Stopband cut-off frequency (fs): It is necessary to cover the frequency range of the interference signal, and the larger the attenuation (As) within the stopband, the better. For example, when suppressing power frequency interference, As ≥ 40dB can effectively reduce the interference amplitude;

Transition band width: The transition band is the frequency range from the passband to the stopband. The narrower the width, the stronger the frequency selectivity of the filter, but the higher the algorithm complexity. Scenarios with high real-time requirements (such as industrial online monitoring) can accept slightly wider transition bands, while laboratory high-precision analysis scenarios require narrow transition bands.

Channel isolation and common mode rejection ratio (CMRR) are key indicators of dual channel filters, especially for differential signals or strong interference scenarios:

Channel isolation degree: refers to the degree of mutual interference between two signals. The higher the isolation degree (such as ≥ 80dB), the more it can avoid interference from one signal from entering the other;

Common mode rejection ratio (CMRR): For dual channel filters with differential inputs, the higher the CMRR (e.g. ≥ 100dB@50Hz ）The better the suppression effect on common mode interference (such as power frequency noise and ground loop interference), the more suitable it is for sensor signal processing in industrial sites.

The phase characteristics of different filtering algorithms vary greatly, directly affecting the phase fidelity of the signal

Linear phase filter (such as FIR filter): The phase within the passband is proportional to the frequency, and the signal has no phase distortion after filtering. It is suitable for scenarios where phase information needs to be preserved, such as phase difference measurement, acoustic positioning, and time series analysis of bioelectric signals;

Nonlinear phase filters (such as IIR filters): have significant phase distortion, but have low algorithm complexity and fast operation speed, making them suitable for scenarios with low phase requirements (such as simple amplitude monitoring and noise suppression).

In dual channel applications, if it is necessary to analyze the phase difference between two signals, a filter with consistent linear phase between the two channels must be selected to avoid measurement errors caused by phase shift between channels.

Real time performance and algorithm complexity

For scenarios with high real-time requirements, such as industrial control closed-loop and high-speed signal monitoring, IIR filters or lightweight FIR filters are preferred. These filters have low computational complexity and can run quickly on embedded devices, such as MCUs and FPGAs;

In laboratory high-precision analysis scenarios (without real-time pressure), modern filtering algorithms such as high-order FIR filters or wavelet transforms can be selected to achieve better frequency selectivity and phase characteristics.

At the same time, attention should be paid to the data throughput of the filter, which needs to meet the sampling rate requirements of dual channel signals and avoid data cache overflow.

3、 Match hardware and system compatibility

The forms of digital dual channel filters include independent modules (such as filter boards), filters integrated into acquisition cards, and software algorithmic filters (such as LabVIEW and MATLAB filter programs based on PCs):

For embedded applications in industrial sites, hardware filter modules are preferred as they have strong anti-interference capabilities and do not rely on the upper computer;

In laboratory testing scenarios, software algorithmic filters can be selected, with high flexibility and real-time parameter adjustment;

When connecting with existing acquisition systems, confirm the interface type of the filter (such as USB, Ethernet, etc.) SPI、I2C）， Ensure compatibility with controllers (MCU, PLC, industrial computer).

The working temperature range of the filter (such as -40 ℃ -85 ℃) and its ability to resist electromagnetic interference (such as compliance with EMC certification) need to be considered for power supply and environmental adaptability in industrial scenarios; Portable devices need to pay attention to low power consumption characteristics; The laboratory setting has lower environmental requirements.