The DSP system (Digital Signal Processor system) is one of the commonly used electronic systems by many electronic engineers. To master the DSP system, the algorithms involved cannot be overlooked. Below, we will discuss the common algorithms and principles of DSP systems, hoping to help everyone.
1. Discrete Signals and Systems
Principle: Discrete signals and systems deal with discrete-time signals, which are signals that take values only at specific time points. DSP achieves digital representation and processing of signals by sampling and discretizing continuous-time signals.
Characteristics: High speed and efficiency, flexibility, and it is the core of modern communication and media technology.
2. IIR Filter Design
Principle: IIR (Infinite Impulse Response) filters have a feedback structure, where there is a feedback path between the input and output signals, and their operation can be described by difference equations.
Characteristics:
· Higher filtering efficiency, capable of achieving more complex filtering characteristics.
· Lower delay time, meeting the requirements for real-time signal processing.
· Smaller memory requirements, suitable for resource-constrained environments.
3. FIR Filter Design
Principle: FIR (Finite Impulse Response) filters’ output depends only on current and past input values, unaffected by future inputs. Its working principle involves convolving the input sequence with a set of numbers known as “coefficients” of the digital filter.
Characteristics:
· High stability, making it a reliable digital filter.
· Linear phase characteristics, which are very useful in certain applications.
· Can be designed as low-pass, high-pass, band-pass, and band-stop filters with any frequency response.
· Easy to implement, can be realized in both hardware and software.
4. Adaptive Filter Design
Principle: Adaptive filters utilize the filter parameters obtained in the previous moment to automatically adjust the current filter parameters to adapt to the unknown or time-varying statistical characteristics of signals and noise, achieving optimal filtering.
Characteristics:
· Can automatically adjust parameters based on environmental changes, suitable for scenarios where the statistical characteristics of signals and noise are unknown or varying.
· LMS (Least Mean Squares) algorithm is one of the commonly used adaptive filtering algorithms, with a simple structure and easy implementation.
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