Digital signal processing (DSP) refers to several techniques for improving the accuracy and reliability of digital communications. The theory behind DSP is quite complex. Basically, DSP works by clarifying, or standardizing, the levels or states of a digital signal. The ADSP circuit is capable of differentiating between human signals, which are ordered, and noise, which is inherently chaotic.
All communications circuits contain some noise. This is true if the signals are analog or digital, and regardless of the type of information transmitted. Noise is the eternal bane of communications engineers, who are always struggling to find new ways to improve signal-to-noise communications systems. Traditional methods of optimizing the S / N ratio include increasing the power of the transmitted signal and increasing the sensitivity of the receiver. (In wireless systems, specialized antenna systems can also help.) Digital signal processing drastically improves the sensitivity of a receiving unit. The effect is most noticeable when the noise competes with a desired signal. A good DSP circuit can sometimes seem like an electronic miracle worker. But there are limits to what you can do. If the noise is so strong that all traces of the signal are obliterated, a DSP circuit can not find any order in the chaos, and no signal will be received.
If an input signal is analog, for example a standard television station, the signal is first converted to digital form by an analog-to-digital converter (ADC). The resulting digital signal has two or more levels. Ideally, these levels are always predictable, accurate or current voltages. However, because the incoming signal contains noise, the levels are not always in the standard values. The DSP circuit adjusts the levels to the correct values. This virtually eliminates noise. The digital signal is converted back to analog via a digital-to-analog converter (DAC).
If a received signal is digital, eg computer data, then the ADC and the DAC are not needed. The DSP acts directly on the incoming signal, eliminating the irregularities caused by the noise, thus minimizing the number of errors per unit time. Digital signal processing (DSP) is the use of digital processing, such as computers, to perform a wide variety of signal processing operations. Signals processed in this way are a sequence of numbers representing samples of a continuous variable in a domain such as time, space, or frequency.
Digital signal processing and analog signal processing are signal processing subfields. DSP applications include audio and voice signal processing, sonar, radar and other sensor processors, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, systems control, biomedical engineering, data processing Seismic, among others.
Digital signal processing may involve linear or nonlinear operations. The processing of non-linear signals is closely related to the identification of non-linear systems and can be implemented in time, frequency and spatio-temporal domains.
The application of digital computing to signal processing allows many advantages over analog processing in many applications, such as detection and correction of transmission errors, as well as data compression. DSP is applicable to both transmission data and static (stored) data.