Misurazioni e statistiche
R2024bÈ possibile utilizzare blocchi e System object DSP System Toolbox™ per misurare le statistiche in movimento e le statistiche stazionarie dei segnali in MATLAB® e Simulink®. Le statistiche in movimento si riferiscono alle statistiche dei segnali di streaming che cambiano nel tempo. Nel metodo della finestra scorrevole per il calcolo delle statistiche in movimento, una finestra di lunghezza specificata si sposta sui dati, campione per campione, man mano che entrano nuovi dati. Gli oggetti e i blocchi calcolano le statistiche dei dati all'interno di questa finestra. Il metodo di ponderazione esponenziale applica una serie di pesi ai campioni di dati ed elabora i dati ponderati. Questi pesi sono calcolati ricorsivamente in base all'età dei dati. Per le statistiche stazionarie, i blocchi e gli oggetti calcolano le statistiche di tutti i dati disponibili in un batch.
Oggetti
Blocchi
 | Median Filter | Median filter |
 | Moving Average | Moving average |
 | Moving Maximum | Moving maximum |
 | Moving Minimum | Moving minimum |
 | Moving RMS | Moving root mean square (RMS) |
 | Moving Standard Deviation | Moving standard deviation |
 | Moving Variance | Moving variance |
 | Power Meter | Measure power and the CCDF of the power of voltage signal in Simulink |
 | Autocorrelation | Autocorrelation of N-D array |
 | Correlation | Cross-correlation of two inputs |
 | Maximum | Maximum values of input or sequence of inputs |
 | Mean | Find mean value of input or sequence of inputs |
 | Median | Median value of input |
 | Minimum | Minimum values of input or sequence of inputs |
 | RMS | Root mean square value of input or sequence of inputs |
 | Sort | Sort input elements by value |
 | Standard Deviation | Standard deviation of input or sequence of inputs |
 | Variance | Variance of input or sequence of inputs |
 | Histogram | Histogram of input or sequence of inputs |
Argomenti
Statistiche di movimento
- What Are Moving Statistics?
Learn how moving statistics are calculated. - Sliding Window Method and Exponential Weighting Method
Learn the differences between the sliding window method and exponential weighting method. - How Is a Moving Average Filter Different from an FIR Filter?
Moving average filter is a special case of the FIR filter. - Measure Statistics of Streaming Signals
Compute the moving average of streaming signals using MATLAB functions and System objects. - Compute Moving Average of Noisy Step Signal
Compare the sliding window averaging method and the exponentially weighted averaging method in Simulink using the Moving Average block. - Compute Moving RMS of Noisy Step Signal
Compute moving RMS using both the sliding window method and the exponential weighting method. - Compute Moving Standard Deviation of Noisy Square Wave Signal
Compare the sliding window standard deviation method and the exponentially weighted standard deviation method in Simulink using the Moving Standard Deviation block. - Compute Moving Variance of Noisy Square Wave Signal
Compare the sliding window variance method and the exponentially weighted variance method in Simulink using the Moving Variance block.
Statistiche stazionarie
- Compute the Mean
Simulink model example to compute the mean using the Mean block. - Compute Mean Using Sliding Window
Model a sliding window using the Buffer block. The Mean block use this window to compute the mean. - Compute the Running Mean
Simulink model example to compute the running mean using the Mean block. - Compute the Maximum
Simulink model example to compute the maximum using the Maximum block. - Compute the Running Maximum
Simulink model example to compute the running maximum using the Maximum block. - Compute the Minimum
Simulink model example to compute the minimum using the Minimum block. - Compute the Running Minimum
Simulink model example to compute the running minimum using the Minimum block. - Compute RMS of Noisy Step Signal
Use the RMS block to compute the RMS of a noisy square wave signal. - Compute the Histogram of Real and Complex Data
Simulink model example that explains how the histogram bin boundaries are calculated based on the input. - Compute the Standard Deviation
Use the Standard Deviation block to compute the standard deviation. - Compute the Running Standard Deviation
Use the Standard Deviation block to compute the running standard deviation. - Compute the Variance
Use the Variance block to compute the variance.
Misurazioni di potenza
- Compute Power Measurements of Voltage Signal in Simulink
Compute average power, peak power, and peak-to-average power ratio of voltage signal. - Compute CCDF Measurements of Voltage Signal in Simulink
Compute relative power and probability, and plot the CCDF curve in Array Plot.
Applicazioni
- Remove High-Frequency Noise from Gyroscope Data
Remove high-frequency noise using a median filter. - Energy Detection in the Time Domain
Detect the event when the signal energy crosses a particular threshold value.
Supporto del segnale a dimensione variabile
- Variable-Size Signal Support DSP System Objects
List of System objects that support variable-sized signals in DSP System Toolbox.
Esempi in primo piano
Streaming Signal Statistics
Perform statistical measurements on an input data stream using DSP System Toolbox™ functionality available at the MATLAB® command line. Compute the signal statistics minimum, maximum, mean, variance and peak-to-RMS and the signal power spectrum density and plot them.
Generate Standalone Executable and Interact with It Using UDP
Generate a standalone executable for streaming statistics using MATLAB® Coder™ and tune the generated executable using a user interface (UI) that is running in MATLAB®.
