Detection, Range and Doppler Estimation
Detection is an important part of a radar or sonar system. To improve detection capabilities, the Phased Array System Toolbox™ includes System objects for performing matched filtering and stretch-processing pulse compression, coherent and noncoherent pulse integration, range and Doppler estimation and constant false alarm rate (CFAR) detection for one or two dimensions. Utility functions let you compute and visualize receiver operating characteristic (ROC) curves for various signal-to-noise ratio (SNR) levels or probabilities of false alarm.
Categories
- Detection
Target detection, CFAR, 2-D CFAR, ROC curves, sonar equation
- Localization
TIme-of-arrival position estimation
- Range and Doppler Estimation
Range and Doppler estimation, range-Doppler and range-angle processing, and FMCW range estimation
- Visualization
Visualize data using RTI and DTI displays
Featured Examples
Constant False Alarm Rate (CFAR) Detection
Introduces constant false alarm rate (CFAR) detection and shows how to use CFARDetector and CFARDetector2D in the Phased Array System Toolbox™ to perform cell averaging CFAR detection.
Estimate Range and Doppler Using Pulse Compression
Use pulse compression to improve detection and range and Doppler estimation.
Range Estimation Using Stretch Processing
Estimate the range of a target using stretch processing in a radar system that uses a linear FM pulse waveform.
Doppler Estimation
A monostatic pulse radar detecting the radial velocity of moving targets at specific ranges. The speed is derived from the Doppler shift caused by the moving targets. We first identify the existence of a target at a given range and then use Doppler processing to determine the radial velocity of the target at that range.
Receiver Operating Characteristic (ROC) Curves Part I: Theoretical Performance Analysis
Assess the performance of both coherent and noncoherent systems in an additive complex white Gaussian noise environment using receiver operating characteristic (ROC) curves.
Receiver Operating Characteristic (ROC) Curves Part II: Monte Carlo Simulation
Generate an ROC curve using a Monte-Carlo simulation.
Detection of Unknown Signals
Design an energy detector to detect an unknown signal embedded in ambient noise.
Detection of Known Signals with Coherent and Non-Coherent Receivers
Use a matched filter to detect a deterministic signal in complex, white, Gaussian noise.
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