Generate a receiver operating characteristic (ROC) curve of a radar system using a Monte-Carlo simulation. The receiver operating characteristic determines how well the system can
Visualize the speed and range of a target in a pulsed radar system that uses a rectangular waveform.
In the Neyman-Pearson framework, the probability of detection is maximized subject to the constraint that the false-alarm probability does not exceed a specified level. The false-alarm
Compute empirically the probability of false alarm for a real-valued signal in white Gaussian noise.
Create a CFAR detector and test its ability to adapt to the statistics of input data. The test uses noise-only trials. By using the default square-law detector, you can determine how close the
Compare the probability of detection resulting from two CFAR algorithms. In this scenario, the order statistic algorithm detects a target that the cell-averaging algorithm does not.
Empirically verify the probability of false alarm in a system that integrates two real-valued pulses. In this scenario, each integrated sample is the sum of two samples, one from each pulse.
Empirically verify the probability of false alarm in a system that uses coherent detection of complex-valued signals. Coherent detection means that the system utilizes information about
To illustrate coherent-on-receive, construct a rectangular pulse waveform with five pulses. The waveform pulse repetition frequency (PRF) is 10 kHz and the pulse width is 50 μs. The pulse
Receiver Operating Characteristic (ROC) curves present graphical summaries of a detector's performance. You can generate ROC curves using the rocpfa and rocsnr functions.
The radarEquationCalculator is a Matlab™ App that lets you determine key radar characteristics such as detection range, required peak transmit power, and SNR. The App works for monostatic
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
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.
Detect a signal in complex, white Gaussian noise using multiple received signal samples. A matched filter is used to take advantage of the processing gain.
Assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves. It assumes the detector operates in an additive complex white
Discusses the detection of a deterministic signal in complex, white, Gaussian noise. This situation is frequently encountered in radar, sonar and communication applications.