![]() ![]() H1((1:Npulsebuffsize) + (k-1)*Npulsebuffsize) = rcv_pulses(rangeidx,:).' Rcv_pulses = buffer(rcv_pulses(matchingdelay+1:end),size(rcv_pulses,1)) Rcv_pulses(:,m) = receiver(rcvsig,~(tx_status>0)) Rcv_pulses = zeros(length(sigtrans),Npulsebuffsize) This example shows a ROC curve generated by a Monte Carlo simulation of a single-antenna radar system and compares that curve with a theoretical curve. You can use the function rocsnr to compute theoretical ROC curves. If you specify Pd and Pfa, then you can determine how much power is needed to achieve this requirement. If the arriving signal SNR is known, then the ROC curve shows how well the system performs in terms of Pd and Pfa. The shape of a ROC curve depends on the received SNR of the signal. The simulation computes Pd and Pfa are by counting the proportion of signal values in each case that exceed the threshold.Ī ROC curve plots Pd as a function of Pfa. The Monte Carlo simulation generates a very large number of radar returns with and without a target present. In this case, the signal is due to noise and its properties depend on the noise statistics. The probability of false alarm ( Pfa) is the probability that the signal value is larger than the threshold when a target is absent. The probability of detection ( Pd) of a target is the probability that the instantaneous signal value is larger than the threshold whenever a target is actually present. A detection system will declare presence or absence of a target by comparing the received signal value to a preset threshold. The receiver operating characteristic determines how well the system can detect targets while rejecting large spurious signal values when a target is absent (false alarms). To improve the performance of your Monte Carlo simulations, you can distribute the computations to run in parallel on multiple cores using Parallel Computing Toolbox™ and MATLAB Parallel Server™.This example shows how to generate a receiver operating characteristic (ROC) curve of a radar system using a Monte Carlo simulation. Running Monte Carlo Simulations in Parallel Simulink Design Optimization™ provides interactive tools to perform this sensitivity analysis and influence your Simulink model design. Monte Carlo simulations help you gain confidence in your design by allowing you to run parameter sweeps, explore your design space, test for multiple scenarios, and use the results of these simulations to guide the design process through statistical analysis. The design and testing of these complex systems involves multiple steps, including identifying which model parameters have the greatest impact on requirements and behavior, logging and analyzing simulation data, and verifying the system design. You can model and simulate multidomain systems in Simulink ® to represent controllers, motors, gains, and other components. Risk Management Toolbox™ facilitates credit simulation, including the application of copula models.įor more control over input generation, Statistics and Machine Learning Toolbox™ provides a wide variety of probability distributions you can use to generate both continuous and discrete inputs. Financial Toolbox™ provides stochastic differential equation tools to build and evaluate stochastic models. In financial modeling, Monte Carlo Simulation informs price, rate, and economic forecasting risk management and stress testing. MATLAB is used for financial modeling, weather forecasting, operations analysis, and many other applications. MATLAB ® provides functions, such as uss and simsd, that you can use to build a model for Monte Carlo simulation and to run those simulations. ![]()
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