Amplitude Comparison Monopulse Radar Research Articles

A Study on the Amplitude Comparison Monopulse Algorithm

This paper presents two amplitude comparison monopulse algorithms and their covariance prediction equation. The proposed algorithms are based on the iterated least-squares estimation method and include the conventional monopulse algorithm as a special case. The proposed covariance equation is simple but predicts RMS errors very accurately. This equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar, and is also applicable to the conventional monopulse algorithm. The proposed algorithms and covariance prediction equations are validated by the numerical simulations with 100,000 Monte Carlo runs.

Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation

Amplitude-comparison monopulse radar in tracking radar uses the tracking scheme of a monopulse radar to estimate the angle components of a target. The performance of the amplitude comparison monopulse radar under measurement uncertainty is analysed. Measurement noises are modelled as Gaussian random variables. Taylor series expansion is adopted to get analytic expression of the mean square error (MSE). Estimation accuracy, in terms of the MSEs for estimate the direction-of-arrival (DOA) estimation algorithm, is usually obtained from the Monte Carlo simulation, which can be computationally intensive especially for large number of repetitions in the Monte Carlo simulation. To get reliable MSE in the Monte Carlo simulation, the number of repetitions should be very large, which implies that there is a trade-off between reliability of the MSE and computational burden in the Monte Carlo simulation. This paper shows the performance of amplitude comparison monopulse radar by linear approximation of nonlinear equations to estimate the DOA. The performance of amplitude comparison monopulse radar is quantitatively analysed via the MSEs, and the derived expression is validated by comparing the analytic MSEs with the simulation based MSEs. In addition, it is shown in the numerical results that analytically derived MSE is much less computationally intensive in comparison with the Monte Carlo simulation-based MSE, which implies that the proposed scheme in this paper results in drastic reduction in computational complexity for evaluation of the MSE.

Joint detection and tracking of unresolved targets with monopulse radar

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. A new algorithm is developed to jointly detect and track more than two unresolved targets from a single detection with the help of tracking information. That is, the method involves the use of tracking information in the detection process. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearities. In order to handle the nonlinearities, the particle filter, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard particle filtering steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Bayesian model selection is then used to find the correct detection event. The corresponding particle set is taken as the correct representation of the target posterior. A simulated amplitude comparison monopulse radar is used to generate the data and validate the joint detection and tracking of more than two unresolved targets.

The probability density function of the monopulse ratio for n looks at a combination of constant and Rayleigh targets

The exact probability density function for the "monopulse ratio, of an amplitude-comparison monopulse radar is presented in closed form. The analysis is valid for multiple looks at any combination of fixed targets, pulse-to-pulse independently fluctuating targets, and receiver noise. The average receiver noise powers in the difference and sum channels need not be equal. Arbitrary signal-to-noise ratio, arbitrary monopulse ratio versus angle characteristic, and arbitrary locations of the targets and jammers in the beam are accommodated. Plots of the density function for various signal-to-noise ratios and off-axis location of the targets are included. A linear approximation is introduced, and the exact results are compared to the linear theory when the signal-to-noise ratio is "small."

Angular jitter in an amplitude comparison monopulse radar due to turbulence

Calculations are presented of the bias and spread of the angle estimator of an amplitude comparison monopulse radar caused by propagation through a layer that contains random index of refraction fluctuations. These fluctuations, caused by irregularities between a target and a receiver, bias the angle estimator towards boresight and cause a spread in the measurements of both on- and off-boresight angles. To illustrate the general results simplified expressions are given for Gaussian antenna patterns, targets near boresight, and a simple power-law irregularity spectrum. Comparisons with other models of angular jitter show that the mean square angular fluctuation observed with an amplitude comparison monopulse radar is closely related to the angular fluctuation, which is predicted when the averaging effect of a radar aperture is considered.

Monopulse Radars Excited by Gaussian Signals

This paper presents the theoretical probability densities of the outputs of both an amplitude-comparison monopulse radar and a phase-comparison monopulse radar when the monopulse radars are excited by Gaussian signals plus Gaussian noises. These probability densities are useful for studying the responses of monopulse radars to noise excitations. For example: Noise excitations arise when the monopulse radars are ?viewing? a noise source or a radar target consisting of randomly moving scatterers. The probability densities also serve as useful approximations for characterizing the outputs of monopulse radars when sinusoidal signals plus Gaussian noises excite the monopulse radars. Some special cases of the probability densities are presented in graphs.