Association of DF Bearing Measurements with Radar Tracks

The problem of associating direction finding (DF) bearingmeasurements with radar tracks is formulated as a multiplehypothesis testing problem. A simple decision rule for associating aset of DF bearing measurements with no radar track or one of mpossible radar tracks was developed using a combination of Bayesian and Neyman-Pearson approaches. The decision algorithmwas checked using both computer simulations and experimentaldata. Finally, a multiplatform algorithm was formulated and tested,using a combination of real and synthetic data.     

Radar Properties of Non-Rayleigh Sea Clutter

Measurements of sea clutter at low grazing angles using high- resolution radar show that the probability density p(x) of envelope detected sea clutter is not Rayleigh. Using the composite surface scattering model, a special varying clutter density p(x|?0) is proposed and is used to explain the non-Rayleigh nature of clutter. Since the clutter distribution has an enormous effect on the performance of a radar, the variation of the clutter densities, p(x) and p(x|?0), with various radar parameters such as frequency, pulsewidth, and polarization is found. Finally, a simulation of the composite surface scattering model is performed, and it verifies the effect of the various parameters on p(x).     

Detection Results for Scanning Radars Employing Feedback Integration

A method is presented for the calculation of the detection probabilities for a scanning radar employing feedback integration. An equation is developed for the optimum feedbacd value? the equation holds for small and large sample sizes and for fluctuating and nonfluctuating targets. Then, a value for the antenna beam-shape factor is given which enables one to calculate detection probabilities.     

Range Resolution of Targets

The problem of resolving targets in range is formulated as a hypothesis-testing problem. A generalized likelihood approach is developed and very good results are obtained, e. g., targets separated by more than a pulsewidth can almost always be resolved, Specifically, simulations showed that when using a sampling rate of 1.5 samples per pulsewidth, two 20 dB nonfluctuating targets can be resolved at a resolution probability of 0.9 and at a false-alarm probability of 0.01 at separations varying between ? and ? of a pulsewidth, depending on the relative phase difference between the targets. The effect of increasing the sampling rate was investigated and it appears that there is only marginal benefit in increasing the sampling rate beyond 1.5 samples per pulsewidth. Finally, the generalized likelihood approach was compared to several easily implemented adhoc approaches and an adhoc approach involving fitting a pulse shape to the data is only slightly (approximately 10%) less accurate than the likelihood approach.     

Comparison of Two Scanning Radar Detectors: The Moving Window and the Feedback Integrator

A comparison is made between the moving window and feedback integrator detectors for a scanning radar. Detection probabilities and position estimates are calculated by using a Monte Carlo simulation. The moving window is a slightly better detector than the feedback integrator: it provides 0.5-dB better detection capability and 8 percent more accurate estimate of position. However, the feedback integrator can be implemented more easily.     

Range Resolution of Targets Using Automatic Detectors

When two targets are closely separated in range, automatic detectors will declare the presence of only one target. To increase the-probability of resolving targets in range, log video should be used and the threshold should be of the form T = ? + F, where the mean ? is the smaller of the two means calculated from reference cells on either the greater range side or the lesser range side of the test cell and F is a fixed number. When the adjacent-detection merging algorithm is used, the probability of resolving targets does not rise above 0.9 until the targets are separated in range by 2.5lse-pulsewidths.