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The range resolution is the minimal range separation between two objects traveling at the same speed before the radar can detect two discrete reflections: = (). In addition to this sampling limit, the duration of the transmitted pulse could mean that returns from two targets will be received simultaneously from different parts of the pulse.
Pulse-Doppler signal processing separates reflected signals into a number of frequency filters. There is a separate set of filters for each ambiguous range. The I and Q samples described above are used to begin the filtering process. These samples are organized into the m × n matrix of time domain samples shown in the top half of the diagram.
The difference between the sample numbers where reflection signal is found for these two PRF will be about the same as the number of the ambiguous range intervals between the radar and the reflector (i.e.: if the reflection falls in sample 3 for PRF 1 and in sample 5 for PRF 2, then the reflector is in ambiguous range interval 2=5-3).
In pulsed radar and sonar signal processing, an ambiguity function is a two-dimensional function of propagation delay and Doppler frequency, (,).It represents the distortion of a returned pulse due to the receiver matched filter [1] (commonly, but not exclusively, used in pulse compression radar) of the return from a moving target.
Pulse width also constrains the range discrimination, that is the capacity of the radar to distinguish between two targets that are close together. At any range, with similar azimuth and elevation angles and as viewed by a radar with an unmodulated pulse, the range resolution is approximately equal in distance to half of the pulse duration ...
Other difficulties arise when the interference covariance matrix is ill-conditioned, making the inversion numerically unstable. [5] In general, this adaptive filtering must be performed for each of the unambiguous range bins in the system, for each target of interest (angle-Doppler coordinates), making for a massive computational burden. [4]
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It avoids linearization by representing the mean and covariance information in the form of a set of points, called sigma points. These points, which represent a distribution with specified mean and covariance, are then propagated directly through the non-linear equations, and the resulting updated samples are then used to calculate a new mean ...