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The discussed methods are confirmed by simulation studies and experimental array systems developed by the authors team at FGAN, now Fraunhofer. We will send you an SMS containing a verification code. Please double check your mobile number and click on "Send Verification Code". Enter the code below and hit Verify.

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Radar Techniques Using Array Antennas

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Application of optical dispersion techniques in phased array antenna beam steering Abstract: Active phased array antennas play an important role in modern radar systems. You can compare the effect of perturbations on the array performance before and after the calibration. Similar to what was described earlier, we start with an array that has amplitude, phase, and element position distortions Fig.

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The goal is to preserve the signal of interest while suppressing the interference sources. Figure 5 compares the expected beam pattern with the pattern that results from applying the designed weights on both the ideal and the perturbed array. The pattern generated using the uncalibrated weights does not satisfy the requirements because a null occurs around the desired 10 degrees azimuth direction. This means that the desired signal can no longer be retrieved. The plot on the right side of Figure 5 shows the calibrated response, which does preserve the desired signal.

With this technique, opportunistic sources are used to simultaneously estimate array-shape perturbations and source directions.

Radar Techniques Using Array Antennas, 2nd Edition. (eBook, ) [unmipersand.tk]

Self-calibration is more challenging to solve in general, as there are more unknowns. With self-calibration also called autocalibration , perturbations are estimated jointly with the positions of various external sources at unknown locations.


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  6. Unlike pilot calibration, this approach results in a small number of signal observations with a large number of unknowns. One way to solve this problem is to construct and optimize against a cost function.

    These cost functions tend to be highly nonlinear and contain local minima. The cost function and optimization algorithm are chosen to achieve a solution as easily and quickly as possible. In addition, parameters associated with the optimization algorithm must be tuned for the given scenario.

    A number of cost function and optimization algorithms exist in the literature. As a scenario changes, you may need to adapt the approach used depending on the robustness of the calibration algorithm.