Download PDF by Danilo Orlando, Francesco Bandiera, Giuseppe Ricci: Advanced Radar Detection Schemes Under Mismatched Signal

By Danilo Orlando, Francesco Bandiera, Giuseppe Ricci

ISBN-10: 1598298410

ISBN-13: 9781598298413

Adaptive detection of indications embedded in correlated Gaussian noise has been an lively box of analysis within the final a long time. This subject is necessary in lots of components of sign processing reminiscent of, simply to supply a few examples, radar, sonar, communications, and hyperspectral imaging. lots of the present adaptive algorithms were designed following the lead of the derivation of Kelly's detector which assumes ideal wisdom of the objective steerage vector. in spite of the fact that, in practical situations, mismatches are inclined to happen because of either environmental and instrumental elements. whilst a mismatched sign is found in the knowledge lower than try, traditional algorithms may possibly endure serious functionality degradation. The presence of robust interferers within the telephone less than try out makes the detection job much more difficult. a good way to deal with this state of affairs depends on using "tunable" detectors, i.e., detectors able to altering their directivity throughout the tuning of right parameters. the purpose of this publication is to provide a few contemporary advances within the layout of tunable detectors and the focal point is at the so-called two-stage detectors, i.e., adaptive algorithms received cascading detectors with contrary behaviors. We derive certain closed-form expressions for the ensuing likelihood of fake alarm and the likelihood of detection for either matched and mismatched indications embedded in homogeneous Gaussian noise. It seems that such options warrantly a large operational diversity by way of tunability whereas holding, even as, an performance in presence of matched indications commensurate with Kelly's detector. desk of Contents: creation / Adaptive Radar Detection of ambitions / Adaptive Detection Schemes for Mismatched indications / superior Adaptive Sidelobe Blanking Algorithms / Conclusions

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23) with S = S + rr † , namely K + 1 times the sample covariance matrix based on the CUT and secondary data. The resulting receiver is a special case of the AMF with De-Emphasis (AMFD), introduced by Richmond in [41], with the de-emphasis parameter equal to 1. 11) [18] and for this reason will be referred to in the following as RAO. 10 we compare the RAO to the W-ABORT for different values of N and K. The figures highlight that the RAO is more selective than the W-ABORT for N = 16, K = 32, but the W-ABORT is to be preferred (in terms of selectivity) for N = 16, K = 48 and also for N = 30, K = 60.

2 ROBUST RECEIVERS Receivers belonging to this class provide good detection performance in presence of sensibly mismatched signals. 11). 21). 9), assuming N = 16, K = 32, Pf a = 10−4 , and different values of cos2 θ . The mismatched signal detection performance of a receiver can also be analyzed inspecting a 2D-graph, wherein the contours of constant Pd are represented as a function of the squared cosine of the mismatch angle, plotted vertically, and the SNR, plotted horizontally. Such plots were introduced in [4], where they are referred to as mesa plots.

15 illustrates the decision regions. The tuning capability is provided by the infinite number of threshold pairs that ensure the same value of Pf a ; more precisely, a proper selection of the two thresholds allows to adjust directivity. 16 shows typical contours of constant Pf a (or iso-Pf a contours) as function of the two thresholds. In order to increase the resulting robustness (or selectivity), it is necessary to choose threshold pairs moving toward the robust (or selective) stage on the iso-Pf a contour.

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Advanced Radar Detection Schemes Under Mismatched Signal Models (Synthesis Lectures on Signal Processing) by Danilo Orlando, Francesco Bandiera, Giuseppe Ricci

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