Adaptive Radar Resource Management by Peter Moo, Zhen Ding

By Peter Moo, Zhen Ding

Radar source administration (RRM) is key for optimizing the functionality of recent phased array radars, that are the first sensor for airplane, ships, and land systems. Adaptive Radar source administration provides an creation to radar source administration (RRM), providing a transparent evaluation of alternative methods and strategies, making it very compatible for radar practitioners and researchers in and universities.

Coverage contains:

  • RRM’s function in optimizing the functionality of contemporary phased array radars
  • The merits of adaptivity in imposing RRM
  • The position that modelling and simulation performs in comparing RRM performance
  • Description of the simulation software Adapt_MFR
  • Detailed descriptions and function effects for particular adaptive RRM techniques
  • The in simple terms ebook totally devoted to adaptive RRM
  • A accomplished remedy of phased array radars and RRM, together with activity prioritization, radar scheduling, and adaptive music replace rates
  • Provides unique wisdom of particular RRM thoughts and their performance

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Example text

Note that longer update rates correspond with better Overview of RRM Techniques 35 solutions. 85 s, and the Kalman filter achieved an update rate of 1 s. The H-infinity filter also resulted in an update rate of about 1 s. 3 s. 5 s. 3 s. 4 s. IMM-MHT results have been published, but there was no detailed results comparison. Computationally, IMM-MHT takes five times longer than the IMMPDAF. 3 A Solution to Benchmark 3 Benchmark 3 was not publicly available, and there is only one publication to date [110].

Estimate the level of the dynamic process noise. The update interval is determined so that the number of track updates per unit time with respect to beam-positioning losses can be reduced. Leung [74] proposed an estimator by solving a formulated dynamic optimization problem using a Hopfield NN. However, the Hopfield NN is not practical due to its low probability of finding the global minimum solution. Sun-Mog and Young-Hum [75] considered optimal scheduling of track updates to minimize the radar energy, a nonlinear optimal control problem.

4 PERFORMANCE COMPARISON .......................................... 49 This chapter compares the performance of an adaptive radar resource management (RRM) technique to that of a nonadaptive technique, using modeling and simulation. 1 specifies the metrics that are computed to quantify performance. 2 describes the simulation tool Adapt_MFR that is used in the comparison. 3, the adaptive RRM technique is described, and the details of its adaptive prioritization, scheduling and track update intervals are quantified.

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