Formas de adquirir la Nacionalidad

Number of Training Samples

FIGURE 3-19 Effect of sample support on output SINR loss for the multipath

interference scenario of example 3.1.

EXAMPLE 3.9

Adaptive Multipath Interference Mitigation

This is a repeat of example 3.1 with the notable exception of unknown interference statistics that must be estimated on the fly. As a consequence, an estimate of the covariance matrix is used in (3.5) for the whitening filter rather than the actual covariance, as was the case in Section 3.2.

Plotted in Figure 3-19 is the overall output SINR loss relative to the optimum for the short-pulse case of example 3.1 as a function of the number of independent samples used in (3.65). The results shown were based on 50 Monte Carlo trials (root mean square average) with a jammer-to-noise ratio of 50 dB and a small amount of diagonal loading to allow for inversion when the number of samples is less than 11 (positive semidefinite case).

It is interesting to note the rapid convergence and contrast this with SINR loss performance for adaptive beamforming, which is generally significantly slower because we are estimating the single dominant eigenvalue–eigenvector pair. For an authoritative examination of principal components estimation and convergence properties, see [29].

3.6.2 Dynamic MIMO Calibration

Perhaps the earliest MIMO radar techniques have their origins in transmit antenna array calibration [30, 31]. While techniques for estimating the receive array manifold using cooperative or noncooperative sources have existed for quite some time [30], methods for dynamically calibrating the transmit array manifold (e.g., AESAs) in situ are relatively recent developments [31].

Figure 3-20 provides an example of using MIMO techniques to dynamically cali-brate an AESA radar. Orthogonal waveforms are simultaneously transmitted from each transmit/receive site of an AESA (typically a single subarray AESA). A cooperative re-ceiver then decodes each individual signal, calculates the relative phases (or time delays), and transmits this information back to the radar. By repeating this process for different orientations, a detailed look-up table for the transmit steering vectors can be constructed

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114 C H A P T E R 3 Optimal and Adaptive MIMO Waveform Design

3.8 FURTHER READING

Further in-depth details and examples on optimum and adaptive MIMO waveform design, including knowledge-aided methods, can be found in [19]. Further details on the orthogonal MIMO approach can be found in [25].

3.9 REFERENCES

[1] M. A. Richards, et al., Principles of Modern Radar, Vol I: Basic Principles. Raleigh, NC:

SciTech, 2010.

[2] J. L. Lawson and G. E. Uhlenbeck, Threshold Signals, volume 24 of MIT Radiation Laboratory Series. New York: McGraw-Hill, 1950.

[3] J. R. Guerci, Space-time adaptive processing for radar. Norwood, MA: Artech House, 2003.

[4] D. K. Barton, Modern radar system analysis. Norwood, MA: Artech House, 1988.

[5] A. Papoulis, Signal analysis: McGraw-Hill New York, 1984.

[6] A. Papoulis, Circuits and Systems: A Modern Approach. New York: Holt, Rinehart and Win-ston, 1980.

[7] H. L. V. Trees, Detection, Estimation and Modulation Theory. Part I. New York: Wiley, 1968.

[8] R. A. Horn and C. R. Johnson, Matrix analysis. Cambridge [England] ; New York: Cambridge University Press, 1990.

[9] A. Papoulis and S. U. Pillai, Probability, random variables, and stochastic processes: McGraw-Hill New York, 1991.

[10] D. A. Pierre, Optimization Theory With Applications: Courier Dover Publications, 1986.

[11] J. R. Guerci and S. U. Pillai, “Theory and application of optimum transmit-receive radar,” in Radar Conference, 2000. The Record of the IEEE 2000 International, 2000, pp. 705–710.

[12] C. E. Cook and M. Bernfeld, Radar signals: Academic Press New York, 1967.

[13] M. A. Richards, Fundamentals of radar signal processing: McGraw-Hill, 2005.

[14] M. J. Lindenfeld, “Sparse frequency transmit-and-receive waveform design,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 40, pp. 851–861, 2004.

[15] S. U. Pillai, et al., “Optimal transmit-receiver design in the presence of signal-dependent interference and channel noise,” Information Theory, IEEE Transactions on, vol. 46, pp. 577–

584, 2000.

[16] D. J. Rabideau, “Closed Loop Multistage Adaptive Beamforming,” Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems and Computers, pp. 98–102, 1999.

[17] H. L. Van Trees and E. Detection, “Modulation Theory, Part II,” ed: New York: John Wiley and Sons, 1971.

[18] H. L. V. Trees, Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise: Krieger Publishing Co., Inc., 1992.

[19] J. R. Guerci, Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach. Norwood, MA: Artech House, 2010.

[20] R. A. Monzingo and T. W. Miller, Introduction to Adaptive Arrays: SciTech Publishing, 2003.

[21] R. Manasse, “The Use of Pulse Coding to Discriminate Against Clutter,” Defense Technical Information Center (DTIC), vol. AD0260230, June 7, 1961.

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3.10 Problems 115

[22] W. Y. Hsiang, “On the sphere packing problem and the proof of Kepler’s conjecture,” Internat.

J. Math, vol. 4, pp. 739–831, 1993.

[23] W. Gander, et al., “A constrained eigenvalue problem,” Linear Algebra Appl, vol. 114, pp. 815–

839, 1989.

[24] D. C. Youla and H. Webb, “Image Restoration by the Method of Convex Projections: Part 1?

Theory,” Medical Imaging, IEEE Transactions on, vol. 1, pp. 81–94, 1982.

[25] D. W. Bliss and K. W. Forsythe, “Multiple-input multiple-output (MIMO) radar and imaging:

degrees of freedom and resolution,” presented at the Signals, Systems and Computers, 2003.

Conference Record of the Thirty-Seventh Asilomar Conference on, 2003.

[26] B. D. Carlson, “Covariance matrix estimation errors and diagonal loading in adaptive arrays,”

Aerospace and Electronic Systems, IEEE Transactions on, vol. 24, pp. 397–401, 1988.

[27] A. M. Haimovich and M. Berin, “Eigenanalysis-based space-time adaptive radar: performance analysis,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 33, pp. 1170–1179, 1997.

[28] A. Farina and L. Timmoneri, “Real-time STAP techniques,” Electronics & Communication Engineering Journal, vol. 11, pp. 13–22, 1999.

[29] S. T. Smith, “Covariance, subspace, and intrinsic Cramer-Rao bounds,” Signal Processing, IEEE Transactions on, vol. 53, pp. 1610–1630, 2005.

[30] B. D. Steinberg and E. Yadin, “Self-Cohering an Airborne e Radio Camera,” Aerospace and Electronic Systems, IEEE Transactions on, vol. AES-19, pp. 483–490, 1983.

[31] J. Guerci and E. Jaska, “ISAT - innovative space-based-radar antenna technology,” in Phased Array Systems and Technology, 2003. IEEE International Symposium on, 2003, pp. 45–51.

[32] S. Coutts, et al., “Distributed Coherent Aperture Measurements for Next Generation BMD Radar,” in Sensor Array and Multichannel Processing, 2006. Fourth IEEE Workshop on, 2006, pp. 390–393.

[33] P. G. Grieve and J. R. Guerci, “Optimum matched illumination-reception radar,” ed: US Patent 5,175,552, 1992.

[34] J. R. Guerci and P. G. Grieve, “Optimum matched illumination waveform design process,”

ed: US Patent 5,121,125, 1992.

[35] A. Farina and F. A. Studer, “Detection with High Resolution Radar: Great Promise, Big Challenge,” Microwave Journal, May 1991 1991.

[36] J. R. Guerci, et al., “Constrained optimum matched illumination-reception radar,” ed: US Patent 5,146,229, 1992.

3.10 PROBLEMS

1. A noncasual impulse response can have nonzero values for negative time indices, that is, h(−k) = 0 for some positive k value. This can arise when the impulse response is not associated with time, such as the case when k is a spatial index or when time samples are processed in batch (buffered) fashion. Rederive the H matrix of equation (3.2) when the impulse response has values from−M to M.

2. Verify that the whitening filter of equation (3.5) (i.e., H_w = R⁻¹²) indeed results in a unity variance diagonal output noise covariance, that is, cov(Hwn) = I , where cov(·) denotes the covariance operator. (Hint: Hwis not a random variable and thus is unaffected by the expectation operator.)

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116 C H A P T E R 3 Optimal and Adaptive MIMO Waveform Design 3. Assume a target has a transfer matrix given by

H_T =

 1 ¹/^√2 1/^√2 1



a. What is the optimum input (eigenfunction) that maximizes SNR for the white noise case?

b. If we interpret H_w as a target response polarization matrix in a H–V (horizon-tal and vertical) basis, what is the optimum polarization direction, assuming H-polarization corresponds to 0 degrees and V corresponds to 90 degrees?