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version role that shifts the mean moving clutter component into the rejection notch of h 2 . The purpose of h 1 is to remove any static clutter component before the frequency translation stage, preventing upconverting it into the passband of h 2 . The resulting complex signal r(n) will be filtered in both the in-phase and quadrature-phase components and is defined in Equation 12. The order of h 1 is q+1, and the order of h 2 is p+1. Expanding the complex exponential term using Euler’s identity and multiplying it across the responses of the first stage filter, the r(n) response can then be expanded into its in-phase and quadrature components. Doppler cross-modulation terms are defined for each component by Equations 13-16, where t pr is the reciprocal of the pulse repetition frequency. b(k) = (–1) k (q)! 2 q . (q – k)!(k)! (10) q out(n) = Σb(k)in(n – k) (11) k =0 x d (n) - jy d (n) h 1 (n) h 2 (n) r(n) The filter response shown in Figure 7 illustrates the stopband improvement of Equation 12. This response is computed using a C-band carrier of 5.4 GHz, Doppler velocity-tuned weighting at 8.5 meters/sec, and a pulse repetition frequency of 1538 Hz. A third-order filter is used on the input stage, and a fourth-order filter is used on the output stage. For comparison, a third- and fourth-order static filter response highlights the significant rejection capability of the broadband filter. The filter sidelobe centered around 0.1 on the normalized scale is the Doppler component that passed the input stage filter and is not fully downconverted into the rejection notch of the output stage filter. This sidelobe will increase as the notch frequency is increased. Pulseto-pulse stagger degrades the low frequency stopband of the broadband filter and is not recommended for dynamic clutter cancellation. Rather, a group-to-group pulse stagger is recommended to alleviate the blind zone cancellations. IpI(n) = cos (2π f d . nt pr ) (13) IpQ(n) = sin (2π f d . nt pr ) (14) QpI(n) = sin (2π f d . nt pr ) (15) QpQ(n) = cos (2π f d . nt pr ) (16) e jn d Figure 5. Doppler clutter-locked MTI transfer function The expansion and collection of Equation 12 will result in two real terms and two imaginary summation terms. Each term will have the same weight distribution as shown in Figure 6 except for the replacement of the Doppler cross-modulation term. The subscript naming identifies how the weight vector contributes to r(n). The letter on the left indicates to which component the weight dot product contributes, and the letter on the right indicates the input component with which to be dot product. Equations 17 and 18 give the real and imaginary responses of r(n), respectively. Conclusion Metric System’s Peregrine is operated in a dynamic MTI mode configuration. It has an instrumented 100km range in a normal MTI mode using four-pulse static filtering with pulse-to-pulse staggering in favorable weather conditions. Log video is monitored for threshold switching into a 70km range special MTI mode when inclement weather, high seas, or chaff conditions are detected. Six-pulse dynamic clutter MTI suppression against moving volumetric clutter simultaneous with low velocity ground and heavy sea clutter is performed in special MTI mode. The adaptive broadband weights are computed and applied in an openloop fashion based on an accumulated log video clutter map that directs phase video sampling in appropriate sectors. Dynamic clutter characteristics typically have a velocity mean around ±20 r(n) = p Σb 2 (k) l = 0 q q Σ b 1 (k)x d (n – l – k) – j Σ b 1 (k)y d (n – l – k) e j(n – l) ω d (12) k = 0 k = 0 b 1 (q)b 2 (p)IpI(0) b 1 (q – 1)b 2 (p)IpI(0) + b 1 (q)b 2 (p – 1) IpI(1) . b 1 (0)b 2 (p)IpI(0) + b 1 (1)b 2 (p –1)IPI(1) + . . . +b 1 (q – 1)b 2 (1)IpI(p – 1) + b 1 (q)b 2 (0)IpI(p) . b 1 (0)b 2 (1)IpI(p – 1) +b 1 (1)b 2 (0)IpI(p) b 1 (0)b 2 (0)IpI(p) Figure 6. b IPI (k) weight vector, where k begins from top (0) to bottom (p+q) Re[r(n)]= Im[r(n)]= p + q p + q Σ b IPI (k)x d (n – k) + Σb IPQ (k)y d (n – k) (17) k = 0 k = 0 p + q p + q Σ b QPI (k)x d (n – k) + Σ b QPQ (k)y d (n – k) (18) k = 0 k = 0 Reprinted from VMEbus Systems / April 2002 Copyright 2002 / VMEbus Systems
dB 0 -10 6th order clutter-locked 3rd order static 4th order static 6th order clutter-locked stagger -20 -30 -40 -50 -60 0 0.5 1 1.5 2 2.5 3 f f pr Figure 7. Doppler clutter-locked MTI response meters/sec with a spectrum width near 1 meter/sec. The filters are applied independently on the in-phase and quadrature-phase components. In addition, the Peregrine performs the matched correlation on these channels independently before applying its modulus. It uses hard-limited (binary), phase-coded pulse compression on a 17.65 ms pulse transmission at 63-bit or 127-bit resolution. Instrumented pulse repetition frequencies vary from 1200 Hz to 1600 Hz, depending on mode, range, and stagger. The Peregrine signal processing cabinet consists of: ■ A VMEbus chassis using a VMIC VMIVME-7740 Pentium III based single-board bus master ■ Two Pentek 4290 quad TMS320C6201 DSP boards ■ A Metric-designed, RAM-based trigger generator card The VMIC board is clocked at 800 MHz running Windows2000 real-time acquisition of the DSP results and synchronization of the triggers. Pentek’s 6211 12-bit A/D mezzanine modules sample two three-channel video signals (I, Q, and log video). A highelevation beam path and a low-elevation beam path provide improved target detection. All DSP algorithms are optimized using the Texas Instruments Code Composer Studio V1.20 and loaded using Pentek’s Swiftnet product. The post-processing programs for multiple clutter map classification, sliding window correlation detection, and plot extractions for tracking filter are compiled using Visual Studio 6.0. In summary, coherent phase video processing using Doppler filtering and pulse compression in dedicated DSP hardware is combined with log-detected clutter mapping and mode control to provide constant false alarm rate detections to non-coherent short-term detection and long-term clutter residue censor processes. References Eaves, Jerry L., and Edward K. Reedy, Principles of Modern Radar, Von Nostrand, 1987. Couch, Leon W., Digital and Analog Communication Systems, Prentice Hall, 1997. Kehtarnavaz, Nasser, and Burc Simsek, C6x-Based Digital Signal Processing, Prentice Hall, 2000. Hayes, Monson H., Digital Signal Processing, Schaum’s Outline Series, McGraw-Hill, 1999. Christopher Repesh is a senior signal processing engineer for Metric Systems, a specialized manufacturer of threatradar-simulator systems, airborne instrumentation, RF datalink communications, and tactical surveillance radar systems. He has been instrumental in re-engineering and migrating legacy video processing hardware in ITT Gilfillan’s FALCON to a COTS software-based solution. Prior to joining Metric Systems, Christopher was an associate research engineer with Mission Research Corporation, developing and employing advanced computational electromagnetic software to diagnose the effectiveness of operationally viable antenna designs and the effects of high-power EMP discharges. Christopher holds a BSEE from the University of Texas at Arlington and an MSEE from the University of Florida, Gainesville. He currently works in Fort Walton Beach, FL. For more information, contact: Metric Systems 645 Anchors Street • Fort Walton Beach, FL 32548 Tel: 850-302-3000 • Fax: 850-302-3371 Web site: www.metricsys.com Reprinted from VMEbus Systems / April 2002 Copyright 2002 / VMEbus Systems
Page 1 and 2: Broadband FIR Moving-Target Indicat
Page 3: avoid multiplicative overflow. Equa

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