Tài liệu 70 Space-Time Adaptive Processing for Airborne Surveillance Radar doc

Hong Wang. “Space-Time Adaptive Processing for Airborne Surveillance Radar.” 2000 CRC Press LLC. <http://www.engnetbase.com>. Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70.1MainReceiveApertureandAnalogBeamforming 70.2DatatobeProcessed 70.3TheProcessingNeedsandMajorIssues 70.4TemporalDOFReduction 70.5AdaptiveFilteringwithNeededandSample-Supportable DOFandEmbeddedCFARProcessing 70.6Scan-To-ScanTrack-Before-DetectProcessing 70.7Real-TimeNonhomogeneityDetectionandSample ConditioningandSelection 70.8SpaceorSpace-RangeAdaptivePre-SuppressionofJammers 70.9ASTAPExamplewithaRevisittoAnalogBeamforming 70.10Summary References Space-TimeAdaptiveProcessing(STAP)isamulti-dimensionalfilteringtechniquedevelopedfor minimizingtheeffectsofvariouskindsofinterferenceontargetdetectionwithapulsedairborne surveillanceradar.Themostcommondimensions,orfilteringdomains,generallyincludetheaz- imuthangle,elevationangle,polarizationangle,dopplerfrequency,etc.inwhichtherelativelyweak targetsignaltobedetectedandtheinterferencehavecertaindifferences.Inthefollowing,theSTAP principlewillbeillustratedforfilteringinthejointazimuthangle(space)anddopplerfrequency (time)domainonly. STAPhasbeenaveryactiveresearchanddevelopmentareasincethepublicationofReedetal.’s seminalpaper[1].WiththerecentlycompletedMultichannelAirborneRadarMeasurementproject (MCARM)[2]–[5],STAPhasbeenestablishedasavaluablealternativetothetraditionalapproaches, suchasultra-lowsidelobebeamformingandDisplacedPhaseCenterAntenna(DPCA)[6].Muchof STAPresearchanddevelopmenteffortshavebeendrivenbytheneedstomakethesystemaffordable, tosimplifyitsfront-hardwarecalibration,andtominimizethesystem’sperformancelossinseverely nonhomogeneousenvironments.Figure70.1isageneralconfigurationofSTAPfunctionalblocks[5, 7]whoseprincipleswillbediscussedinthefollowingsections. c  1999byCRCPressLLC FIGURE 70.1: A general STAP configuration with auxiliary and main arrays. 70.1 Main Receive Aperture and Analog Beamforming For conceptual clarity, the STAP configuration of Fig. 70.1 separates a possibly integrated aperture into two parts: the main aperture which is most likely shared by the radar transmitter, and an auxiliary array of spatially distributed channels for suppression of Wideband Noise Jammers (WNJ). For convenience of discussion, the main aperture is assumed to have N c columns of elements, with the column spacing equal to a half wavelength and elements in each column being combined to produce a pre-designed, nonadaptive elevation beam-pattern. The size of the main aperture in terms of the system’s chosen wavelength is an important system parameter, usuallydeterminedbythesystemspecificationsoftherequiredtransmitterpower-aperture product as well as azimuth resolution. Typical aperture size spans from a few wavelengths for some short-range radars to over 60 wavelengths for some airborne early warning systems. The analog beamformingnetworkcombinesthe N c columnsofthemainaperturetoproduce N s receiverchannels whose outputs are digitized for further processing. One should note that the earliest STAP approach presented in [1], i.e., the so-called “element space” approach, is a special case of Fig. 70.1 when N s = N c is chosen. The design of the analog beamformer affects c  1999 by CRC Press LLC 1. the system’s overall performance (especially in nonhomogeneous environments), 2. implementation cost, 3. channel calibration burden, 4. system reliability, and 5. controllability of the system’s response pattern. The design principle will be briefly discussed in Section 70.9; and because of the array’s element error, column-combiner error, and column mutual-coupling effects, it is quite different from what is available in the adaptive array literature such as [8], where already digitized, perfectly matched channels are generally assumed. Finally, it should be pointed out that the main aperture and analog beamforming network in Fig. 70.1 may also include nonphased-array hardware, such as the common reflector-feed as well as the hybrid reflector and phased-array feed [9]. Also, subarraying such as [10] is considered as a form of analog beamforming of Fig. 70.1. 70.2 Data to be Processed Assume that the radar transmits, at each look angle, a sequence of N t uniformly spaced, phase- coherent RF pulses as shown in Fig. 70.2 for its envelope only. Each of N s receivers typically consists of a front-end amplifier, down-converter, waveform-matched filter, and A/D converter with a sampling frequency at least equal to the signal bandwidth. Consider the kth sample of radar return over the N t pulse repetition intervals (PRI) from a single receiver, where the index “k” is commonly called the range index or cell. The total number of range cells, K 0 is approximately equal to the product of the PRI and signal bandwidth. The coherent processing interval (CPI) is the product of the PRI and N t ; and since a fixed PRI can usually be assumed at a given look angle, CPI and N t are often used interchangeably. FIGURE 70.2: A sequence of N t phase-coherent RF pulses (only envelope shown) transmitted at a given angle. The pulse repetition frequency (PRF) is 1/T. With N s receiver channels, the data at the kth range cell can be expressed by a matrix X k , N s × N t , for k = 1, 2, .K 0 . The total amount of data visually forms a “cube” shown in Fig. 70.3, which is the raw data cube to be processed at a given look angle. It is important to note from Fig. 70.3 that the term “time” is associated with the CPI for any given range cell, i.e., across the multiple PRIs, while the term “range” is used within a PRI. Therefore, the meaning of the frequency corresponding to the time is the so-called doppler frequency, describing the rate of the phase-shift progression of a return component with respect to the initial phase of the phase-coherent pulse train. The doppler frequency of a return, e.g., from a moving target, depends on the target velocity and direction as well as the airborne radar’s platform velocity and direction, etc. c  1999 by CRC Press LLC FIGURE 70.3: Raw data at a given look angle and the space, time, and range axes. 70.3 The Processing Needs and Major Issues At a given look angle, the radar is to detect the existence of targets of unknown range and unknown doppler frequencyin the presenceofvarious interference. In other words,one can view the processing as a mapping from the data cube to a range-doppler plane with sufficient suppression of unwanted components in the data. Like any other filtering, the interference suppression relies on the differences between wanted target components and unwanted interference components in the angle-doppler domain. Figure 70.4 illustrates the spectral distribution of potential interference in the spatial and temporal (doppler)frequency domain beforethe analog beamforming network, while Fig. 70.5 shows atypical range distribution of interferencepower. Astargetsof interestusually haveunknown doppler frequencies and unknown distances, detection needs to be carried out at sufficiently dense doppler frequencies along the look angle for each range cell within the system’s surveillance volume. For each cell at which target detection is being carried out, some of surrounding cells can be used to produce an estimate of interference statistics (usually up to the second order), i.e., providing “sample support”, under the assumption that all cells involved have an identical statistical distribution. Figure 70.4 also shows that, in terms of their spectral differences, traditional wideband noise jammers, whether enteringthe systemthrough directpathor multipath(terrain scattering/near-fieldscattering), require spatial nulling only; while clutter and chaff require angle-doppler coupled nulling. Coherent repeater jammers (CRJ) represent a nontraditional threat of a target-like spectral feature with randomized ranges and doppler frequencies, making them more harmful to adaptivesystems than to conventional nonadaptive systems [11]. Although Fig. 70.5 has alreadyserved to indicatethat the interference is nonhomogeneous in range, i.e., its statistics vary along the range axis, recent airborne experiments have revealed that its severe- ness may have long been underestimated, especially over land [3]. Figure 70.6 [5, 7] summarizes the sources of various nonhomogeneity together with their main features. As pointed out in [12], a serious problem associated with any STAP approach is its basic assumption that there is a sufficient amount of sample support for its adaptive learning, which is most often void in real environments even in the absence of any nonhomogeneity type of jammers such as CRJ. Therefore, a crucial issue for the success of STAP in real environments is the development of data-efficient STAP approaches, in conjunction with the selection of reasonably identically distributed samples before estimating interference statistics. To achieve a sufficient level of the data efficiency in nonhomogeneous envi- ronments, the three most performance- and cost-effective methods are temporal degrees-of-freedom (DOF) reduction, analog beamforming to control the spatial DOF creation, and pre-suppression of WNJ as shown in Fig. 70.1. Another crucial issue is the affordability of STAP-based systems. As pointed out in [9], phased- arrays, especially those active ones (i.e., with the so-called T/R modules), remain very expensive despite the 30-year research and development. For multichannel systems, the cost of adding more c  1999 by CRC Press LLC FIGURE 70.4: Illustration of interference spectral distribution for a side-mounted aperture. receivers and A/D converters with a sufficient quality makes the affordability even worse. Ofcourse, morereceiverchannelsmeanmoresystem’savailablespatialDOF. However, it isoftenthe case in practice that the excessive amount of the DOF, e.g., obtained via one receiver channel for each column of a not-so-small aperture, is not necessary to the system. Ironically, excessive DOF can make thecontrolof theresponse patternmore difficult, evenrequiring significant algorithm constraints [8]; andafterall, ithastobereducedtoalevelsupportable bytheavailableamountofreasonablyidentically distributed samples in real environments. An effective solution, as demonstrated in a recent STAP experiment [13], is via the design of the analog beamformer that does not create unnecessary spatial DOF from the beginning — a sharp contrast to the DOF reduction/constraint applied in the spatial domain. Channel calibration is a problem issue for many STAP approaches. In order to minimize perfor- mancedegradation, thechannelswithsome STAPapproachesmustbematchedacrossthesignalband, and steering vectors must be known to match the array. Considering the fact that channels generally differ in both elevation and azimuth patterns (magnitude as well as phase) even at a fixed frequency, the calibration difficulty has been underestimated as experienced in recent STAP experiments [5]. It is still commonly wished that the so-called “element-space” approaches, i.e., the special case of N s = N c in Fig. 70.1, with an adaptive weight for each error-bearing “element” which hopefully can be modeled by a complex scalar, could solve the calibration problem at a significantly increased system-implementation cost as each element needs a digitized receiver channel. Unfortunately, such a wish can rarely materialize for a system with a practical aperture size operated in nonhomogeneous environments. With a spatial DOF reductionrequired bythese approachesto bring down the number of adaptive weights to a sample-supportable level, the element errors are no longer directly accessible by the adaptive weights, and thus the wishful “embedded robustness” of these element-space STAP approaches is almost gone. In contrast, the MCARM experiment has demonstrated that, by mak- ing best use of what has already been excelled in antenna engineering [13], the channel calibration problem associated with STAP can be largely solved at the analog beamforming stage , which will be discussed in Section 70.9. The above three issues all relate to the question: “What is the minimal spatial and temporal DOF c  1999 by CRC Press LLC FIGURE 70.5: Illustration of interference-power range distribution, where  h indicates the radar platform height. required?” To simplify the answer, it can be assumed first that clutter has no Doppler spectral spread caused by its internal motion during the CPI, i.e., its spectral width cut along the doppler frequency axis of Fig. 70.4 equals to zero. For WNJ components of Fig. 70.4, the required minimal spatial DOF is well established in array processing, and the required minimal temporal DOF is zero as no temporal processing can help suppress these components. The CRJ components appear only in isolated range cells as shown in Fig. 70.5, and thus they should be dealt with by sample conditioning and selection so that the system response does not suffer from their random disturbance. With the STAP configuration of Fig. 70.1, i.e., pre-suppression of WNJ and sample conditioning and selection for CRJ, the only interference components left are those angle-doppler coupled clutter/chaff spectra of Fig. 70.4. It is readily available from the two-dimensional filtering theory [14] that suppression of each of these angle-doppler coupled components only requires one spatial DOF and one temporal DOF of the joint domain processor! In other words, a line of infinitely many nulls can be formed with one spatial DOF and one temporal DOF on top of one angle-doppler coupled interference component under the assumption that there is no clutter internal motion over the CPI. It is also understandable that, when such an assumption is not valid, one only needs to increase the temporal DOF of the processor so that the null width along the doppler axis can be correspondingly increased. For conceptual clarity, N s − 1 will be called the system’s available spatial DOF and N t − 1 the system’s available temporal DOF. While the former has a direct impact on the implementation cost, calibration burden, and system reliability, the latter is determined by the CPI length and PRI with little cost impact, etc. Mainly due to the nonhomogeneity-caused sample support problem discussed earlier, the adaptive joint domain processor may have its spatial DOF and temporal DOF, denoted by N ps and N pt respectively, different from the system’s availables by what is so-called DOF reduction. However, the spatial DOF reduction should be avoided by establishing the system’s available spatial DOF as close to what is needed as possible from the beginning. 70.4 Temporal DOF Reduction Typically an airborne surveillance radar has N t anywhere between 8 and 128, depending on the CPI and PRI. With the processor’s temporal DOF, N pt , needed for the adjustment of the null width, normally being no more than 2 ∼ 4, huge DOF reduction is usually performed for the reasons of the sample support and better response-pattern control explained in Section 70.3. c  1999 by CRC Press LLC FIGURE 70.6: Typical nonhomogeneities. An optimized reduction could be found, given N t , N pt , and the interference statistics which are still unknown at this stage of processing in practice [7]. There are several non-optimized temporal DOF reduction methods available, such as the Doppler-domain (joint domain) localized processing (DDL/JDL) [12, 15, 16] and the PRI-staggered Doppler-decomposed processing (PRI-SDD) [17], which are well behaved and easy to implement. The DDL/JDL principle will be discussed below. The DDL/JDL consists of unwindowed/untapered DFT of (at least) N t -point long, operated on each of the N s receiver outputs. The same N pt + 1 most adjacent frequency bins of the DFTs of the N s receiver outputs form the new data matrix at a given range cell, for detection of a target whose Doppler frequency is equal to the center bin. Figure 70.7 shows an example for N s = 3, N t = 8, and N pt = 2. In other words, the DDL/JDL transforms the raw data cube of N s × N t × K 0 into (at least) N t smaller data cubes, each of N s × (N pt + 1) × K 0 for target detection at the center doppler bin. The DDL/JDL is noticeable for the following features. 1. There is no so-called signal cancellation, as the unwindowed/untapered DFT provides no desired signal components in the adjacent bins (i.e., reference “channel”) for the assumed target doppler frequency. 2. The grouping of N pt +1 most adjacent bins gives a high degree of correlation between the interference component at the center bin and those at the surrounding bins—afeature important to cancellation of any spectrum-distributed interference such as clutter. The cross-spectral algorithm [18] also has this feature. 3. The response pattern can be well controlled as N pt can be kept small — just enough for the needed null-width adjustment; and N pt itself easily can be adjusted to fit different clutter spectral spread due to its internal motion. 4. Obviously the DDL/JDL is suitable for parallel processing. While the DDL/JDL is a typical transformation-based temporal DOF reduction method, other methods involving the use of DFTs are not necessarily transformation-based. An example is the PRI-SDD [17] which applies time-domain temporal DOF reduction on each doppler component. This explains why the PRI-SDD requires N pt times more DFTs that should be tapered. It also serves c  1999 by CRC Press LLC FIGURE 70.7: The DDL/JDL principle for temporal DOF reduction illustrated with N s = 3, N t = 8, and N pt = 2. as an example that an algorithm classification by the existence of the DFT use may cause a conceptual confusion. 70.5 Adaptive Filtering with Needed and Sample-Supportable DOF and Embedded CFAR Processing After the above temporal DOF reduction, the dimension of the new data cube to be processed at a given look angle for each doppler bin is N s × (N pt + 1) × K 0 . Consider a particular range cell at which target detection is being performed. Let x,N s (N pt + 1) × 1, be the stacked data vector of this range cell, which is usually called the primary data vector. Let y 1 , y 2 , .,y k ,allN s (N pt + 1) × 1 and usually called the secondary data, be the same-stacked data vectors of the K surrounding range cells, which have been selected and/or conditioned to eliminate any significant nonhomogeneities with respect to the interference contents of the primary data vector. Let s, N s (N pt + 1) × 1,be the target-signal component of x with the assumed angle of arrival equal to the look angle and the assumed doppler frequency corresponding to the center doppler bin. In practice, a look-up table of the “steering vector” s for all look-angles and all doppler bins usually has to be stored in the processor, basedon updatedsystemcalibration. Aclass of STAPsystems with the steering-vector calibration-free feature has been developed, and an example from [13] will be presented in Section 70.9. There are two classes of adaptive filtering algorithms: one with a separately designed constant false alarm rate (CFAR) processor, and the other with embedded CFAR processing. The original sample matrix inversion algorithm (SMI) [1] belongs to the former, which is given by η SMI =    ˆw H SMI x    2 H 1 > < H 0 η 0 (70.1) where w SMI = ˆ R −1 s , (70.2) c  1999 by CRC Press LLC and ˆ R = 1 K K  k=1 y k y H k (70.3) The SMI performance under the Gaussian noise/interference assumption has been analyzed in detail [1], and in general it is believed that acceptable performance can be expected if the data vectors are independent and identically distributed (iid) with K, the number of the secondary, being at least two times N s (N pt +1). Detection performance evaluation using a SINR-like measure deserves some care when K is finite, even under the iid assumption [19, 20]. Ifthe output of an adaptive filter, when directly used for threshold detection, producesa probability of false alarm independent of the unknown interference correlation matrix under a set of given conditions, the adaptive filter is said to have an embedded CFAR. Under the iid Gaussian condition, two well-known algorithms with embedded CFAR are the Modified SMI [21] and Kelly’s generalized likelihood ratio detector (GLR) [22], both of which are linked to the SMI as shown in Fig. 70.8.The FIGURE 70.8: The link among the SMI, modified SMI (MSMI), and GLR where N = (N ps + 1)(N pt + 1) × 1. GLR has the following interesting features: 1. 0 < 1 K η GLR < 1, which is a necessary condition for robustness in nongaussian interfer- ence [23]. 2. Invariance with respect to scaling all data or scaling s. 3. One cannot express η GLR as ˆw H x; and with a finite K, an objective definition of its output SINR becomes questionable. Table 70.1 summarizes the modified SMI and GLR performance, based on [21, 24]. It should be noted that the use of the scan-to-scan track-before-detect processor (SSTBD to be discussed in Section 70.6) does not make the CFAR control any less important because the SSTBD itself is not error-free even with the assumption that almost infinite computing power would be available. Moreover, the initial CFAR thresholding can actually optimize the overall performance, in addition to a dramatic reduction of the computation load of the SSTBD processor. Traditionally, filter and CFAR designs have been carried out separately, which is valid as long as the filter is not data- dependent. Therefore, such a traditional practice becomes questionable for STAP, especially when K c  1999 by CRC Press LLC [...]... 1533– 1542, Dec 1992 [9] Skolnik, M., The radar antenna-Circa 1995, J Franklin Inst., Elsevier Science Ltd., 332B(5), 503–519, 1995 [10] Klemm, R., Antenna design for adaptive airborne MTI, Proc 1992 IEE Intl Conf Radar, 296–299, Brighton, U.K., Oct 12-13, 1992 [11] Wang, H., Zhang, Y and Wicks, M.C., Performance evaluation of space-time processing adaptive array radar in coherent repeater jamming environments,... International Conference on Radar, 365–368, Beijing, China, Oct 22-24, 1991 [13] Brown, R.D., Wicks, M.C., Zhang, Y., Zhang, Q and Wang, H., A space-time adaptive processing approach for improved performance and affordability, Proc IEEE 1996 National Radar Conference, 321–326, Ann Arbor, MI, May 13-16, 1996 [14] Pendergrass, N.A., Mitra, S.K and Jury, E.I., Spectral transformations for two-dimensional digital... National Radar Conference, 130–135, Ann Arbor, MI, May 13-16, 1996 [4] Fenner, D.K and Hoover, Jr., W.F., Test results of a space-time adaptive processing system for airborne early warning radar, Proc IEEE 1996 National Radar Conference, 88–93, Ann Arbor, MI, May 13-16, 1996 c 1999 by CRC Press LLC [5] Wang, H., Zhang, Y and Zhang, Q., Lessons learned from recent STAP experiments, Proc CIE International Radar. .. performance of an existing airborne radar system, whether its original design is based on low sidelobe beamforming or -DPCA While the trend is toward more affordable computing hardware, STAP processing still imposes a considerable burden which increases sharply with the order of the adaptive processor and radar bandwidth In this respect, -STAP reduces computational requirements in matrix order N 3 adaptive. .. environments, Proc IEEE Long Island Section Adaptive Antenna Syst Symp., 65–69, Melville, NY, Nov 7-8, 1994 [12] Wang, H and Cai, L., On adaptive spatial-temporal processing for airborne surveillance radar systems, IEEE Trans on Aerospace and Electronic Systems, AES-30(3), 660– 670, July 1994 Part of this paper is also in Proc 25th Annual Conference on Information Sciences and Systems, 968–975, Baltimore,... rate in adaptive arrays, IEEE Trans on Aerospace and Electronic Systems, AES-10, 853–863, Nov 1974 [2] Little, M.O and Berry, W.P., Real-time multichannel airborne radar measurements, Proc IEEE National Radar Conference, 138–142, Syracuse, NY, May 13-15, 1997 [3] Melvin, W.L., Wicks, M.C and Brown, R.D., Assessment of multichannel airborne radar measurements for analysis and design of space-time processing. .. localized adaptive MTD processor, IEEE Trans on Aerospace and Electronic Systems, AES-27(3), 532–539, May 1991 [16] DiPietro, R.C., Extended factored space-time processing for airborne radar systems, Proc 26th Asilomar Conference on Signals, Systems, and Computers, 425–430, Pacific Grove, CA, Nov 1992 [17] Brennan, L.E., Piwinski, D.J and Staudaher, F.M., Comparison of space-time adaptive processing. .. Oct 8-10, 1996 [6] Staudaher, F.M., Airborne MTI, Radar Handbook, Skolnik, M.I., Ed., McGraw-Hill, New York, 1990, chap 16 [7] Wang, H., Space-Time Processing and Its Radar Applications, Lecture Notes for ELE891, Syracuse University, Summer 1995 [8] Tseng, C.Y and Griffiths, L.J., A unified approach to the design of linear constraints in minimum variance adaptive beamformers, IEEE Trans on Antennas and... Q., A view of current status of space-time processing algorithm research, Proc IEEE 1995 Intl Radar Conf., 635–640, Alexandria, VA, May 8-11, 1995 [29] Klemm, R., Adaptive air and spaceborne MTI under jamming conditions, Proc 1993 IEEE Natl Radar Conf., 167–172, Boston, MA, April 1993 [30] Marshall, D.F., A two step adaptive interference nulling algorithm for use with airborne sensor arrays, Proc 7th... of space-time adaptive processing approaches using experimental airborne radar data, IEEE 1993 National Radar Conference, 176–181, Lynnfield, MA, April 20-22, 1993 [18] Goldstein, J.S., Williams, D.B and Holder, E.J, Cross-spectral subspace selection for rank reduction in partially adaptive sensor array processing, Proc IEEE 1994 National Radar Conference, Atlanta, Georgia, May 29-31, 1994 [19] Nitzberg, . Space-Time Adaptive Processing for Airborne Surveillance Radar. ” 2000 CRC Press LLC. <http://www.engnetbase.com>. Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar. Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70. 1MainReceiveApertureandAnalogBeamforming 70. 2DatatobeProcessed 70. 3TheProcessingNeedsandMajorIssues