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The Ubiquitous The Ubiquitous

MATCHED FILTERMATCHED FILTER. . . it’s everywhere!!. . . it’s everywhere!!

an evening with a very important principle that’s finding exciting new applications in modern radar

R. T. Hill AES Societyan IEEE Lecturer Dallas Chapter

25 September 2007

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How do receivers work?A brief review, then, of

network theory

characterizing a receiver by its“impulse response function”

representing radio signals

reminding ourselves of “convolution”in linear systems . .

Wow! . . all in twenty minutes or so!

Well, first we’ll need a receiver block diagram

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Generalized Receiver Block Diagram

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“characterizing” a receiver (or generally, a network) by its “impulse response function”

Why?

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Do you see how we can represent any signal s(t) as acollection of impulses . .?

. . the impulse is a wonderful function, so useful – I’ll make some comments about it.

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Also, our signal (certainly in radio/radar work)is surely bipolar-cyclic . . a modulated carrier

. . that is, we can think of it, in radio work, as a sine waveof voltage field intensity and polarity, leaving all the relationshipsto its accompanying magnetic field and the medium at hand“to Maxwell”, so to speak!

Do you see nowhow impulses could still be usedto represent eventhis complex radiosignal?

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+

+

Now, we see here that the output is indeed the sum of the many impulse response functions, weighted and translated by the input signal . . that the action of thislinear network is, by superposition, a convolution!

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About designing our receiver . .

What impulse response functiondo I want??

Well, what do we want ourreceiver to do . . produce areplica of our signal? NO!!

. . . discuss . . .

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For maximum sensitivity to our own signal’s being atthe input, we don’t need to see a copy of it at the output . . . . we simply need, at the output, the greatest possible indication of its presence at the input.

In other words, we need to have an impulse responsefunction that, when convolved with our signal, wouldgive the greatest possible “signal to noise ratio” at theoutput.

Oh, yes . . did I neglect to mention “noise”?

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Admitting, then, that our total input is, say, “gi(t)”, made up of our desired signal AND accompanying (but wholly independent) noise, we see that we cantreat these two inputs separately as they pass throughour receiver:

Yes, noise . . always present in radio work . .

Oh, yes , , looks complicated, but nothing new here . . justremember the words here! Let the math “talk” to you!

and our output is just the sum of the two convolutions – one dueto the signal being present and one from the always-present noise.

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. . WHAT Impulse Response Functionwould produce the greatest possible indication, at theoutput, of our signal’s arrival at the input?

Answer . . . . various methods (differential calculus; the “Schwartz

inequality”) lead to the conclusion that maximum sensitivity is achieved when the IRF is the complex conjugate of the subject signal:

Concepts:► Signal to Noise Ratio (SNR) as a measure of sensitivity► Representing our cyclic signal in this “A ejθ “ form

and, note that some details of necessary time displacement notation are overlooked here

So, we might ask . .

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This, then, is

The Matched Filter

A receiver the impulse response function of which isthe complex conjugate of a particular signal will producethe greatest possible signal-to-noise ratio at the outputwhen that signal is at the input and in the presence ofindependent and completely random noise . . . this receiver is the most “sensitive” to the particular signal . . . . it is “matched” to it.

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Representing our signal by a rotating vector . . . . a great convenience

. . a vector rotating at the carrier frequency, amplitude modulated by s(t) with possible phase modulation (a binary phase coded signal, for example) shown as Φ(t) . . do you know, or remember, this convention? Sure helps in diagramming a lot of things in today’s signal processing.

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. . consider a four-segment amplitude- and phase-modulated signal,and for the moment, without noise . .

Some discussion . . to improve our understanding

. . something similar to a child’sscattering the blocks with which he had made a tower . . what if we wanted to see that maximum height (rebuild the tower) again? I would re-align the blocksby multiplying each by its conjugate (remember: angles add when vectors are multiplied) and – voila! – the tower appears again, maximum possible height!

Is “angle” then enough? The conjugate involves the amplitude . .why?

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In conjugating the phase modulation of our signal, why multiplyin the convolution by the amplitude as well?

Ah . . we cannot ignore the noise!

The circles here show an expectation of the noise contribution. Our input signal gi(t) is our own signal abcd and this noise . . butnotice, the noise is (of course) of the same strength regardless of the amplitude of abcd at that time. Also note, the noise is completelyrandom. This utter randomness and independence of abcd are propertiesof “Gaussian” noise, “white” noise, as from natural thermal phenomena. Now, to convolve with, say, a unit level phase-only conjugate would exaggerate some of the noise effects in the angle-corrected vector addition – unwise. The best thing for us to do, in such noise, isindeed to ignore it! “Match” to our signal alone!

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OK . . but just one further thought . . Remember the child’s block tower? Consider:

The child’s playroom is subject to a mild earthquake (good grief!) as the blocks are tumbled in the way we expected.

“White” noise . . we’re OK . . use the matched filter

On the other hand, what if a “wind” had been blowing distinctly from, say, the west as the blocks were tumbled in addition to the earthquake’s vibratory behavior?

Not random!! Biased! We’d better compensate for that, assuming we can sense it. That is, we may wish to use a “whitening” filter to randomize the disturbance before the matched filter!

That idea is indeed “key” to much of the adaptive signal processingso strong in today’s radar literature . . . more about that to come

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Where do we use the Matched Filter?

Some illustrations . . Pulse compression, very common in radar . .

Receive beam steering, direction finding in antennas,the “adaptive antenna” . .

Space-Time Adaptive Processing (STAP) in radar . .

Polarimetry in radar, adaptive processing, target recognition . .

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Illustration # 1 . . Pulse Compression

► First, some remarks about pulse compressionin modern radar . . fine range resolution desired, but still with long pulse for lots of energy

► Achieved by modulating the transmitted pulse, then “compressing” the pulse on receivewith (of course) a matched filter

► Techniques – binary phase coding widely used; typical lengths of hundreds to one – our example here? A mere four to one!

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first bit out

Binary phase coding with a “tapped delay line”

Showing a 180o

phase shift on one tap, giving the binary code sequence “ + - + + “ , one of the Barker codes.

On receive (after down conversion), the signal is sent through its Matched Filter . . in this case, the same circuit with the taps reversed:

+ + - +

Clearly, pulse compression is a “convolution” process, and we see the “time” or “range” sidelobes in the output which, for all the Barker binary codes, are never more than unity value, while the narrow main peak is full value, the number of bits in the code. In this matched situation, the output is the “autocorrelation function”, and a low sidelobe level is a very desirable attribute of a candidate code.

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A peculiar thing about binary phase coding

The idea that the “tapped delay line, backwards” is indeed a conjugating matched filter is not so clearin binary phase coding . . adding or subtracting 180°results in the same zero phase for that bit (all bits then being phase aligned).

Just to illustrate conjugation more clearly, imagine that our four-segment sequence had been 0°, -30°, 0°, 0° – terrible autocorrelation function, but it makes our point about phase “realignment” by conjugation in this convolution process.

Pulse expanderon transmit

Pulse compressoron receive

Compressed pulse output (here showing rather poor range sidelobes)

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The Barker codes: sidelobe levelLength 2 + - and + + - 6.0 dB 3 + + - - 9.5 4 + + - + and + + + - - 12.0 5 + + + - + - 14.0 7 + + + - - + - - 16.9 11 + + + - - - + - - + - - 20.8 13 + + + + + - - + + - + - + - 22.3 dB

Modulo 2 adder

Seven-stage shift register

This seven-stage shift register is used to generate a 127-bit binary sequence that can in turn be used to control a (0,180o) phase shifter through which our IF signal is passed in the pulse modulator of our waveform generator. Such shift-register generators produce sequences of length 2N – 1 (before repeating; N is the number of stages). Today, computer programs generate the modulation,storing sequences known to have good autocorrelation functions, for many lengths other than just 2Int - 1.

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Illustration # 2 . . Antennas; the “Adaptive” Array

► First, some remarks about how antennas form receivebeams, phased arrays the simplest and very pertinent illustration

► Next, we’ll observe that compensating for the “angleof arrival” for an echo is indeed a form of (you guessed it) a matched filter, this one in “angle space”

► Then, we’ll consider (again in angle space) that the “noise” may NOT be utterly random, not “white” (statisticallyuniform) in angle . . we may need a “whitening” filter before our matched filter

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First, consider a few discrete elements of a phased array, a line array . .

These simple sketcheswill remind us of howantennas, phased arraysspecifically, perform beamsteering . . a matter ofcompensating for the element-to-element phasedifference resulting from the path-length differences associated with the desired beam-steering angle (the angle-of-arrival “under test”, so to speak)

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. . continuing . .

Can you see how the“compensating” phasecontrol in the phasedarray is acting as a conjugating matchedfilter? Here, the “segments”of our “signal” are NOTa function of time (as beforediscussed in signal processing)but rather a function of space,the position of each elementof the array. The same Matched Filter principle applies, but here ina different “dimensional space” than normally considered in matchedfilter teaching.

But what was the other part of the principle? Ah . . that the “background noise” be independent and random . .

. . necessary condition for the Matched Filter to give best possible output (here, angle measurement accuracy).

Is our noise stationary in angle, uniform statistically??? Perhaps not!!

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Bottom line – the coherent sidelobe cancellers (CSLC) – the more elaborate “adaptive phased arrays”

are forms of spatial “whitening filters”, here to “whiten”the heterogeneous disturbance – noise – in angle. Why? So that

a straightforward angle matched filter can be used most effectively.

The “Adaptive Antenna” . .

Array signal processing . . first, spatial analysis, then compensation to “whiten”

Discussion

► Spatial analysis analogous to “spectral analysis”

► Finding compensating weights for each element involves solving as many simultaneous algebraic equations . . inverting the covariance matrix NOT EASY

► Adapted pattern will be “inverse” to the angular distribution of noise, “whitening” it ► Today’s art state . . 16 DOF rather standard . .

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Illustration # 3 . . Space-Time Adaptive Processing, STAP

► Adaptive antenna processing is Space-Adaptive. What is meant by Space-Time Adaptive Processing?

► A few remarks about Doppler processing in radar, itselfan application of the Matched Filter . . Doppler filtersare indeed filters matched to a particular Doppler shift

► Many radars, airborne ones particularly, need to do Dopplerprocessing when the background (noise, continuous groundclutter) is certainly NOT spectrally uniform . . once again,we’ll need a “whitening” filter

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Doppler filtering

Theory View a single Doppler “filter” as a classic “Matched Filter”, that is, we multiply (convolve) the input signal with the conjugate of the signal being sought.

sample #1 2 3 4

signal

x

reference

Recall, phase angles add when complex numbers (vectors) are multiplied – that is,the signal is “rotated back” in phase by the amount it might have been progressingin phase . . To the extent that such a component was in the input signal will we get an output in this particular filter. We’ve built a Matched Filter for that component (that frequency component) alone. HOWEVER, this is best ONLY IF backgroundnoise is utterly random in Doppler frequencies . .

=

product

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The airborne radar situation . . for discussion

Do you see the need for “whitening” the backgroundin BOTH the angle dimension (the broadband interferenceis purely angle dependent) and in Doppler shift (the groundreflectivity may NOT be utterly random, uniformly distributed in angle)?

Broadband interference(jamming) suggests needfor adaptive antenna

Terrain featurescontribute to non-uniform spectrumof the side-lobe coupledground clutter

An airborne radar

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STAP – to be adaptive in both the antenna’s pattern (as before discussed) and also in the weights to put on each pulse return to shape the Doppler filters in spectrum (compensating for the non-uniform spectrum of the background clutter)

The “data field”available to us

To adapt to the background’s sensed heterogeneity in both angle and spectrum,we must solve (to be “fully adaptive”) MN simultaneous equations (size of the covariancematrix to invert: MN x MN. No wonder, then, today’s literature is full of STAP papers addressing ways to “reduce the dimensionality” of the processing, find the best thatwe can do in “partial adaptivity”! Very exciting work!

Space

Time

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Illustration # 4 . . The Polarimetric Matched Filter

► First, a short general review of polarimetry in radar, its uses, its value

► Then, an example of the Polarimetric Whitening Filter and how a polarimetric radar image (by SAR)is improved just from PWF application to the area clutter

► Of course, the “whitening” to randomize the polarization stateof the surrounding area (local clutter in a scene) permits us then to search for targets (building, vehicles) the “polarimetric signature” of which may have beenestimated in advance.

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Radar Polarimetry . . a little review

► Polarization of an Electro-Magnetic wave is taken as the spatial orientation of the E-field . . most, but certainlynot all, radars are designed to operate, for various reasons, in either horizontal or vertical (linear) polarization, fixed by the antenna design – that is, they are not “polarimetric”

► A “Fully Polarimetric Radar” (FPR) can, first, transmit one polarization and separately measure the received signal in each of two orthogonal polarizations, thendo the same, transmitting the orthogonal polarization

(e.g., transmit H, receive H and V; then transmit V, receive H and V)

► We learn a lot about a target by sensing its polarimetric scattering

► Developed well by the meteorological radar community, someother specialty radars

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Polarimetry used for image enhancement

“Whitening” and “Matching” filters

► The work under Dr. Les Novak (MIT/Lincoln Laboratories) in the 1990s is extremely valuable in establishing these approaches to image enhancement by polarimetry. A number of papers in our conferences (to be cited here) and other teaching material he has provided me contribute to this instruction. An airborne SAR at 33 GHz, fully polarimetric, was used in many valuable experiments there.

► Review . . Detection of things of interest (targets) in the presence of return not of interest (noise, clutter) requires contrast between the two in some observable dimension space (here, our image).

► The idea of “whitening” and “matching” is universal, forms matched filter theory.

● The whitening filter: attempts to minimize the “speckle” of the background,– that is, the standard deviation among the pixels of the clutter – inimages formed by combining the complex images in HH, HV and VVusing complex weights among them, weights that minimize the correlation in cluttered regions among the three images. The weightsare based on knowledge of the clutter covariance matrix, a priori in the Novak work reviewed here, and involved its inversion, not difficult with order three. With such PWF, cells that do not “belong” to the clutter willhave increased contrast with the background and are more easily seen.

● The matched filter: attempts to maximize the target intensity to clutter intensity in the combined image, by using weights based on knowledge (estimates) of the polarimetric covariance of target AND clutter returns.

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(Polarimetry and image enhancement, cont.)

. . from the Novak, Lincoln Laboratory work onPWF . . a dual-power-line scene, images bythe 33 GHz fully polarimetric airborne SAR.

The histograms show the increased contrast(separation of the clutter and towers compilations)afforded by PWF processing compared to anon-polarimetric image, here the HH.

One can see the visible effect in the two imagesabove, HH on the left, PWF-processed at right.

All here with 1 foot x 1 foot resolution.

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Well . . did we make it to this concluding slide??

The Matched Filter● The conjugate impulse response function –

max sensitivity to a signal in the presence of white noise

● Normally taught in the context of just temporal signalprocessing . . functions of time, etc

● Should be no less seen by students of radar as the underlying principle to many advances, in “other” dimension spaces: angle (antennapatterns), spectral analysis (Doppler filtering),polarimetric analysis (as in synthetic apertureradar image enhancement)

● Today’s “adaptive” processes are generally the MF-related “whitening” required in non-random environments

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The EndMore than you wanted to know about

The Matched Filter