# The Ubiquitous MATCHED FILTER it’s everywhere!!

## Presentation on theme: "The Ubiquitous MATCHED FILTER it’s everywhere!!"— Presentation transcript:

The Ubiquitous MATCHED FILTER . . . it’s everywhere!!
an evening with a very important principle that’s finding exciting new applications in modern radar R. T. Hill AES Society an IEEE Lecturer Dallas Chapter 25 September 2007

Wow! . . all in twenty minutes or so!
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 

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

Do you see how we can represent any signal s(t) as a
collection of impulses . .? . . the impulse is a wonderful function, so useful – I’ll make some comments about it.

is surely bipolar-cyclic . . a modulated carrier . . that is, we can think of it, in radio work, as a sine wave of voltage field intensity and polarity, leaving all the relationships to its accompanying magnetic field and the medium at hand “to Maxwell”, so to speak! Do you see now how impulses could still be used to represent even this complex radio signal?

+ 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 this linear network is, by superposition, a convolution!

What impulse response function do I want?? Well, what do we want our receiver to do . . produce a replica of our signal? NO!! . . . discuss . . .

Oh, yes . . did I neglect to mention “noise”? 
For maximum sensitivity to our own signal’s being at the 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 response function that, when convolved with our signal, would give the greatest possible “signal to noise ratio” at the output. Oh, yes . . did I neglect to mention “noise”?

Yes, noise . . always present in radio work . .
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 can treat these two inputs separately as they pass through our receiver: Yes, noise . . always present in radio work . . Oh, yes , , looks complicated, but nothing new here . . just remember the words here! Let the math “talk” to you! and our output is just the sum of the two convolutions – one due to the signal being present and one from the always-present noise.

So, we might ask . . Answer . . . . WHAT Impulse Response Function
would produce the greatest possible indication, at the output, 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 . .

The Matched Filter This, then, is
A receiver the impulse response function of which is the complex conjugate of a particular signal will produce the greatest possible signal-to-noise ratio at the output when that signal is at the input and in the presence of independent and completely random noise this receiver is the most “sensitive” to the particular signal . . . . it is “matched” to it.

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.

Some discussion . . to improve our understanding
. . 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’s scattering 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 blocks by 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?

In conjugating the phase modulation of our signal, why multiply
in 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 . . but notice, the noise is (of course) of the same strength regardless of the amplitude of abcd at that time. Also note, the noise is completely random. This utter randomness and independence of abcd are properties of “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, is indeed to ignore it! “Match” to our signal alone!

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 processing so strong in today’s radar literature more about that to come

Where do we use the Matched Filter? Some illustrations . .

Illustration # 1 . . Pulse Compression
► First, some remarks about pulse compression in 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 receive with (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!

  + + - + Binary phase coding with a “tapped delay line”
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.

A peculiar thing about binary phase coding
The idea that the “tapped delay line, backwards” is indeed a conjugating matched filter is not so clear in 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 expander on transmit Pulse compressor on receive Compressed pulse output (here showing rather poor range sidelobes)

The Barker codes: sidelobe level Length 2 + - and + + - 6.0 dB
and 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.

Illustration # 2 . . Antennas; the “Adaptive” Array
► First, some remarks about how antennas form receive beams, phased arrays the simplest and very pertinent illustration ► Next, we’ll observe that compensating for the “angle of 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” (statistically uniform) in angle . . we may need a “whitening” filter before our matched filter 

First, consider a few discrete elements of a phased array, a line array . .
These simple sketches will remind us of how antennas, phased arrays specifically, perform beam steering . . a matter of compensating for the element-to-element phase difference resulting from the path-length differences associated with the desired beam-steering angle (the angle-of-arrival “under test”, so to speak)

. . continuing . . Can you see how the “compensating” phase
control in the phased array is acting as a conjugating matched filter? Here, the “segments” of our “signal” are NOT a function of time (as before discussed in signal processing) but rather a function of space, the position of each element of the array. The same Matched Filter principle applies, but here in a different “dimensional space” than normally considered in matched filter 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!! 

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

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, itself an application of the Matched Filter . . Doppler filters are indeed filters matched to a particular Doppler shift ► Many radars, airborne ones particularly, need to do Doppler processing when the background (noise, continuous ground clutter) is certainly NOT spectrally uniform . . once again, we’ll need a “whitening” filter

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 # signal x reference = product 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 progressing in 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 background noise is utterly random in Doppler frequencies 

The airborne radar situation . . for discussion
Do you see the need for “whitening” the background in BOTH the angle dimension (the broadband interference is purely angle dependent) and in Doppler shift (the ground reflectivity may NOT be utterly random, uniformly distributed in angle)? Broadband interference (jamming) suggests need for adaptive antenna Terrain features contribute to non-uniform spectrum of the side-lobe coupled ground clutter An airborne radar

and also in the weights to put on each pulse return to shape the
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 covariance matrix 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 that we can do in “partial adaptivity”! Very exciting work! Space Time

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 state of the surrounding area (local clutter in a scene) permits us then to search for targets (building, vehicles) the “polarimetric signature” of which may have been estimated in advance.

Radar Polarimetry . . a little review
► Polarization of an Electro-Magnetic wave is taken as the spatial orientation of the E-field . . most, but certainly not 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, then do 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, some other specialty radars

 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 – in images formed by combining the complex images in HH, HV and VV using complex weights among them, weights that minimize the correlation in cluttered regions among the three images. The weights are 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 will have 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.

. . from the Novak, Lincoln Laboratory work on
(Polarimetry and image enhancement, cont.) . . from the Novak, Lincoln Laboratory work on PWF . . a dual-power-line scene, images by the 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 a non-polarimetric image, here the HH. One can see the visible effect in the two images above, HH on the left, PWF-processed at right. All here with 1 foot x 1 foot resolution.

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 signal processing . . 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 (antenna patterns), spectral analysis (Doppler filtering), polarimetric analysis (as in synthetic aperture radar image enhancement) ● Today’s “adaptive” processes are generally the MF-related “whitening” required in non-random environments

More than you wanted to know about
The End More than you wanted to know about The Matched Filter