Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
2000-01-18
2003-03-04
Von Buhr, Maria N. (Department: 2125)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S039000, C702S194000
Reexamination Certificate
active
06529794
ABSTRACT:
BACKGROUND OF THE INVENTION
One job of sensor technology is the contact-free, precise measurement of distances and speeds. Microwaves, light waves or ultrasound are utilized for this purpose. Such sensors are versatilely employed, for example in industrial automation, in automotive technology and in the household. Radar sensors according to the FMCW principle (frequency modulated continuous wave) are standard.
FIG. 1
shows a typical circuit diagram of a FMCW sensor (also see A. G. Stove, “Linear FMCW Radar Techniques.”, IEE Proc. F 139, 343-350 (1992)). The signal source is an oscillator no that can be frequency-modulated. This oscillator is detuned time-dependently in frequency via a drive unit. The sensor emits the transmission signal s(t) via the transmission/reception unit SEE, and receives a reception signal r(t) delayed in time corresponding to the running time to the target. A separation of transmission and reception signals is effected, for example, by a transmission and reception diplexer SEW; for instance, (a circulator or a directional coupler. Alternatively, separate antennas can be employed for transmission and reception (what is referred to as a bistatic arrangement). The measured signal mess(t) generated in a first mixer MI
1
, which corresponds to the mixed product (difference frequency) of transmission signal s(t) and reception signal r(t), is filtered with a low-pass filter TP
1
. For generating a measured signal that allows a presentation as a complex number, the reception signal r(t) can be mixed in a second mixer M
12
with the transmission signal shifted in phase by &pgr;/2 in a phase shifter PH and can be subsequently filtered in a second low-pass filter TP
2
. The evaluation in an evaluation unit AE is preferably constructed with a digital signal processor to which the measured signal digitalized with analog-to-digital converters A/D is supplied as real part cosine and imaginary part sine of the complex signal. The frequency modulation of the transmission signal usually occurs linearly in time (see H. D. Griffiths, “The Effect of Phase and Amplitude Errors in FM Radar”, IEEE Colloquium on High Time-Bandwidth Product Waveforms in Radar and Sonar, London, UK, May 1, 1991, pages 9/1-9/5).
DE 195 33 124 discloses a method wherein errors in the frequency modulation are detected and corrected upon employment of a delay line. Such a delay line is shown in drawing
FIG. 1
, this comprising a delay element T (preferably a surface wave component) and a further mixer RMI. Preferably, a further low-pass filter TP
3
follows. In the further mixer RMI, the delayed signal is mixed with the current signal to form a reference signal. A correction signal is calculated in the evaluation unit AE, this correction signal then also potentially serving the purpose of undertaking a correction of the frequency modulation via a programmable drive unit. The drawing
FIG. 1
shows a sensor system wherein the transmission frequency before the delay is mixed onto a lower intermediate frequency in a mixer ZFMI. The local oscillator LO is provided for this purpose, this supplying a lower frequency than the signal source MO.
In any case, the sensor signal mess(t) of a FMCW radar sensor is composed of a superimposition of discrete sine oscillations whose frequencies represent the quantities to be measured (distance, running time, velocity). Given, for example, a filling level sensor, the frequency of the sensor signal is proportional to the difference between container height and filling height; given the presence of a plurality of targets, correspondingly more frequencies occur. When a target moves, than the distance-dependent frequency of the sensor signal has an additional Doppler frequency additively superimposed on it. In the specific case of a CW radar sensor, only this Doppler frequency is detected, this representing the target velocity.
A Fourier transformation is usually employed for interpreting the frequency spectrum of measured signals. In this “Fourier analysis”, the resolution with which neighboring frequencies can be separated is limited due to various influences. Given FMCW sensors, the limiting resolution also derives from the bandwidth of the frequency modulation limited by the technology or by approval stipulations. On the other hand, an optimally high frequency resolution is desirable so that noise frequencies can be separated from the signal frequencies in as far-reaching way as possible.
The application of auto-regressive methods (AR; specific parametric modeling methods) has been discussed in the literature for enhancing the frequency resolution of FMCW sensors (L. G. Cuthbert et al, “Signal Processing In An FMCW Radar For Detecting Voids and Hidden Objects In Building Materials”, in I. T. Young et al (editors), Signal Processing III: Theories and Applications, Elsevier Science Publishers B. V., EURASIP 1986). In fact, a higher frequency resolution can be achieved with modern parametric modeling methods (AR, MA and ARMA; S. M. Kay, “Modem Spectral Estimation, Theory and Application”, Prentice Hall, Englewood cliffs, N.J., 1988) than with Fourier analysis, but only with an adequately great signal-to-noise ratio of the measured signal, which must amount to more than 40 dB given a typical data length of N=100. This prerequisite, however, is generally not met in a FMCW sensor.
Methods for analysis of frequency spectra wherein mathematical investigations deriving from Prony are combined with what is referred to as a singular value decomposition (referred to below as SVD) are described in the text book by S. L. Marple, Jr., “Digital Spectral Analysis With Applications”, Prentice Hall, Englewood Cliffs, N.J., 1988. These methods are always especially successful when the signal x(n) is composed of a finite number p of discrete frequencies f
k
(with the amplitudes c
k
) and noise R(n) according to
x
(
n
)
=
∑
k
=
1
p
c
k
ⅇ
2
π
ⅈ
f
k
n
+
R
(
n
)
,
n
=
1
,
2
,
3
,
…
,
N
,
(
1
)
This is the case in FMCW sensors, particularly in combination with the above-described distortion-correction with a surface wave delay line.
The frequencies f
k
and their plurality p as well as the corresponding (complex) amplitudes c
k
are to be determined from the finite data set [x(
1
), x(
2
), . . . , x(N)]. In the underlying Prony method (without SVD), what are referred to as the FBLP coefficients (forward backward linear prediction) a (k) (k=0, . . . , p) are calculated from x(n) in a first step. These satisfy the equations
(
0
)
x
(
n
)+
a
(
1
)
x
(
n−
1)+ . . . +
a
(
p
)
x
(
n−p
)=0 (2
and, with the complex, conjugated data x*(n) in reverse sequence
a
(
0
)
x
*(
n−p
)+
a
(
1
)
x
*(
n−p
+1)+ . . . +
a
(
p
)
x
*(
n
)=0 (2b)
for n=p+1, p+2, . . . , N.
A total of N-p of such linear equation pairs (2a, 2b) can be erected with the given data set [x(
1
),x(
2
), . . . , x(N)], i.e.
2
(N−p) equations; the a(k) can be determined therefrom. In general (when N>2p applies), the equation system (2a, 2b) is composed of more equations than of unknowns a(k). The method according to the principle of the least square (referred to below as LS) is then utilized for a solution, and, as a result thereof, averaged via noise influences due to the noise components R(n) from equation (1): for an over-defined, linear equation system A
*
x=b, the LS solution is generally established by
x
=(
A
H
*
A
)
−1
*
A
H
*
b
(see, for example, the textbook of Marple, Page 77).
The sought signal frequencies f
k
are then calculated in a second step from the zero settings z
k
of the polynomial P
9
z) formed with the a(k) according to
P
(
z
)
=
∑
k
=
0
p
a
(
k
)
z
p
-
k
(
3
)
When, for instance, z
k
is one of the p zero settings, i.e. P(Z
k
)=0 applies, then
exp(2&pgr;·
i·f
k
)=
z
k
. (4)
Wh
Heide Patric
Storck Eckhard
Vossiek Martin
Bahta Kidest
Schiff & Hardin & Waite
Siemens Aktiengesellschaft
Von Buhr Maria N.
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