Method for detecting a radar target

The invention relates to a method of detecting a radar target, especially a sea target, in the presence of clutter caused by ocean waves in particular. To detect a radar target within a predeterminable region, first a measurement window corresponding to this region is formed in the video range of the radar system, and a frequency distribution is determined for all pulse-height values (amplitudes) within the measurement window. The average value and the standard deviation can be determined from the distribution; from these values, a hit-recognition threshold is determined and used to decide whether a pulse-height value is to be associated with a radar target to be detected.

The invention is based on a method of detecting a radar target, as defined 
in the preamble to claim 1. 
The term "radar target" as used in this patent application is a synonym 
(abbreviation) for the expression "a target that can be detected by means 
of a radar system." "Target" refers to an object that reflects radar waves 
and possesses at least one predeterminable characteristic, for example, it 
exceeds a predeterminable minimal radar backscatter cross section. 
The invention can be applied particularly to the detection of sea targets, 
such as ships, that are surrounded by possible so-called (radar) clutter 
caused by, for example, ocean waves and/or environmental influences such 
as clouds and/or precipitation. 
In the detection of sea targets in particular, it is known to divide a 
region to be monitored into cells (measurement windows) having a 
predeterminable area. Each cell contains a predeterminable number of 
so-called radar resolution cells. It is obvious to first select at least 
one measurement window that contains only (possibly time-dependent) 
clutter, and to determine a so-called clutter threshold value (in the 
video signal) for this cell (in the video signal of the radar system) that 
is based on, for example, the formation of a clutter average value within 
the window. If a predeterminable (amplitude) pulse exceeds the clutter 
threshold value in the video signal, this pulse is first considered a 
possible echo of a radar target (sea target), and may be supplied to a 
further evaluation, such as a classification and/or identification, if 
needed. 
It is apparent that, in the detection of sea targets, the associated 
clutter threshold value is dependent on both time and location. This is 
because the motion of water waves can change over time, for example due to 
an impending storm. Furthermore, the wave motion and therefore the clutter 
can vary by location, at least from the viewing direction of a radar 
system; for example, they may assume different values in the close and far 
ranges. Such conditions interfere with and impede the detection of sea 
targets in particular. 
It is the object of the invention to improve a generic method so as to 
permit a reliable determination of a clutter threshold value, especially 
in the detection of sea targets. 
This object is accomplished by the features disclosed in the characterizing 
portion of claim 1. Advantageous embodiments and/or modifications ensue 
from the further claims. 
A first advantage of the invention is that the determination of the clutter 
threshold in a wide range is independent of (radar) targets located within 
the (measurement) window selected for determining the clutter threshold 
value. It is advantageously not necessary to extract the target amplitudes 
(in the video range) present in this (measurement) window, which 
amplitudes are associated with the (radar) targets, for example by 
reducing the (measurement) window and increasing the number to be 
evaluated. In other words, it is advantageously not necessary to know 
where (radar) targets are located within the (measurement) window, because 
an extraction is only possible with such (difficult-to-obtain) knowledge. 
It is advantageously possible to reliably determine the clutter threshold 
value even if the component of the target amplitudes assumes a value of 
about 50% of all amplitudes to be evaluated in a selected (measurement) 
window. 
A second advantage is that the method is virtually independent of the 
design of the radar system, particularly the target extractor used in the 
system. The extractor generally includes a digital data-processing system 
that preferably has a programmable microprocessor. If, for example, the 
method of the invention is to be employed in an existing radar system, it 
is advantageously only necessary to change the program (software) of the 
data-processing system. Thus, no assemblies (hardware) need to be changed, 
which is also advantageous. 
Further advantages ensue from the following description.

The invention is based on the use of a radar-target extractor that is known 
per se, and is also referred to hereinafter as sea-target extractor. At 
this time, a sea-target extractor of this type usually includes a digital 
data-processing system. A sea-target extractor has the task of selecting 
the echo signals that are to be allocated to targets to be detected, for 
example ships, from a digitized video signal that corresponds to the 
received (radar) echo signals. In other words, it must distinguish between 
echo signals that originate from a (sea) target and those that are caused 
by clutter (swell) in particular. 
The sea-target extraction performed in such a sea-target extractor 
essentially involves a step-wise reduction in the incoming data stream 
until, eventually, only data that are to be allocated to a sea target are 
outputted. Predeterminable threshold values with which the transmission of 
interfering data is prevented under predeterminable conditions are used to 
reduce the data stream. 
To suppress clutter that is based particularly on the ocean swell (wave 
motion), it is advisable to use a clutter threshold value that is adapted 
to the swell and is based on a clutter model in which the clutter pulse 
heights are described by a (pulse height) frequency distribution. A 
frequency distribution of this type is completely characterized by the 
parameters of the average value M and the scatter S. 
In sea-target extraction, it is useful to use a central threshold value, 
referred to as the hit-recognition threshold TEK, i.e., all echo pulses 
(in the video signal) that exceed the hit-recognition threshold TEK are 
considered to be associated with a sea target. The hit-recognition 
threshold TEK is linked with a false-alarm probability P.sub.fa 
corresponding to the formula 
##EQU1## 
where f(x) represents the constant distribution density function of the 
present clutter distribution VT. If the swell, and therefore the 
associated clutter distribution, correspond to, for example, a Gaussian 
distribution with the average value M=.mu. and the standard deviation 
S=.delta. it is possible to determine the hit-recognition threshold TEK 
from this Gaussian distribution according to the formula 
EQU TEK=.mu.+.delta..multidot.K(P.sub.fa), (2) 
where K is a constant. 
For example, with a predeterminable false-alarm probability P.sub.fa 
=10.sup.-2, the value K(P.sub.fa =10.sup.-2)=2.32 results for the constant 
K. 
The determination of the hit-recognition threshold TEK is explained below: 
If, for example, a sea region located at a predeterminable range and having 
a predeterminable area is scanned by a radar system, the region can be 
represented as a measurement window on a display screen. The measurement 
window contains pulse-height values (echo signals) that correspond to 
clutter and target amplitudes (in the video range). 
If it is assumed, in an ideal case, that only clutter amplitudes (i.e., 
only clutter-based amplitudes) are present, the average value M and the 
standard deviation S are determined from the associated (clutter) 
pulse-height values. From this, the hit-probability threshold TEK is 
determined corresponding to the formula 
EQU TEK=Function (M, S, P.sub.fa) . (3) 
If the present distribution of the (clutter) pulse-height values is 
Gaussian, Formula (3) gives way to Formula (2), and the TEK can be 
determined in the described manner. 
In real cases, however, interferences are present, for example due to a 
deformation of the clutter distribution, that is, the distribution is 
asymmetrical with respect to the average value M, and/or unknown (target) 
pulse-height values (originating from targets to be detected) are present 
in the measurement window. 
In the method of the invention, all pulse-height values lying within a 
predeterminable measurement window are evaluated, that is, pulse-height 
values that correspond to clutter and those that are associated with 
targets to be detected. For all pulse-height values present in quantized 
(digitized) form inside the measurement window, the frequency of the pulse 
heights is now determined, meaning that the associated 
distribution-density function VDF is determined. From this, 
predeterminable criteria, which will be described below, are determined, 
and from these, the average value M and the standard deviation S are 
determined. These are used to determine the hit-recognition threshold TEK 
corresponding to Formula (3). 
These criteria are based on, for example, empirical experiment values, 
where, for example, 
a known deformation ("shortening") of the present pulse-height distribution 
of all pulse-height values within the current measurement window is 
considered; 
the present bit number of the analog-digital converter used for generating 
the present pulse-height values is taken into consideration; the selected 
bit number determines whether the pulse-height values are determined with 
a fine or coarse gradation. 
The determination of the average value M and the standard deviation S with 
consideration of the aforementioned, exemplary criteria is described in 
detail below in conjunction with the schematic figures. 
It is assumed hereinafter that the echo signals (clutter and/or target 
echoes) that a radar system receives from a predeterminable measurement 
window are down-converted into the video range in a manner known per se, 
resulting in an amplitude-modulated, analog video signal. If needed, this 
signal can subsequently be filtered in analog fashion, for example by 
means of a threshold circuit, particularly a CFAR circuit embodied as a 
digital component, so that, for example, predeterminable noise components 
are removed from the analog video signal. An analog/digital converter now 
converts this low-noise, analog video signal into an associated, digitized 
video signal. The analog/digital converter uses an analog amplitude value 
to generate an associated, digital, quantized amplitude value AMP 
(abscissas in FIGS. 1 and 2). The maximum number L of possible amplitude 
values is dependent on the bit number of the used analog/digital 
converter, corresponding to the formula L=2.sup.bit number -1. 
FIG. 1 shows the frequency H.sub.i (ordinate), where i=0, 1, 2, . . . , L, 
of the distribution-density function VDF (solid line in FIG. 1) for a 
current, predeterminable measurement window and the quantized amplitude 
values AMP therein, which can be caused by clutter and targets to be 
detected, as a function of the possible quantized amplitude values AMP 
(abscissa), which are determined by the bit number of the analog/digital 
converter. It can be seen from FIG. 1 that present targets having an 
amplitude value greater than 13, for example, are not readily 
recognizable, in other words, in the form of "peaks" in the frequency 
distribution, but are distributed over a plurality of amplitude values, 
that is, a "blurred" representation is present in the amplitude 
distribution. At this time, commercially-available analog/digital 
converters that can be used for these applications, and have bit numbers 
of, for example, 4, 8, 16 or 32, can be obtained inexpensively. In FIG. 1, 
purely for the sake of a graphic depiction, it is assumed that a threshold 
value of zero is present in the aforementioned threshold circuit, and that 
the clutter-based, quantized amplitude values possess a Gaussian 
distribution whose average (amplitude) value M lies at the quantized 
amplitude value 7. The quantized amplitude values associated with targets 
to be detected lie at quantized amplitude values that are greater than 13. 
Clearly, a very crude quantization is present for the amplitude values. 
It can be seen from FIG. 1 that no amplitude values are associated with 
clutter, but, around the average value M, they possess significantly 
higher frequencies H.sub.i than the targets to be detected. 
The following method steps are performed in the method: 
1. For all echo signals (clutter and targets) present in the measurement 
window, the frequencies H.sub.i associated with a respective, quantized 
amplitude value are determined at the associated, digitized amplitude 
values, resulting in a distribution-density function. In the process, the 
frequencies H.sub.i are given in percentage values, for example, with the 
sum of all frequencies H.sub.i within the measurement window equaling 
100%. For the average value M, with the assumption of a 
Gaussian-distributed clutter of the quantized amplitude values, the 
formula 
##EQU2## 
applies, where i=0, 1, 2, . . . L and L=2.sup.bit number -1. The 
following formula applies for the standard deviation S, with the 
assumption of a Gaussian-distributed clutter: 
##EQU3## 
This means that the average value M lies at the quantized amplitude value 
at which the distribution function (VF) of the quantized amplitude values 
assumes the value of 0.5. 
The amplitude value M+S then lies at the quantized amplitude value at which 
the distribution function (VF) of the quantized amplitude values assumes 
the value of 0.841345. 
The given 50% mark (for the average value M) and the 84% mark (for the 
standard deviation) are only applicable for a Gaussian distribution of the 
clutter. If, however, as dictated by the selection of the signal 
processing of the clutter, a distribution other than the Gaussian 
distribution is present, the "50% mark" and the "84% mark" must be adapted 
to this distribution. In theory, this is possible for any distribution. 
2. Formulas (4) and (5) yield the following instruction for the 
application: 
Starting from the smallest quantized amplitude value H.sub.0 (i=0), the 
frequencies H.sub.i of the present distribution-density function VDF are 
added until 50% of the quantized amplitude values present in the 
measurement window have been detected. The quantized amplitude value im 
associated with the average value M lies at this location. 
The frequencies H.sub.i are then added until 84.1345% of the quantized 
amplitude values present in the measurement window have been detected. The 
quantized amplitude value i.sub.M+S associated with the value M+S lies at 
this location. 
The standard deviation S can then be determined from the formula 
EQU S=i.sub.M+S -i.sub.M .(6) 
3. The hit-recognition threshold TEK is correspondingly ascertained with 
the average value M determined in this manner and the 
subsequently-determined standard deviation S, according to Formula (3). 
Formulas (4) and (5) apply for a model in which a Gaussian distribution of 
the clutter is assumed. If, in contrast, the clutter is not 
Gaussian-distributed, but is associated with (corresponds to) a different 
distribution, Formulas (2), (4) and (5) must be adapted to this 
distribution. 
The described method of determining the average value M and the standard 
deviation S has considerable advantages over other M/S estimation methods 
in use: 
a) The described pulse-height analysis is not limited to a Gaussian clutter 
distribution, but can be applied to virtually any others, for example to a 
clutter distribution possessing a plurality of (relative) maxima of the 
quantized amplitude values, the values being virtually identical in 
height. In such a case, it is only necessary to adapt the criteria 
corresponding to Formulas (4) and (5) to the present distribution, for 
example by means of empirically-determined weighting factors. If the 
distribution is not Gaussian, the determination of the hit-recognition 
threshold TEK must be modified by a corresponding, different formula from 
Formula (2). This is represented generally by the term "Function(M, S, 
P.sub.fa)" in Formula (3). 
b) The method permits a technically simple, precise and fast determination 
of the average value M and the standard deviation S, because only 
mathematically-simple calculation operations need be performed. 
c) Advantageously, no target amplitudes are factored into the estimation of 
the average value M and the standard deviation S, because target 
amplitudes are large, i.e., greater than M+S, which is greater than M, and 
because the addition of the frequencies H.sub.i begins at the smallest 
quantized amplitude value (H.sub.0). 
d) For a precise estimation of the average value M and the standard 
deviation S, it is advantageously unnecessary for a complete 
distribution-density function VDF to be present for clutter; rather, 
corresponding to FIG. 2, a distribution that is "shortened" downward can 
also be present. This type of distribution occurs, for example, when the 
noise and/or CFAR thresholds mentioned at the outset are so high that they 
can also be used to suppress clutter components. 
It is apparent that, for the described method, it is only necessary that at 
least half of the distribution function be available. In such a 
"shortened" distribution-density function VDF, a frequency H.sub.0 of 
non-interfering height, e.g. H.sub.0 =30%, is associated with the smallest 
quantized amplitude value. Starting from this high frequency H.sub.0, the 
addition is continued in the method until the values given in Formulas (4) 
and (5) are attained. In an extreme case, even the value of i.sub.M =0 is 
permissible. 
e) Advantageously, no additional conversion, for example by means of 
tables, is necessary for converting the measured values for the average 
value M and the standard deviation into the associated, correct 
theoretical values, particularly in a "shortened" distribution-density 
function. 
An obvious advantage of the described method is that, nearly each time, 
only the clutter distribution is detected, because it is known, for 
example from empirical measurements, that the target pulse-height values 
associated with possible targets occur with a significantly lower 
frequency, but with considerably higher quantized amplitude values, than 
the clutter pulse-height values--see FIGS. 1 and 2. 
Clutter amplitudes appear at smaller amplitude values in the frequency 
distribution. In contrast, target amplitudes generally appear at 
considerably larger amplitude values. 
If, in applications, it is now possible that more than approximately 8% of 
the amplitudes located in a measurement window are to be assessed as 
target amplitudes, for example, in the unfavorable combination of a small 
measurement window and remote scanning, the skewing of the measurement of 
the values M and S by the real target amplitudes is avoided. This is 
effected in that, in this case, the "100% mark" in Formulas (4) and (5) is 
not defined by the addition up to the value L, that is, 
##EQU4## 
but rather by the fact that the "100% mark" is established by a summation 
up to a value P, with P&lt;L, for example P=L/2. The establishment of the 
mark is based on the fact that the probability that clutter amplitudes 
larger than or equal to L/2 will occur is negligible, particularly in the 
case of a "shortened" distribution. 
It is apparent that, in the method, it is advantageously not necessary to 
determine at which locations within the measurement window targets are 
located, and at which locations only clutter is present. This means that 
it is not necessary to divide the measurement window into smaller 
sub-windows that would otherwise be required for recognizing targets. 
The invention is not limited to the described embodiments, but can be 
analogously applied to others. Thus, it is possible to use the method to 
detect general radar targets that are surrounded by so-called fixed-target 
and/or moving-target clutter.