Patent Description:
Direction finding of transmitters is a common task, for example for locating a source of noise or locating any other transmitting devices.

During direction finding, usually the signals of the transmitters to be located are received and measured at different measurement points. Based on the measurement, bearings from the measurement points to the transmitters are calculated. Calculating bearings is per se known.

For example, the <CIT> discloses a method and a system for locating a transmitting device using an apparatus that has a first and a second antenna, which are spaced apart. By measuring the bearings with the two antennas and the position of the apparatus at different measurement locations, the transmission source can be located.

The difficulty arises because the location of the transmitters have to be estimated based on the bearings alone. However, in many situations so-called ghost locations occur that, judged from the received signals alone, closely resemble a transmitter even though no transmitter is present at these locations.

As shown in <CIT>, ghost positions and transmitter positions may be detected by a neural network based on bearing-only data from sensors.

The ghost locations pose a serious problem for automated direction finding so that a graphical representation of the bearings is usually outputted to the operator. The operator himself then estimates the location of the transmitters based on the graphical representation. It is therefore the object of the invention to provide a method for direction finding and a system for direction finding that allow an automated and resource efficient way for locating a transmitter.

For this purpose, a method for direction finding of at least one stationary and/or moving transmitter is provided, comprising steps as specified in claim <NUM>.

In the following, the word "transmitter" is used for any source of electromagnetic radiation, especially in radio frequencies. Thus, the term "transmitter" includes sources and emitters as well.

The invention is defined by the appended independent claims and is based on the idea that the complex matter of direction finding can be broken down into two problems that can be handled separately in a very efficient way.

Firstly, calculating the bearings is very well known and can be done with usual microprocessors.

Artificial neural networks are a very efficient tool for analyzing graphical input and for recognizing patterns in graphical input. Thus, creating the graphical representation of the bearings and using the graphical representation as the sole or additional input for the artificial neural network leads to further improvements in terms of the efficiency of and resources needed for the artificial neural network.

In other words, it has been realized that the problem of estimating the locations can be turned into a graphical problem that can efficiently be solved by an artificial neural network, even if ghost locations are present.

It has been realized that for the next step - estimating the location based on the bearings - an artificial neural network can be used that is trained to this specific problem. Such a specialized artificial network can be realized for this step much more cost-efficiently than for the whole problem of direction finding.

Thus, in total a very resource efficient and fully automated method and system for direction finding can be realized.

The location of the measurement point at the time of the measurement is saved, may be determined from a memory as it is not always necessary to determine the location for each measurement anew, in particular when the measurements points are stationary.

Preferably, pre-training of the artificial neural network is performed using a test data set, wherein the test data set comprises sets of bearings and/or graphical representations of bearings and information about the correct location of the at least one transmitter for each set of bearings and/or graphical representation. This way, an efficient artificial neural network specialized for this task is realized.

For example, the test data set includes sets of bearings and/or graphical representations having ghost locations. The correct location may be labelled in the graphical representations.

In an embodiment of the invention, the locations of the measurement points are determined relative to each other and/or absolute, in particular using a receiver for a satellite based location system, and/or that the locations of the at least one transmitter are estimated relative to each of the measurement points and/or absolute. The precision of the estimation is improved that way.

For a precise calculation, the bearings are determined using the direction of signals at the measurement points and/or using the location of the measurement points and/or that the bearings are determined in two and/or three dimensions.

The bearings may very efficiently be determined using an algorithm for calculating bearings, in particular based on the super resolution technique, the Watson-Watt technique and/or the running-fix technique.

In another aspect of the invention, the graphical representation is transferred to the artificial neural network in addition to the bearings.

In order to provide reliable measurement data, the signals may be measured using at least two stationary antennas located at the measurement points and/or at least one movable antenna. The moveable antenna may be moved between measurement points.

For example, each antenna may be part of a direction finding unit.

In another embodiment of the invention, the artificial neural network is a convolutional neural network, a recurrent neural network, a capsule neural network or any combination thereof. These types of artificial neural networks are very efficient for the task of estimating the location based on the calculated bearings.

In order to track moving transmitter, at least the step a) is repeated again at another point in time, wherein a track covered by one or more of the antennas is determined and/or a track covered by one or more of the at least one transmitter is estimated. Of course, one or more of steps b) to e) may also be repeated.

For the ease of use, the operator is provided with the bearings, the graphical representation of the bearings, the estimated locations of the at least one transmitter and/or a map, wherein the map may include the estimated location of the at least one transmitter, the location of the measurement points, geospatial data, the graphical representation of the bearings, the track covered by one or more of the antennas and/or the track covered by one or more of the at least one transmitter. The graphical representation may be laid out over the map.

For the above-mentioned purpose, also system for direction finding of at least one stationary and/or moving transmitter is provided, comprising at least two stationary direction finding units with an antenna each and/or at least one movable direction finding unit with an antenna, a bearing module for determining the bearings from the antenna to each of the at least one transmitter, and a pre-trained artificial neural network for estimating the locations of the at least one transmitter based on the determined bearings. The system is configured to perform the method as described above.

It is also conceivable that each direction finding unit may be provided with a bearing module.

The relative location of direction finding modules may be determined by the location modules themselves.

It is also possible that the absolute location is passed to the system from an external source or the direction finding units comprise a receiver for a satellite based location system.

In another aspect, the system comprises a control unit having the artificial neural network and/or the bearing module.

In one embodiment of the invention, the system comprises a display unit for providing the operator with the bearings, a graphical representation of the bearings, the estimated locations of the at least one transmitter and/or a map, wherein the map may include the estimated location of the at least one transmitter, the location of the measurement points, geospatial data, the graphical representation of the bearings, the track covered by one or more of the antennas and/or the track covered by one or more of the at least one transmitter. This simplifies the use of the system.

For example, the artificial neural network is a convolutional neural network, a recurrent neural network, a capsule neural network or any combination thereof.

The features mentioned for the method are to be seen as features of the system as well and vice versa.

Additional features and advantages of the invention will be apparent from the following description of the embodiments and the attached drawings to which reference is made. In the drawings:.

<FIG> shows a first embodiment of a system for direction finding of at least one stationary and/or moving transmitter.

In the first embodiment, the system comprises three direction finding units <NUM> that each have an antenna <NUM>.

Each direction finding unit <NUM> is also equipped with a receiver <NUM> of a satellite based location system, like GPS, Galileo, GLONASS or Beidou.

In this embodiment, the direction finding unit <NUM> and with that the antennas <NUM> are stationary, i.e. they are not moved during the direction finding process.

Of course, the direction finding unit <NUM> may be mobile in the sense that they can be deployed in different locations for direction finding.

The system <NUM> further has a control device <NUM> comprising a bearing module <NUM>, an artificial neural network <NUM> as well as a display unit <NUM>.

The bearing module <NUM> and the artificial neural network <NUM> may be integrated as separate or a single hardware module, or the bearing module <NUM> and the artificial neural network <NUM> are software modules that are executed by a single hardware module.

In the first embodiment shown in <FIG>, the control device <NUM> is provided as a device separate from the direction finding units <NUM>.

In this embodiment, the control device <NUM> comprises a control unit <NUM> that includes the bearing module <NUM> and the artificial neural network <NUM>.

The control unit <NUM> is, for example, a computer system having a processor and a memory.

The control unit <NUM> may also control the display unit <NUM> to provide information to the operator of the system <NUM>.

The control device <NUM>, especially the control unit <NUM> is connected to the direction finding unit <NUM> for data transmission. This may be achieved by a cable connection or wirelessly via a known communication standard.

The bearing module <NUM> comprises an algorithm that is able to calculate bearings <NUM> based on the signals received by the direction finding unit <NUM> and the antennas <NUM>. The algorithm may be based on per se known techniques for calculating bearings, for example the super resolution technique, the Watson-Watt technique and/or the running-fix technique.

The artificial neural network <NUM> may be a convolutional neural network, a recurrent neural network, a capsule neural network or any combination of the mentioned neural network types.

The artificial neural network <NUM> has been pre-trained so that it is configured to estimate the locations of transmitters based on a given set of bearings <NUM>. The training process will be described in more detail below.

For direction finding, i.e. for determining the locations LT of the transmitters T, the direction finding units <NUM> are set up spaced apart from each other at different locations L1, L2, L3.

The locations L<NUM>, L<NUM>, L<NUM>, more precisely the location of the antennas <NUM> define different measurement points P<NUM>, P<NUM> and P<NUM>.

It is also possible, that one direction finding unit <NUM> is used to which a plurality of antennas <NUM> are connected that are located at the different measurement points P<NUM>, P<NUM> and P<NUM>.

Once the direction finding units <NUM> have been set up in step S1 (see <FIG>), the absolute locations are determined with the receiver <NUM>, and the locations L<NUM>, L<NUM> and L<NUM> are provided to the control device <NUM>, more precisely the control unit <NUM> (step S2).

It is also possible that the absolute location is supplied to the control unit <NUM> or the direction finding unit <NUM> by an external source, like an external receiver of a satellite based location system.

Of course, the first step S1 and possible also the second step S2 are not necessary if permanently fixed direction finding units <NUM> are used. Instead, the locations L<NUM>, L<NUM> and L<NUM> of the measurement points P<NUM>, P<NUM> and P<NUM> can be determined from the memory.

In step S3, each of the direction finding units <NUM> measures the signals that are emitted from the transmitters T so that the measurements of the signals of the transmitters T are performed at the three measurement points P<NUM>, P<NUM> and P<NUM>.

The measured signals are then transferred to the control unit <NUM>, more precisely the bearing module <NUM>.

In step S4, the bearing module calculates the bearings <NUM> from each of the direction finding units <NUM> to each of the transmitters T.

In other words, the bearing module <NUM> calculates the bearings <NUM> from each of the measurement points P<NUM>, P<NUM> and P<NUM> to each of the transmitters T.

The bearing module <NUM> or the control unit <NUM> may then create a graphical representation of the bearings in step S5.

An exemplary graphical representation is shown in <FIG> and may comprise a picture or a bitmap in which the bearings <NUM> are drawn in the correct relative location and orientation to each other.

To create this graphical representation, the locations L<NUM>, L<NUM> and L<NUM> of the measurement points P<NUM>, P<NUM> and P<NUM> are used.

In the next step S6, the graphical representation is transferred to the artificial neural network <NUM>. The artificial neural network <NUM> estimates in step S7 the locations LT of the transmitters T based on the graphical representation.

Because the graphical representation is used, the artificial neural network <NUM> may be designed for image recognition and does not need to process or take into account information about calculating bearings or wave propagation. Thus, the artificial neural network <NUM> can be very efficient and quick.

In other words, the artificial neural network <NUM> receives the picture, i.e. the graphical representation, and marks the regions in the picture where the transmitters T are estimated to be.

The estimated location LT of the transmitters T may then be transformed into the absolute locations LT of the transmitters T.

It is also conceivable, that only the relative location LT of the transmitters T with respect to the measurement points P<NUM>, P<NUM>, P<NUM> is estimated and used.

It is of course possible that the artificial neural network <NUM> is able to process the bearings <NUM> without the need to provide the bearings as a graphical representation.

After the locations LT have been estimated, the control unit <NUM> controls the display unit <NUM> to display the graphical representation with the estimated locations of the transmitters LT, as can be seen in <FIG> (step S8).

In <FIG>, the artificial neural network <NUM> has located the two transmitters T correctly as indicated by the circles.

Further, the point <NUM>, which is a so-called ghost location, at which another transmitter may be suspected, is correctly identified as such a ghost point without a transmitter T.

This graphical representation may be laid out over a map of the region around the transmitters T. The map may be enriched with geospatial information.

Further, also the measurement points P<NUM>, P<NUM>, P<NUM> may be shown on the display unit <NUM> laid over the map.

Thus, a method for quickly estimating and visualizing the location of the transmitters T is realized. The method can be done in real-time because the algorithm to calculate the bearings and the artificial neural networks <NUM> are specialized for a specific task and can therefore be realized with high efficiency.

Before the artificial neural network <NUM> can be used for estimating the location of the transmitters T, it has to be trained. The training step or learning step A is performed before the method can be executed.

The training is done using a test data set. The test data set comprises different sets of bearings <NUM> and information about the location LT of the transmitters L. For example, the test data set consists of a plurality of graphical representations of the bearings <NUM> in which the location LT has already been highlighted or labelled. The sets of bearings <NUM> or the graphical representation may include ghost locations.

The bearings <NUM> or graphical representations of the test data set are then fed forward into the artificial neural network <NUM> and the results of the estimated locations are compared to the actual, known locations LT of the test data set.

The deviation or error in the location LT is then used to adjust the artificial neural network <NUM>, for example by backward feeding the artificial neural network <NUM> with the error. Learning methods for artificial neural networks <NUM> are per se known.

After the artificial neural network <NUM> has been trained with a sufficient number of sets of test bearings <NUM> and/or test graphical representations, the artificial neural network <NUM> is then able to repeatedly estimate the locations LT of the transmitters T based on the bearings/the graphical representations alone. The artificial neural network is then pre-trained.

<FIG> and <FIG> show further embodiments of the system and the method that correspond essentially to the first embodiment of <FIG>. Thus, only the differences are explained in the following, wherein same reference signs are used for identical or like parts.

In the embodiments shown in <FIG>, the control device <NUM> of the system <NUM> is not realized as a separate device. Instead, the control unit <NUM> and the display unit <NUM> are a part of one of the direction finding units <NUM>.

It is also possible that all of the direction finding units <NUM> comprise a control unit <NUM> with an artificial neural network <NUM> and/or a bearing module <NUM> so that the system <NUM> can be controlled from any one of the direction finding units <NUM>.

Further, each of the direction finding units <NUM> comprises its own bearing module <NUM> so that the control unit <NUM> does not need a bearing module anymore.

It also is possible that the direction finding units <NUM> do not have a receiver <NUM> but a communication module <NUM>. The communication modules <NUM> of each of the direction finding units <NUM> communicate with each other in order to determine the relative location L<NUM>, L<NUM>, L<NUM> with respect to each other. This information is sufficient to allow the estimation of the relative location LT of the transmitters T.

In the situation shown in <FIG>, the system <NUM> is used to locate and track a moving transmitter T instead of a plurality of stationary transmitters as described before.

The method for this second situation differs from the method described with respect to <FIG> in that the signals of the transmitter T are measured at at least two different points in time, i.e. step S3 is repeated.

For example, the signals of the transmitter T are measured at a first point in time for the first time. At this point in time, the transmitter T is located at location LT1, indicated by the dashed square in <FIG>.

At a second, later point in time, the measurement of the signals of the transmitter T are repeated. Now, the transmitter T is at a different location LT2 indicated by the solid square in <FIG>.

For each of the measurements, the bearing modules <NUM> of the direction finding units <NUM> calculate the bearings <NUM> and transfer the bearings to the control unit <NUM>. Of course, the bearings <NUM> are associated with the point in time at which the respective measurement was taken.

Using the artificial neural network <NUM>, the estimated locations LT1, LT2 of the transmitter T are determined.

The control unit <NUM> may then show the estimated locations LT1, LT2 and a track the transmitter T using the display unit <NUM> in addition to the visualization explained with respect to <FIG>.

Of course, more than two measurements of the signals of the transmitter T can be performed to achieve a finer and/or longer tracking of the location of the transmitter T.

The calculation of the bearings <NUM> and/or the estimation of the location of the transmitter T can be performed after each measurement or in lump after all measurements are completed.

It is also possible, that the position of the transmitter T is tracked in real-time when continuously repeating the method steps.

In the third embodiment shown in <FIG>, the system <NUM> comprises a single direction finding unit <NUM>.

In contrast to the embodiments described before, the direction finding unit <NUM> of this embodiment is moveable, meaning that the direction finding unit <NUM> is moved while the location of the transmitters T is determined.

It is conceivable that only the antenna <NUM> is moved instead of the whole direction finding unit <NUM>.

The direction finding unit <NUM> used in this third embodiment is similar to the once shown in the second embodiment of <FIG>, i.e. it comprises the bearing module <NUM>, the artificial neural network <NUM> and the control unit <NUM>.

Of course, it is also possible that the control unit <NUM> is provided in a separate control device <NUM>.

For locating the transmitters T, the direction finding unit <NUM> is deployed at a first measurement point P<NUM> (step S1'). At this first measurement point P<NUM> (indicated by the dashed lines in <FIG>) the location L<NUM> of the first measurement point P<NUM> is then determined using a receiver <NUM> for a satellite based location system (step S2).

As before, in step S3, the signals of the transmitters T are measured and received.

Then, the direction finding unit <NUM> is redeployed to a second measurement point P<NUM>, the location L<NUM> of this measurement point P<NUM> is determined and the signals of the transmitters T are measured once again. Thus, the steps S1', S2 and S3 are repeated.

The three steps may be repeated a third time or even more often.

For each of the measurements, the bearings <NUM> are determined. As soon as the bearings <NUM> of the second measurement are determined, the bearings <NUM> or a graphical representation thereof can be fed to the artificial neural network <NUM> to estimate the locations LT of the transmitters T (steps S4 to S7).

Thus, it is possible to estimate the location of the transmitters T with a single direction finding unit <NUM>.

The control unit <NUM> may output on the display unit <NUM> the track of the direction finding unit <NUM> in addition to the other information (step S8).

The three embodiments of the system <NUM> and the method shown above are merely examples. It is of course possible to combine the features of the shown embodiments in any given way. For example, one or more moving transmitters T may also be located using the system of the first embodiment.

For the sake of simplicity, the explanations and the Figures are restricted to two dimensions. Thus, the bearings <NUM> are calculated in two dimensions and the locations LT of the transmitters T are also estimated in two dimensions. The dimensions for the absolute location are the longitude and the latitude.

Claim 1:
Method for direction finding of at least one stationary and/or moving transmitter (T), comprising the following steps:
a) measuring the signals emitted by each of the at least one transmitter (T) at at least two different measurement points (P<NUM>, P<NUM>, P<NUM>),
b) determining the location of the measurements points (P<NUM>, P<NUM>, P<NUM>) at the time of the measurement, and
c) determining the bearings (<NUM>) from the measurement points (P<NUM>, P<NUM>, P<NUM>) to each of the at least one transmitter (T),
characterized by
d) creating a graphical representation of the bearings (<NUM>),
e) transferring the graphical representation of the bearings (<NUM>) to a pre-trained artificial neural network (<NUM>), and
f) estimating the locations (LT; LT1, LT2) of the at least one transmitter (T) by the artificial neural network (<NUM>) based on the graphical representation of the bearings (<NUM>).