EP2936192A1

EP2936192A1 - Methods and apparatus for a radar having windfarm mitigation

Info

Publication number: EP2936192A1
Application number: EP12890501.5A
Authority: EP
Inventors: Xiaoli LU; Oliver HUBBARD; Jian Wang; Emily Wang
Original assignee: Raytheon Canada Ltd
Current assignee: Raytheon Canada Ltd
Priority date: 2012-12-21
Filing date: 2012-12-21
Publication date: 2015-10-28
Also published as: EP2936192A4; WO2014094106A1

Abstract

Methods and apparatus for processing radar data including obtaining first and second sets of radar return signals concurrently, removing wind turbine signals adaptively from the first and second sets of radar return signals, and detecting targets in the first and second sets of radar return signals, and identifying detected targets due to clutter.

Description

METHODS AND APPARATUS FOR A RADAR

HAVING WINDFARM MITIGATION

BACKGROUND

In general, there are two types of radars that are used in a conventional civil Air Traffic Control (ATC) system: a Primary Surveillance Radar (PSR) and a Secondary Surveillance Radar (SSR). The PSR transmits pulses and reports the range and azimuth of detected objects in a given surveillance area. The detected objects include aircraft and non-aircraft objects. The SSR transmits interrogation signals to aircraft in the given surveillance area and receives information from the aircraft that have operational transponders responsive to the interrogation signals. The information received by the SSR includes the range, azimuth, identity and height of aircraft that reply to the interrogation signals.

However, some aircraft, such as those used by organized crime, those hijacked or military aircraft, may deliberately turn off their transponders. Other aircraft may have a damaged transponder. Furthermore, non- aircraft airborne objects, such as birds, cannot respond to the interrogation signals. As a result conventional SSR cannot detect these non-aircraft objects or aircraft that do not respond to the SSR interrogation pulses, which can be a serious problem. For example, in the United States, there were over 9,000 reported wildlife strikes to civil aircraft in 2010. Most of these collisions occurred near airports at low elevations in the glide path where aircraft were either landing or taking off. This is also the area in which aircraft are most vulnerable to collisions.

In the past decade many countries, such as the UK, Netherlands, Germany, Canada and the U.S., have launched programs to deploy large wind farms comprising many hundreds of wind turbines, which provide alternative sources of energy. However, this has raised many concerns from air traffic control (ATC) and military authorities since radar returns from large concentrations of wind turbines have the potential to distract and confuse air traffic controllers. The presence of wind farms within the field of view of PSR presents a considerable design challenge. Echoes originating from wind turbine structures have similar characteristics to those of aircraft and may be significantly stronger in amplitude. For example, the echo may dominate and mask return originating from aircraft resulting in a "radar blind zone" and missing detections. In addition, the aircraft track may be seduced away from its correct path due to misassociation with an echo originating from the wind farm. Further, the echoes originating from the wind farm may result in the generation of a high rate of false track reports in the vicinity of the farm.

For example, very large wind turbines have a Radar Cross Section (RCS) of up to 25 dBsm on average and in some cases even as high as 50 dBsm, whereas the typical RCS of a commercial aircraft during approach (i.e. when landing) ranges from about 3 dBsm to about 10 dBsm. In addition, the Doppler frequencies of the radar returns from the rotating blades of a wind turbine are similar to the Doppler frequencies of an approaching aircraft (e.g., 1671 Hz at a frequency of 2,800 MHz which corresponds to a velocity of 174 knots while the approach speed of a commercial aircraft is about 150 knots). Even for these aircraft having higher radial velocity, their returns can fall back to the Doppler band occupied by wind turbine returns because of frequency aliasing due to low pulse repetition frequency (PRF) of the PSR. Thus, radar returns from wind turbines have similar Doppler characteristics and larger RCS than aircraft and can completely mask a radar return from an aircraft making it almost invisible to a radar system in the vicinity of a wind farm. Wind farm regions result in a significantly lower probability of detection of aircraft by a civil ATC radar system than in adjacent non-wind farm regions. SUMMARY

The present invention provides method and apparatus for a radar system that mitigates windfarm interference in accordance with exemplary embodiments of the invention. In one embodiment, a radar system uses primary radar data in target detection and tracking. SSR data can be used to calibrate the high/low beam of the primary surveillance radar (PSR). While exemplary embodiments of the invention are shown having particular configurations, components, and processing, it is understood that embodiments of the invention are applicable to radars in general in which windfarm mitigation is desirable.

In one aspect of the invention, a method of processing radar data comprises: obtaining first and second sets of radar return signals concurrently, removing, using a computer processor, wind turbine signals adaptively from the first and second sets of radar return signals, detecting targets in the first and second sets of radar return signals, and identifying detected targets due to clutter.

The method can further include one or more of the following features: the first set of radar return signals comprise low beam data and the second set of radar return data comprises high beam data, the removing step further comprises: estimating and determining the wind turbine signals from a first one of the first and second sets of return data and applying estimated parameters on the other of the first and second sets of return data, the removing step further comprises:

performing optimization to improve the wind turbine signals estimations prior to clutter cancellation, the removing step further comprises: utilizing prior knowledge on a number of the wind turbines and geophysical locations of the wind turbines, and/or utilizing the prior knowledge on the number of the wind turbines and the geophysical locations of the wind turbines and a candidate clutter signal height information to form an adaptive active turbine list, the identifying step further comprises: determining the detected targets common in both the first and second sets of radar return signals, comparing amplitudes associated with the common detected targets to an amplitude threshold, and identifying the common detected targets with amplitudes greater than the amplitude threshold as potential detected targets, the identifying step further comprises: estimating a height of the potential detected targets, and maintaining the potential detected targets having a height less than a height threshold as potential detected targets due to at least one of wind turbine clutter and bird clutter, setting the height threshold as a function of a range of the potential detected targets based on height estimation accuracy, the identifying step further comprises: comparing range and azimuth values of the potential detected targets due to wind turbine clutter with a range gate azimuth map having known locations of wind turbines in the vicinity of the radar system, and maintaining the potential detected targets having range and azimuth values corresponding to a wind farm region as potential detected targets due to wind turbine clutter, detecting targets in a given set of radar return signals for a range cell comprises: pre-processing the given set of radar return signals, performing Doppler processing on the pre-processed given set of radar return signals to produce several Doppler outputs, performing CFAR detection on the several Doppler outputs to produce several CFAR detection results, and merging the CFAR detection results to obtain detection results for the range cell, generating a CFAR threshold for performing the CFAR detection for a given Doppler output by: averaging values in an early range window prior to the range cell to obtain a first average, averaging values in a late range window after the range cell to obtain a second average, selecting the smaller of the first and second averages to produce an initial value, determining a clutter level in a clutter map that corresponds to the range cell and the given Doppler output, setting the CFAR threshold to the larger of the initial value and the clutter level, and adding a constant based on a desired false alarm rate to the CFAR threshold, performing CFAR detection for a given Doppler output comprises: generating a CFAR threshold based on a clutter map that corresponds to the given Doppler output, wherein the clutter map includes clutter information due to at least one of wind turbines and birds, and excluding the wind turbine containing cells in the range windows prior to averaging, obtaining the first and second sets of radar return signals concurrently comprises generating a high beam pattern and a low beam pattern for receiving radar return signals, wherein the high and low beam patterns overlap with one another and produce a stable delta gain pattern, generating the high and low beam patterns to produce the stable delta gain pattern with an overlap region that includes the angle of ascent and descent of an aircraft, using a Primary Surveillance Radar (PSR) to obtain the first and second sets of radar return signals concurrently, detect targets in the first and second sets of radar return signals, identify detected targets due to clutter, and generate first detection results, and using a Secondary Surveillance Radar (SSR) to generate second detection results, and combining the first and second detection results to produce a combined report, and/or generating detection information based on the target detections in the first and second sets of radar return signals for a plurality of scans, generating plots based on the detection information for the plurality of scans, and generating tracks of the detected targets taking into account detection information related to the detected targets that are due to clutter. In another aspect of the invention, an article comprises: a computer readable medium containing stored non-transitory instructions that enable a machine to: obtain first and second sets of radar return signals concurrently, remove wind turbine signals adaptively from the first and second sets of radar return signals, detect targets in the first and second sets of radar return signals, and identify detected targets due to clutter. The article can include code for one or more of the following features: the first set of radar return signals comprise low beam data and the second set of radar return data comprises high beam data, the removing step further comprises: estimating and determining the wind turbine signals from a first one of the first and second sets of return data and applying estimated parameters on the other of the first and second sets of return data, the removing step further comprises:

performing optimization to improve the wind turbine signals estimations prior to clutter cancellation, the removing step further comprises: utilizing prior knowledge on a number of the wind turbines and geophysical locations of the wind turbines, and/or utilizing the prior knowledge on the number of the wind turbines and the geophysical locations of the wind turbines and a candidate clutter signal height information to form an adaptive active turbine list, the identifying step further comprises: determining the detected targets common in both the first and second sets of radar return signals, comparing amplitudes associated with the common detected targets to an amplitude threshold, and identifying the common detected targets with amplitudes greater than the amplitude threshold as potential detected targets, the identifying step further comprises: estimating a height of the potential detected targets, and maintaining the potential detected targets having a height less than a height threshold as potential detected targets due to at least one of wind turbine clutter and bird clutter, setting the height threshold as a function of a range of the potential detected targets based on height estimation accuracy, the identifying step further comprises: comparing range and azimuth values of the potential detected targets due to wind turbine clutter with a range gate azimuth map having known locations of wind turbines in the vicinity of the radar system, and maintaining the potential detected targets having range and azimuth values corresponding to a wind farm region as potential detected targets due to wind turbine clutter, detecting targets in a given set of radar return signals for a range cell comprises: pre-processing the given set of radar return signals, performing Doppler processing on the pre-processed given set of radar return signals to produce several Doppler outputs, performing CFAR detection on the several Doppler outputs to produce several CFAR detection results, and merging the CFAR detection results to obtain detection results for the range cell, generating a CFAR threshold for performing the CFAR detection for a given Doppler output by: averaging values in an early range window prior to the range cell to obtain a first average, averaging values in a late range window after the range cell to obtain a second average, selecting the smaller of the first and second averages to produce an initial value, determining a clutter level in a clutter map that corresponds to the range cell and the given Doppler output, setting the CFAR threshold to the larger of the initial value and the clutter level, and adding a constant based on a desired false alarm rate to the CFAR threshold, performing CFAR detection for a given Doppler output comprises: generating a CFAR threshold based on a clutter map that corresponds to the given Doppler output, wherein the clutter map includes clutter information due to at least one of wind turbines and birds, and excluding the wind turbine containing cells in the range windows prior to averaging, obtaining the first and second sets of radar return signals concurrently comprises generating a high beam pattern and a low beam pattern for receiving radar return signals, wherein the high and low beam patterns overlap with one another and produce a stable delta gain pattern, generating the high and low beam patterns to produce the stable delta gain pattern with an overlap region that includes the angle of ascent and descent of an aircraft, using a Primary Surveillance Radar (PSR) to obtain the first and second sets of radar return signals concurrently, detect targets in the first and second sets of radar return signals, identify detected targets due to clutter, and generate first detection results, and using a Secondary Surveillance Radar (SSR) to generate second detection results, and combining the. first and second detection results to produce a combined report, and/or generating detection information based on the target detections in the first and second sets of radar return signals for a plurality of scans, generating plots based on the detection information for the plurality of scans, and generating tracks of the detected targets taking into account detection information related to the detected targets that are due to clutter.

In a further aspect of the invention, a radar system comprises: a processor, a memory coupled to the processor, the memory and the processor configured to: obtain first and second sets of radar return signals concurrently, detect targets in the first and second sets of radar return signals, and identify detected targets due to clutter. The memory and the processor can be further configured for one or more of the following features: the first set of radar return signals comprise low beam data and the second set of radar return data comprises high beam data, the removing step further comprises: estimating and determining the wind turbine signals from a first one of the first and second sets of return data and applying estimated parameters on the other of the first and second sets of return data, the removing step further comprises: performing optimization to improve the wind turbine signals estimations prior to clutter cancellation, the removing step further comprises: utilizing prior knowledge on a number of the wind turbines and geophysical locations of the wind turbines, and/or utilizing the prior knowledge on the number of the wind turbines and the geophysical locations of the wind turbines and a candidate clutter signal height information to form an adaptive active turbine list, the identifying step further comprises: determining the detected targets common in both the first and second sets of radar return signals, comparing amplitudes associated with the common detected targets to an amplitude threshold, and identifying the common detected targets with amplitudes greater than the amplitude threshold as potential detected targets, the identifying step further comprises: estimating a height of the potential detected targets, and maintaining the potential detected targets having a height less than a height threshold as potential detected targets due to at least one of wind turbine clutter and bird clutter, setting the height threshold as a function of a range of the potential detected targets based on height estimation accuracy, the identifying step further comprises: comparing range and azimuth values of the potential detected targets due to wind turbine clutter with a range gate azimuth map having known locations of wind turbines in the vicinity of the radar system, and maintaining the potential detected targets having range and azimuth values corresponding to a wind farm region as potential detected targets due to wind turbine clutter, detecting targets in a given set of radar return signals for a range cell comprises: pre-processing the given set of radar return signals, performing Doppler processing on the pre-processed given set of radar return signals to produce several Doppler outputs, performing CFAR detection on the several Doppler outputs to produce several CFAR detection results, and merging the CFAR detection results to obtain detection results for the range cell, generating a CFAR threshold for performing the CFAR detection for a given Doppler output by: averaging values in an early range window prior to the range cell to obtain a first average, averaging values in a late range window after the range cell to obtain a second average, selecting the smaller of the first and second averages to produce an initial value, determining a clutter level in a clutter map that corresponds to the range cell and the given Doppler output, setting the CFAR threshold to the larger of the initial value and the clutter level, and adding a constant based on a desired false alarm rate to the CFAR threshold, performing CFAR detection for a given Doppler output comprises: generating a CFAR threshold based on a clutter map that corresponds to the given Doppler output, wherein the clutter map includes clutter information due to at least one of wind turbines and birds, and excluding the wind turbine containing cells in the range windows prior to averaging, obtaining the first and second sets of radar return signals concurrently comprises generating a high beam pattern and a low beam pattern for receiving radar return signals, wherein the high and low beam patterns overlap with one another and produce a stable delta gain pattern, generating the high and low beam patterns to produce the stable delta gain pattern with an overlap region that includes the angle of ascent and descent of an aircraft, using a Primary Surveillance Radar (PSR) to obtain the first and second sets of radar return signals concurrently, detect targets in the first and second sets of radar return signals, identify detected targets due to clutter, and generate first detection results, and using a Secondary Surveillance Radar (SSR) to generate second detection results, and combining the first and second detection results to produce a combined report, and/or generating detection information based on the target detections in the first and second sets of radar return signals for a plurality of scans, generating plots based on the detection information for the plurality of scans, and generating tracks of the detected targets taking into account detection information related to the detected targets that are due to clutter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of this invention, as well as the invention itself, may be more fully understood from the following description of the drawings in which:

FIG. 1 is a block diagram of an exemplary embodiment of a portion of an ATC radar system; FIG. 1 A shows further details for the ATC radar system of FIG. 1 ; FIG. IB is a schematic representation of an ATC system illuminating a windfarm within a surveillance area FIG. 1 C is a schematic representation of an exemplary radar system showing illustrative high and low beams; FIG. 2 is a flowchart diagram of a radar data processing method for wind turbine clutter mitigation that can be used in the ATC radar system of FIG. 1 ;

FIG. 3 is a flowchart diagram of wind turbine mitigation in pre- Doppler processing stage that can be used by the civil ATC radar system of FIG. 1 ;

FIG. 4 is a plot showing exemplary patterns for high and low receive beams that can be used by the civil ATC radar system of FIG. 1 to receive radar return signals;

FIG. 5 is a plot of an antenna delta gain pattern over a range of elevation angles that correspond to the high and low receive beams of FIG. 4;

FIG. 6 is an example plot of phase vs. elevation angles that can be used to mitigate higher elevation ambiguity region; FIG. 7 is a block diagram of an exemplary embodiment of PSR data processing that can be used by the civil ATC radar system to mitigate wind turbine clutter after CFAR processing in FIG. 1 ; and

FIG. 8 is a schematic representation of an exemplary computer that can perform at least a portion of the processing described herein.

DETAILED DESCRIPTION

In general, an ATC radar system provides windfarm mitigation in accordance with exemplary embodiments of the invention. The system estimates the azimuth, range and height of a target for a range of elevation angles in a surveillance area and mitigates the effect of clutter due to various objects, such as birds and wind turbines. In one embodiment, the radar system implements independent concurrent low and high beam channel processing, compares detection information obtained by both beams, and retains information regarding the beam in which detection occurs, as well as the beam in which significant clutter is detected. The detection information can be used to discriminate between aircraft and wind turbines, for example. In various embodiments, one or more of the following processing features can be used to combine information from multiple beams at various points including: 1) combining information from the beams at the RF stage; 2) combining information from the beams after pulse compression; 3) combining information from the beams at the input to the binary integrator; 4) combining information from the beams at the input to the plot extractor; and/or 5) combining information from the beams at or after the PSR/SSR data combiner. In the illustrative embodiment of FIG. 1 A, information from the beams is combined at a PSR data processor.

Since wind turbines and aircraft are objects with differences in altitudes, a preliminary distinction between aircraft and wind turbines can be based on a difference in the altitudes of these objects by using two independent concurrent low and high beam receiver channels.

Discrimination between aircraft and wind turbines can be further improved by selective use of detection thresholds per individual radar data sets provided by separate Doppler filters in a

Doppler filter bank. Discrimination between aircraft and wind turbines can also be improved by improving the data association processing when generating tracks. This can also include using classification processing that represent the behavior of aircraft and clutter originated by wind farms or bird migrations.

FIG. 1 shows an exemplary civil ATC radar system 10 having windfarm mitigation in accordance with exemplary embodiments of the invention. The system 10 includes a control module 12 that controls overall operation of the system. In one embodiment, the control module includes a user interface to enable interaction with a user, such as air traffic control personnel. A PSR 14 and a SSR 16 are coupled to a signal processing module 17 to process the return from the PSR and SSR. A height estimation module 18 is coupled to the signal processing module 17 to determine a height of a target, as described more fully below. An output module 19 is coupled to the signal processing module 17 to output processed information in a format to enable display to a user, for example.

FIG. 1A shows further detail for the ATC radar system of FIG. 1. The system includes a data combiner and calibrator 20 module, a plot extractor 22 module, a tracker module 24, and a classifier module 26 coupled in serial to process the SSR and PSR data combined in the data combiner 20. The system can further include an output device 28.

It is understood that in alternative embodiments, the radar system 10 may include additional components and may not include all of the components shown in FIG. 1 A, and/or may have a different configuration than that shown in FIG. 1A. For example, the classifier 26 may be additionally, or optionally, connected to at least one of the data combiner and calibrator 20 and the plot extractor 22 to classify detected targets. This allows target classification to be done at various stages of target tracking including during or after detection, plot extraction or track formation. There can also be an input module (not shown) that can be used by an operator to provide an additional level of control to the radar system 10.

The control unit 12 controls the overall operation of the civil ATC radar system 10. While connections may be shown to the PSR 14, the SSR 16 and the data combiner and calibrator 20, it is understood that the control unit 12 can provide control signals to any component of the radar system 10.

FIG. IB shows an exemplary system with a PSR and SSR covering a surveillance area SA within which a windfarm WF and targets TA are located. FIG. 1 C shows an exemplary system with illustrative high and low beams illuminating a windfarm within a surveillance area.

Referring again to FIG. 1 A, the PSR 14 locates and tracks objects within the surveillance area of the radar system 10 by transmitting a low beam and concurrently processing return signals from objects in the surveillance area in high and low beam reception channels by beamforming each of the receiving beams to high and low directions as described more fully below. The concurrent processing enables the radar system 10 to discriminate between airborne objects and clutter in the surveillance area. More particularly, the data combiner and calibrator 20 can provide information on targets and clutter that are detected by at least one of the beams by using data provided by the PSR 14, the height estimation lookup table 18, and/or the SSR 16, as described more fully below.

The data combiner and calibrator 20 can generate and maintain the height estimation lookup information 18, which can be provided as a table, by using calibration data provided by known objects. In an exemplary embodiment, the PSR 14 and the SSR 16 can provide the calibration data. In other embodiments, devices other than the SSR 16 can provide the calibration data, such as an ADS-B (or UAT or VDL Mode 4) device (not shown), which takes advantage of GPS and data link technology to autonomously transmit real-time aircraft position (including altitude) to both ground based stations for air traffic control, as well as to appropriately equipped aircraft. The SSR 16, the ADS-B device and any other device that can be used to provide known height calibration data are referred to herein as a height calibration data provision device. Calibration is discussed in more detail below. Alternatively, or in addition, a test target or a co-operating airplane may be used.

In an exemplary embodiment, the PSR 14 includes a PSR antenna 30, a PSR duplexer 32, a PSR transmitter 34, and a PSR receiver 36. The PSR receiver 36 can include a low beam signal processing path that includes a low beam receiver 38, and a low beam signal processor 40, and a high beam signal processing path that includes a high beam receiver 42, and a high beam signal processor 44. The PSR 14 further includes a PSR data processor 46 that interacts with a range azimuth gate map 48 stored in a data store (not shown) that can be accessed by the PSR 14. Alternatively, the functionality of the low beam and high beam signal processors 40 and 44 and the PSR data processor 46 can be provided by a digital signal processor.

The PSR antenna 30 can be a scanning antenna, a phased array antenna, or any other suitable antenna. The PSR transmitter 34 can be a solid-state transmitter, a tube transmitter, or any other suitable transmitter. Various waveforms can be used for generating the transmitted radar pulses, such as unmodulated waveforms, modulated complex waveforms, such as nonlinear FM waveforms, as well as other suitable waveforms transmitted in a simplex frequency, dual frequency or other suitable fashion as is well known by those skilled in the art. A variable pulse repetition frequency (PRF) may also be used across different coherent processing intervals (CPIs) to combat the blind speed problem. However, a constant PRF across different CPIs can also be used.

In use, the control unit 12 controls the operation of the PSR 14 and can provide timing control signals to the PSR duplexer 32, the PSR transmitter 34, and the PSR receiver 36 to control the timing of the transmission and reception of signals. The PSR transmitter 34 can be configured to create the radar pulse signals that are to be transmitted and amplify these signals to a higher power level to provide adequate range coverage. The PSR receiver 36 is sensitive to the range of frequencies being transmitted and provides amplification for received radar return signals. The PSR duplexer 32 comprises a switch that connects either the PSR transmitter 34 or the PSR receiver 36 to the PSR antenna 30 depending on whether signals are to be transmitted or received, respectively, and to protect the receiver 36 from the high power output of the PSR transmitter 34. These elements are well known to those skilled in the art.

During the transmission of an outgoing pulse, the PSR duplexer 32 couples the PSR transmitter 34 to the PSR antenna 30 for the duration of the pulse. After the pulse has been transmitted, the PSR duplexer 32 couples the PSR antenna 30 to the PSR receiver 36. The PSR 14 transmits electromagnetic energy in a given sector of the surveillance area according to a beam pattern. In some cases, the PSR antenna 30 can rotate to direct the beam pattern, as well as detect radar return signals from objects, along different sectors in the surveillance area. In other cases, electronic beamforming can be used to scan different areas of the surveillance area, as is commonly known to those skilled in the art.

Low beam return signals are processed by the low beam receiver 38 and the low beam signal processor 40. High beam return signals are processed by the high beam receiver 42 and the high beam signal processor 44. These paths operate similarly and are similarly implemented.

Accordingly, only the low beam signal processing path is described in any detail. The low beam receiver 38 includes circuitry for pre-processing the low beam return signal. The low beam receiver 38 typically includes analog and digital circuitry, such as one or more filters, amplifiers, and mixers, and an analog to digital converter. These elements perform filtering, amplification, and down-conversion (i.e. demodulation to a lower frequency band) as is commonly known by those skilled in the art. Filtering removes extraneous unwanted signals in the return radar signals. In some cases, heterodyning can be used to demodulate the filtered data from the RF band to an IF band where analog to digital conversion can take place.

The low beam signal processor 40 then processes the pre-processed low beam return signal to detect any targets and determine the target's range RL, amplitude AMPL and azimuth AZIL. The high beam receiver 42 and the high beam signal processor 44 process high beam return signals in a similar fashion to detect targets and determine the target's range RH, amplitude AMPH and azimuth AZIH-

In one embodiment, the low beam signal processor 40 is implemented using a digital signal processor. The low beam signal processor 40 can perform demodulation to the baseband, low- pass filtering and downsampling. The low beam signal processor 40 can perform matched filtering by employing one or more matched filters that have a transfer function or impulse response that is matched to the transmitted radar pulses. The data from the matched filter(s) are then separated into CPIs for analysis in which the data is range-aligned and beamformed to provide the range-azimuth data. The range information in the range-azimuth data provides an estimate of a target's distance from the radar system 10. The azimuth information in the range- azimuth data provides an estimate of the angle of the target's location with respect to the center of the antenna 30. The low beam signal processor 40 applies adaptive processing to estimate the signal returns due to wind turbines with the information provided from the range azimuth gate map 48 or equivalent wind turbine location maps. The estimated signal parameters are sent to high beam signal processor 44. The low beam signal processor 40 then applies Doppler filtering to the range-azimuth data to produce range-Doppler-azimuth data. The Doppler information in the range-Doppler-azimuth data provides an estimate of a target's radial velocity by measuring the possible target's Doppler shift, which is related to the change in frequency content of a given radar pulse that is reflected by the target with respect to the original frequency content of the given radar pulse. Detection is also performed. The processing provided by the low and high beam signal processors 40 and 44 is described in more detail below with respect to FIG. 3 and FIG. 7.

The range, azimuth, and amplitude of targets detected by the low and high beam signal processors 40 and 44 are then provided to the PSR data processor 46. The PSR data processor 46 compares the targets detected by the high beam and the low beam to determine targets that are detected by both beams. If the PSR data processor 46 determines that a target is only detected in the low beam, then the PSR data processor 46 associates the range RL, azimuth AZIL and a low beam indicator, such as 0 for example, to the detected target. If the PSR data processor 46 determines that a target is only detected in the high beam, the PSR data processor 46 associates the range R¾ azimuth AZ¾ and a high beam indicator, such as 1 for example, to the detected target. If the PSR data processor 46 determines that a target is detected in both the high and low beams, by finding a target for which RH is similar to RL and AZIH is similar to AZI_L, the PSR data processor 46 associates the range R_H or R_L, azimuth AZIH or AZIL, and the delta gain, calculated as per equation 1 below, to the detected target.

The PSR data processor 46 then estimates the delta gain for targets that are detected in both the low and high beams. The PSR data processor 46 then estimates the height of the targets that are detected in both beams by using the azimuth and delta gain values as indices into the height- estimation lookup table 18 to obtain the corresponding elevation angle. The target's elevation angle can be found by searching the height estimation lookup table 18 for the range and azimuth values that are closest to the range and azimuth values of the target. Alternatively, suitable interpolation processing can be applied to the elevation angles of the two closest pairs of range and azimuth values that correspond to the range and azimuth values of the target. The PSR data processor 46 then uses the looked-up elevation angle to calculate the sine (i.e. trigonometric function) of the elevation angle and multiply by the range of the target to estimate the height of the target. Alternatively, the height estimation lookup table 18 may include height values instead of elevation angles if the tangent operation is used on the range and azimuth values when the height estimation lookup table 1 8 is first created or calibrated, thereby saving the PSR data processor 46 from performing an extra calculation. If the target is detected only in the high beam or the low beam, then the PSR data processor 46 does not calculate the delta gain of the target and notes which beam the target was detected with. In this case the PSR data processor 46 can provide an indicator, such as 0 and 1. For each detected target, the PSR data processor 46 can output the range, and azimuth, and either the estimated height or a beam indicator.

Wind turbines in general have a limited height of approximately 100 meters. They also have very large radar cross sections, which result in strong radar return signals when located within the radar horizon. Accordingly, for cases in which there are detections in both beams, the height can be estimated by the PSR data processor. If the estimated height is limited to several hundred meters then the return can be temporarily labeled as a wind turbine. However, aircraft returns embedded in the sidelobe of strong wind turbines can generally be detected only by clutter cancellation being applied as described above. FIG. 2 shows exemplary processing steps for radar data processing for wind turbine clutter mitigation in accordance with exemplary embodiments of the invention. A first clutter mitigation stage 200 includes pre- Doppler processing, a second stage 202 includes CFAR processing, and a third stage 204 includes processing applied after CFAR processing. In the mitigation processing, the wind turbine map (or range gate azimuth map) is used for the guidance of the estimation on wind turbine clutter.

The pre-Doppler processing stage 200 includes receiving pulse compressed data 250 on which wind turbine clutter processing 252 is performed using a wind turbine clutter map 254. Due to low altitude, the returns from wind turbines are much stronger in the low beam return signal than these in the high beam return signal.

In an exemplary embodiment, processing begins with low beam pulse compressed IQ data in low beam signal processor 40 (FIG. 1 A). Wind turbine returns can be modeled as spatially separated point targets. The mainlobe peak of the strongest wind turbine return in low beam is least affected by the sidelobes of the nearby weaker air targets. Therefore, if its position matches the geophysical wind farm location the maximum value in the pulse compressed data can be approximately treated as an estimate of the mainlobe peak from the strongest wind turbine.

The geophysical wind farm locations can be set and stored before deploying the radar. In one embodiment, the system generates a fine resolution map centered on the radar origin and each map cell reflects the existence of wind turbines or not in range azimuth gate map 48 (FIG. 1 A). Based on the known pulse compression sidelobe shape, the whole signal return due to the strongest wind turbine can be estimated and aligned based on the mainlobe peak position. FIG. 3 shows an exemplary sequence of processing steps for processing wind turbine returns. In general, the estimated strongest wind turbine returns are removed from the overall received signals, then the same procedures can be applied repeatedly to estimate the next strongest signal for the rest of wind turbine locations. The procedure can be repeated until all the wind turbine signals are estimated or the filtered data reach background noise level. Ideally this process should be guided by the available wind turbine locations.

If the estimated signal falls within the wind farm area according to the predefined wind turbine map, the estimated wind turbine signals can be scaled based on the established low/high beam power and phase relationship and used to filter the high beam return signal. The power ratio between high beam and low beam is relatively stable and vary slightly for different carrier frequencies. This power ratio is a function of elevation angle. The power ratio can be established based on available measurements during system integration and may require occasionally calibration during the operation. The calibration can be done based on available SSR information as described at later section.

The phase relationship between high beam and low beam is sensitive and may not be reliable. This can be addressed by real time optimization techniques. The wind turbine map can be a coarse or fine resolution map and can be updated when changes are made to the already known wind farms or if new wind farms are installed in the vicinity of the radar system. This map can also be online generated and maintained in real time based on false plots or tracks activities. By doing this, only these active turbines with significant false breakthrough are addressed. The filtered low beam and high beam data are subjected to Doppler processing.

Assume the received signal is r_L(t) for low beam and ¾(ΐ) for high beam, the pulse compressed waveform (autocorrelation function) is p(t) and receiver noise is n(t), there are N wind turbine ed as follows:

x(t = 0.

In step 300, low beam pulse compressed data is r_L(i) and the estimated wind turbine x(t) is set to zero. In step 302, the system estimates the most significant signal's amplitude ^, phase Θ and time delay r in low beam data r_L (t) with the guidance of the wind turbine locations from the wind turbine clutter map 304, and extracts in step 306 the estimated signal from the overall received data with the pre-defined pulse compression sidelobes from the received data r_L t), wherein in one embodiment τ = arg(m ax( iis(r_i (t)))_J 4 = abs (r_L( ), Θ = phase (r_L(_T)), and r(t) = r_L(t) - A^-pit - τ)

In step 308, it is determined whether the sidelobe reduction is less than a threshold δ. For example, if

then let r_L(t) = r(i), and x(t) = x (t) + Ae^ (t - τ) in step 310. Note that pulse is the length of uncompressed pulse and compressed _pulse is the length of compressed pulse, δ is used to control the desired sidelobe reduction. If the sidelobe reduction was not less than δ, then in step 312 let TL ) = ri t) and x(t) = x(t) to exclude the identified peak from next iteration.

In step 314, it is determined whether all wind turbine signals are estimated or the residue reaches expected noise level. If not, processing returns to step 302 for next strongest signal. If so, in step 316, the system filters out the estimated wind turbine signals x(t). The system filters the estimated signals x(t) with the guidance of wind turbine locations in the wind turbine clutter map 304 to remove potential targets and obtain the low beam wind turbine signal y_L (t) in step

318. In one embodiment, the system identifies time delays r, r₂ ... τ_Ν from the background knowledge on (active) wind turbine locations and the corresponding amplitudes and phases. The low beam wind turbine signal is obtained from y_L (t) = Y^_{= 1} A_i e^⁹ⁱ (t— T_j ) . In step 320, the system removes wind turbine signal y_L (t) from original low beam data. The filtered low beam data is sent for Doppler processing. In step 350, the low beam wind turbine signal yi_(t) from step 318 is obtained. In step 352, the high beam pulse compressed data ΓΗ(Ϊ) is obtained. In step 354 the high beam windturbine signals y (t) are estimated and the system determines if the high/low beam amplitude and phase information are reliable. If so in step 356, the high beam wind turbine signals are set to

yH(t)=yL(t)*R*e^Jct , where R is the amplitude ratio between high and low beam, and a is the phase difference. In step 358, the system removes wind turbine signal y_H(t) from the original high beam data r_N(t) and the filtered high beam data are sent for Doppler processing, along with the low beam data from step 320.

When the phase relationship between high and low beams is not reliable, optimization can be used in step 360 to filter the wind turbine signal in the high beam. In one embodiment, the system solves the following optimization problem to estimate the phase differences (σ,) between high and low beam

In one embodiment, when the concurrent beam data are available clutter can be suppressed as local peaks are identified around the wind farm area. The corresponding heights are estimated for these potential targets. The filtering is only applied for those potential targets with height of no more than a few hundred meters, for example. In other words, the wind turbine locations are pre- filtered before used as guidance for clutter signal cancellation. In the second stage 202 (FIG. 2), wind turbine clutter can be removed in CFAR processing. The Doppler processed data 256 are subjected to CFAR detection 258. During CFAR detection process, the cell under test is compared to a threshold generated based on local noise/clutter estimation. When estimating the background noise or clutter, the prior knowledge can be utilized to improve the CFAR performance. For this application, these cells containing known wind turbines are excluded from the average process to avoid noise estimation bias. This can improve weak target detection performance close to wind turbines. This is complimentary to the previous filtering process to address these cells with known wind turbines. This happens when strong interference prevents the correct wind turbine filtering in the first stage. A more generalized process maintains a dynamic wind turbine clutter map based on the feedback from tracker or classifier. If the total number of wind turbine cells within the reference window for noise estimation exceeds a certain limit, the noise estimate is set to the nominal noise value, which could prevent false detection from noise fluctuation due to the excessively reduced cells available for averaging. The detections from both high beam and low beam can be used to estimate the height of the target and so as to suppress wind turbine clutter breakthrough from detections. These false alarms due to wind turbines can be from residue of prior filtering or multi-path turbine reflected signals. Referring again to FIG. 1 A, the PSR 14 of the radar system 10 transmits pulses along a low beam direction and concurrently uses both a low beam and a high beam to receive radar return signals. FIG. 4 shows a plot of exemplary patterns for a high beam 400 and a low beam 402 that can be used by the PSR 14. The high beam 400 has a high beam axis 404 and the low beam 402 has a low beam axis 406. There is an offset of about 5 degrees between the high beam axis 404 and the low beam axis 406. As can be seen, the high and low beams 400 and 402 have a cosecant beam pattern.

In alternative embodiments, pencil or fan beams can be used. However, other beam patterns can be employed as well as other offsets between the high beam axis 404 and the low beam axis 406, as long as a stable delta gain pattern results, which is discussed in relation to FIG. 5. For instance, the gain of the high and low beams 400 and 402 can be varied during antenna manufacture by shifting the High beam horn alignment (relative to the Low Beam horn) to extend the overlap region by three to nine degrees in some cases.

FIG. 5 shows a plot of an antenna delta gain pattern over a range of elevation angles that corresponds to the high and low beams 400 and 402 of FIG. 4. The delta gain pattern is a plot of delta gain (in dB) versus elevation angle (in degrees). The delta gain is the difference in gain (i.e. amplitude) between a radar return signal from a target detected by the low beam 402 (hereafter referred to as a low beam return signal), and a radar return signal from the same target detected by the high beam 400 (hereafter referred to as a high beam return signal). The delta gain can be calculated as follows:

Delta Gain (dB) = Low Beam Return Signal Amplitude (dB)

- High Beam Return Signal Amplitude (dB) (1) An offset value can be added to ensure that the delta gain values that are calculated are positive. An alternative calculation is set forth below:

Delta Gain = (Low Beam Return Signal Amplitude

- High Beam Return Signal Amplitude) /

(Low Beam Return Signal Amplitude + High Beam Return Signal Amplitude) Experimentation has shown that there is a stable correspondence between the delta gain and elevation angle in the overlap region of the high beam 400 and the low beam 402 as shown in FIG. 5. In this region there is a gain variation of about 18 dB and an elevation angle variation from about 0.6 to 5.8 degrees. The elevation angle corresponds to the height of the target, i.e., the height of the target can be approximately estimated by multiplying the range of the object by the sine of the elevation angle. More accurate estimates can be obtained by considering the earth curvature, the antenna height and the ray tracing factor. Furthermore, the range of values of the elevation angle in the overlap region corresponds to the glide path of civil aircraft; typically the angle of ascent or descent for a civil aircraft is about 2.5 degrees. For civil aircraft, this region close to the ground is where the biggest risk exists for aircraft that are taking off and landing.

In exemplary embodiments, the radar system 10 can provide a preliminary detection of targets in the glide path of an aircraft by using the high and low beams 400 and 402, and the delta gain pattern. When a target is detected, based on the estimated height, a preliminary decision can be made as to whether the target is an airborne target, or is clutter that may be ground-based.

Further processing can then be performed to confirm the type of detected target and information can be fed back to the PSR receiver to adjust data processing for improving detection of airborne targets. Once airborne targets are identified then depending on the particular scenario, an air traffic controller can provide flight pattern incursion information to aircraft that are landing or taking off. This is in contrast to a conventional ATC system that cannot provide height information for all objects in the glide path or cannot identify ground-based clutter due to wind turbines; in this conventional case, the airport is shutdown if objects are detected by the PSR and the height is unknown (this can be a significant problem during periods of bird migration). On either side of the overlap region, there is a lower elevation ambiguity region and a higher elevation ambiguity region. The upper limit of the overlap region is defined by the beam axis 404 of the high beam 400. The lower limit of the overlap region is defined by the beam axis 406 of the low beam 402. The low elevation ambiguity region is caused by the leveling off or differing decay of residual gains in the high and low beam gains. This causes a fold over or leveling off of the gain delta pattern creating the potential for two elevation angles to have the same gain delta. This lower elevation ambiguity region can be eliminated by setting a suitable tilt angle for the PSR antenna so that the elevation angles associated with the low elevation ambiguity region are negative and correspond to a certain depth below the ground. Accordingly, if a delta gain value of 13 dB is calculated, this corresponds to an elevation of 1.8 degrees since based on the delta gain pattern the other instance of 13 dB is in the low elevation ambiguity region which has a negative elevation angle.

The high elevation ambiguity region is also caused by the leveling off or differing decay of residual gains in the high and low beam gains. This causes a fold over or leveling off of the gain delta pattern creating the potential for two elevation angles to have the same gain delta. This higher elevation ambiguity region can be mitigated by using the phase information with an example as shown in FIG. 6, where the phase differences of an aircraft from the high beam and low beam are correlated with respect to the target's elevation. This phase information can be utilized to address high flying targets.

The delta gain is calculated when there are detections in both high beam and low beam for one target. Referring to the plots in FIG, 5 and FIG. 6, the elevation angle can be obtained and the height of the target can be estimated accordingly. Based on the estimated height, a preliminary decision can be made as to whether the target is an airborne target, or is clutter that may be ground-based.

If the estimated height is limited to several hundred meters, the PSR data processor will attach a temporary label of wind turbine on the detected target. Other conditions can be applied to determine whether this target is from wind turbine clutter or not. One condition is the strength of the detections in both the high beam and low beam for this target with a reference of an amplitude threshold. The guidance from range-azimuth map of the wind farm is used as another reference for indication on the wind turbine clutter. Once the detected target is confirmed as a wind farm clutter, the PSR data processor provides clutter detection feedback to high beam and low beam signal processor to indicate a false detection. With the feedback signal, the low and high beam processors exclude the wind turbine clutter from the detection results and examine if there any weaker detections which indicate aircraft targets.

Once airborne targets are identified then depending on the particular scenario, an air traffic controller can provide flight pattern incursion information to aircraft that are landing or taking off. This is in contrast to a conventional PSR system that cannot provide height information or cannot identify ground-based clutter due to wind turbines; in this conventional case, the airport can be shut down if objects are detected by the PSR and the height is unknown (this can be a big problem during bird migration).

Wind turbines in general have a limited height, which can be approximately 100 meters on average. They also have very large radar cross sections, which guarantees strong low and high beam radar return signals that can result in detections in both the low and high beam signal processing paths. Accordingly, for cases in which there are detections in both beams, the height estimated by the PSR data processor can result in temporarily labeling the detected target as a wind turbine if the estimated height is limited to several hundred meters. A height threshold can be specified but this depends on height estimation accuracy and the target range. Different height thresholds can be used for different ranges; for example, a smaller threshold can be used for near range targets and a larger threshold can be used for far-range targets.

Alternatively, the height threshold can be pre-calculated for different wind farm regions based on the range azimuth gate map 48 (FIG. 1 A). Height thresholds can also be similarly specified and used to discriminate targets due to birds from targets due to aircraft. The PSR data processor 46 can increase the confidence of the label by determining if the detection falls within the wind farm area according to the range azimuth gate map 48. The range azimuth gate map 48 is a map showing the location of known wind farms as well as other sources of clutter such as highways, the ocean, and the like. The map 48 can be a coarse map and can be updated when changes are made to the already known wind farms or if new wind farms are installed in the vicinity of the radar system 10. Three conditions can be used to detect clutter due to a wind farm. The first two conditions can be assessed individually to determine if the detected target is due to wind clutter from a wind turbine. Alternatively, the third condition can be assessed along with either of the first and second conditions to increase the confidence of labeling a detected target as being due wind turbine clutter. Alternatively, all three of these conditions can be combined to obtain a greater level of confidence that the detected target is due to clutter from a wind turbine.

A first condition is that there is a strong detection in both the low and high beam return signals for a target. The amplitude of the detected target can be compared to an amplitude threshold to determine if it is a strong detection indicative of wind farm clutter. A second condition is that the gain ratio of the low and high beam return signals for the possible target indicates an estimated elevation of less than about a few hundred meters (this depends on height estimation accuracy and the range of the wind farm with respect to the radar site as mentioned previously). A third condition is that the azimuth and range of the possible target correspond with the location of a wind farm as indicated by the range-azimuth gate map 48. Using dual beam operation and various combinations of these conditions, false detection due to wind turbines can be

significantly suppressed. In addition, if the wind turbine clutter is detected in only one of the beams, the output of the same Doppler filter in the other beam can be checked to estimate a target height.

When it is determined that a detected target is actually clutter due to a wind farm, the PSR data processor 46 can provide clutter detection feedback signals to the low and high beam signal processors 40 and 44 to indicate a false detection. The low and high beam signal processors 40 and 44 can then remove the detected wind farm clutter from the detection results and determine whether there are any other targets in the current radar data that is being processed, as explained more fully with respect to FIG. 7.

If another target is located in either of the low and high beam radar return data, then this information is provided to the PSR data processor 46 for processing once more. In another alternative, rather than removing the detected wind farm clutter from the detection results, the detected wind farm clutter and the corresponding low/high beam information can be provided to the plot extractor 22 and the tracker 24 to improve tracking performance (in this case, the detection results can still be reviewed in the PSR receiver 36 to determine if there are any aircraft or bird targets). A further consideration is when an aircraft flies near a wind farm area, typically the aircraft can only be detected in the high beam return radar signal. Under such circumstances, the probability of target detection for an aircraft target can be increased using dual beam operation by noting detections in the high beam return radar signal that are also in close proximity to a wind farm area by comparing the range and azimuth values for such detections with respect to the wind farm locations indicated by the range azimuth gate map 48. The use of SO-CFAR and individual clutter maps (described in more detail below) may also help in aircraft detection in this situation.

FIG. 7 shows a block diagram of an exemplary embodiment of certain components of the low beam signal processor 40 (a similar structure can be used for the high beam signal processor 44). The signal processor 40 includes a Doppler filter bank 62, a Constant False Alarm Rate (CFAR) detection module 64, a CFAR merge module 66 and an optional binary integrator 68. The Doppler filter bank 62 can include several Doppler filters and in this example includes five Doppler filters 70-78. The CFAR detection module 64 includes a corresponding number of CFAR detectors 80-88 which each use a clutter map 90-98.

In use, the Doppler filters 70-78 filter the preprocessed radar data provided by the low beam receiver 38 across Coherent Processing Intervals (CPIs) to provide several Doppler outputs that each include range-Doppler-azimufh data that can be used to separate moving targets from clutter. The Doppler information in the range-Doppler-azimuth data provides an estimate of a possible target's radial velocity by measuring the possible target's Doppler shift, which is related to the change in frequency content of a given radar pulse that is reflected by the possible target with respect to the original frequency content of the given radar pulse. The several Doppler outputs are processed on a range cell basis, i.e. the range-Doppler-azimuth cell for the current range cell that is being processed by the low beam signal processor 40. A range cell is a cell on a range-azimuth plot between certain azimuth and range values, for example i.e. between 0 and 5 degrees and 10 and 1 1 nautical miles. In some embodiments, a detection threshold can be calculated as a function of the following three factors: 1) a CFAR threshold which is calculated based on a set of early range cells (i.e. early range cell window) before the current range cell being processed and a set of late range cells (i.e. late range cell window) after the current range cell being processed, by replacing excessively high amplitudes in each window with the average noise level in that window, averaging the amplitudes in both of the windows, and selecting the higher average; 2) a minimum threshold based on the target range; and 3) a clutter map output for zero-Doppler filter, scaled to the corresponding Doppler filter by applying weighting coefficients. This technique is referred to as a Greatest-OF (GO) CFAR method and the largest of the three thresholds is selected and an offset is added for false alarm control.

Alternatively, the detection method can be implemented as follows. The outputs of the Doppler filters 70-78 are processed by a corresponding one of the CFAR detectors 80-88 by processing either the power or the magnitude of the outputs of the Doppler filters 70-78. The CFAR detectors 80-88 select a threshold value based on a combination of radar amplitude data associated with the current range-azimuth cell that is being processed as well as the clutter level in a corresponding range cell provided by one of the corresponding clutter maps 90-98. Based on the dual beam and multiple clutter map operation, a CFAR technique referred to as the Smallest Of (SO)-CF AR method, along with peak editing, can be used to increase the sub-clutter target detection probability and super-clutter target detection probability. This CFAR method may not work as well in single beam and single clutter map operation due to the complex effects of wind turbines. Different types of CFAR methods can also be used. In some embodiments, rather than using peak editing, the peaks can be replaced with an average value. For instance, assuming that there is a wind farm with wind turbines falling into several cells and the clutter-to-noise ratio (CNR) due to the wind turbines in each cell is about 60 dB. This is a typical number for the strong clutter return signals, which spreads over the entire dynamic range. In this case, the wind turbine return signals can raise the CFAR threshold to such a level that the aircraft in a range cell is lost and no detection is declared. This effect is called "detection shadowing". As confirmed in field tests, it can result in the loss of aircraft detection when the wind farms are located within +/- 1 nautical mile from the aircraft's position. This can be fixed by substituting the amplitude level of the return signals in the range cells where anomalously high power is recorded with the average noise amplitude. If, in the example presented, the amplitude of the return signals in the several cells with wind turbine clutter are substituted with the average noise amplitude in all of the cells in the early and late range windows, the detection threshold can be brought back to a normal level in which detection of aircraft is more probable (note rather than use amplitude, power can be used).

In another embodiment, the Cell Averaging (CA) CFAR technique can be used with peak editing in which the CFAR threshold is based on the early range and late range averages with the peak value edited (i.e. removed) from each of these averages. This avoids contamination from nearby targets in the estimation of a mean-level CFAR threshold.

In either case, the CFAR threshold is set to an initial value that is the smallest of these averages in the early and late range windows in order to detect aircraft targets (i.e. SO-CFAR). The threshold level is then compared to the clutter level in the corresponding range cell of the corresponding clutter map. The threshold value is set to the clutter level if the clutter level is larger than the initial value (this helps mitigate the effect of wind turbine clutter); otherwise, the threshold is set to the initial value. In either case an offset is added to the threshold level to control the level of false alarms.

Accordingly, the clutter maps 90-98 can help reduce false detections due to wind turbines. The clutter maps 90-98 can be created by smoothing the outputs of the corresponding Doppler filters 70-78 to estimate the average clutter for each range-azimuth cell. The clutter maps 90-98 can be updated from scan to scan. However, the clutter maps that include clutter due to wind turbines will have values that fluctuate more quickly than those of the clutter map used for the zero Doppler filter because this clutter is more stable and predictable. For the rest of the clutter maps associated with non-zero Doppler filters, the clutter may have higher dynamics and several scans will be required for any clutter activity to be thresholded out. Since targets of interest can also match the characteristics of the unwanted wind turbine radar return signals, the clutter maps corresponding to the CFAR detectors applied to the outputs of the non-zero Doppler filters can be applied selectively in range and azimuth such that real aircraft returns are not adversely affected. Specifically, aircraft on approach to an airport have to maintain a uniform speed and these airport approach patterns can rapidly integrate into any Doppler-based clutter map. This can be taken care of by how the CFAR threshold is calculated. In addition, the clutter map associated with the Doppler filter that typically detects birds can be updated differently to capture the change in clutter due to bird flight.

Accordingly, the clutter maps associated with the non-zero Doppler filters should be updated more quickly than the clutter map associated with the zero Doppler filter. If the clutter map associated with the zero Doppler filter is integrated or averaged over 16 scans, for example, then the clutter maps associated with the non-zero Doppler filters can be integrated or averaged over 4 or 8 scans. In general, the clutter maps 90-98 employ an individual cell size of one range resolution cell by one beamwidth or less. Its overall coverage typically extends to full range and 360 degrees.

The clutter maps 90-98 will help suppress clutter with the following general characteristics: 1) clutter that is largely fixed in range and azimuth, 2) clutter that contains a fairly stable Doppler spectrum return, and 3) clutter that persists for a minimum period of time. Since all of these characteristics describe the radar returns from the wind turbines in the situations of variable wind intensity, the clutter maps 90-98 will be helpful to mitigate the effects of clutter due to wind farms.

The CFAR detectors 80-88 each provide a CFAR output. If a data set being processed by one of the CFAR detectors 80-88 exceeds the CFAR threshold for a given range cell, then the corresponding CFAR detector produces a CFAR alarm in its output. Each CFAR alarm can include information about the beam in which detection occurred in addition to current information about amplitude, range, and azimuth for the possible target as well as the number of the Doppler filter in which the detection occurred. The CFAR merge module 66 then selects the biggest target (i.e. the largest CFAR output) from those detected and indicated as such in the CFAR output data for the current range cell that is being processed. In the third stage 204 (FIG. 2), wind turbine clutter mitigation can occur after CFAR processing. A merge module 260 receives feedback from PSR data processing 262 and provides the merge information to a binary integrator 264, for example. As shown in FIG. 7, the CFAR detectors each provide a CFAR output after one of the Doppler filters in the filter bank. If a data set being processed by one of the CFAR detectors exceeds the CFAR threshold for a given range cell, then the corresponding CFAR detector produces a CFAR alarm in its output. Each CFAR alarm includes information about the beam in which detection occurred in addition to current information concerning amplitude, range, azimuth for the possible target as well as the number of the Doppler filter in which the detection occurred. The CFAR merge module then selects the biggest target (i.e. the largest CFAR output) from those detected and indicated as such in the CFAR output data for the current range cell that is being processed.

The merged CFAR alarms are further integrated by the binary integrator 68 to provide preliminary detection data that is sent to the PSR data processor 46. The binary integrator 68 integrates the largest CFAR outputs for m CPIs for a given range cell in a sliding window fashion. The binary integrator 68 can be, but is not limited to, a "2 out of 3" binary integrator. For example, a "3 out of 4" binary integrator can be used. For a "2 out of 3" binary integrator, the largest CFAR outputs must be associated with a detection for 2 out of 3 consecutive CPIs for the binary integrator 68 to declare a detected target. Accordingly, the binary integrator 68 correlates the detections from several consecutive CPIs to control false alarms due to clutter or second time around targets.

The data combiner and calibrator 20 receive SSR detection information from the SSR 16 that includes the identity, range, azimuth, and height of aircraft with transponders that respond to the coded transmissions of the SSR 16. The data combiner and calibrator 20 also receives PSR detection information from the PSR 14 that includes the range, azimuth, and estimated height or beam indicator for possible targets detected with at least one of the high and low beams 400 and 402 from the PSR 14. The detection information from the PSR 14 can also include certain types of clutter such as that due to wind turbines or birds. Birds are similar to wind turbines and can be handled somewhat similarly. Birds are usually flying at a low altitude and slow speed. Both height estimation and clutter maps corresponding to low speed Doppler filter, can be incorporated into the processing methodology explained previously, to mitigate the effect of bird echoes. For instance, another height threshold may be used to discriminate targets due to birds from aircraft targets as is similarly done for wind turbine clutter. In addition, the clutter map in the CFAR detection module 64 that corresponds to the Doppler speed expected for birds can be updated at a rate commensurate to capture the change in clutter due to bird flying across the surveillance region. The data combiner and calibrator 20 then combines the information from the PSR 14 and the SSR 16 and can provide a combined report. The combined report is shorter than individual PSR and SSR reports when taken together. This optimizes communication with downstream radar modules since less data needs to be transmitted to these modules. For a given aircraft that responds to the polling by the SSR 16, the data combiner and calibrator 20 includes the range, azimuth, height and identity of the given aircraft in the combined report. For a given target that does not respond to the polling by the SSR 16, the data combiner and calibrator 20 includes the range, azimuth, and estimated height of the target provided by the PSR 14 in the combined report. If the estimated height is not available, then the data combiner and calibrator 20 provides a beam indication to indicate in which beam the target was detected. Also, the data combiner and calibrator 20 can indicate in the combined report if wind turbine or bird clutter has been detected by the PSR 14. Accordingly, the data combiner can further provide early warning of potential bird strike situations when the range, azimuth, height and trajectory of PSR only objects conflict with the known airport approach and departure paths. Correspondingly the unambiguous identification of the target's height from the PSR data can be used to eliminate it from being a threat to aircraft on approach and departure from airports.

Alternatively, the data combiner and calibrator 20 can still provide an SSR only report with range, azimuth, height and identity for aircraft with operational transponders. In another alternative, the data combiner and calibrator 20 can provide a PSR only report with the range, azimuth, and either the estimated height or the beam indicator for detected targets. The data combiner and calibrator 20 can also provide the combined, PSR-only or SSR-only data to a downstream radar elements for further processing as shown in FIG. 1 A. In each of these cases including the combined report, information on the predominant Doppler filter and beam detection can also be included (i.e. the Doppler filter output and the beam from which detection was based). When the PSR data processor 46 determines that the possible detected target is actually clutter due to a wind turbine, it sends a clutter detection feedback signal to the CFAR merge module 66 to indicate a false detection. The CFAR merge module 66 then determines whether there is a second strongest detection. If there is a second strongest detection result, then the CFAR merge module 66 discards the strongest detection result and selects the second strongest detection result as a potential target detection. This operation increases the probability of aircraft target detection in the vicinity of wind farms. However, if there is no second strongest detection, then the first strongest detection is retained and it is labeled as clutter due to a wind turbine. Alternatively, detections due to wind turbine clutters and labeled as such can be retained and used in downstream radar processing modules or in radar reports.

As described above, an inventive radar system 10 can generate detection information based on the target detections in the first and second sets of radar return signals for a plurality of scans. The detection information can include the range, azimuth, amplitude. Doppler value, and estimated height or beam indicator for the targets detected by the PSR 14. Plots can then be generated based on this detection information for the plurality of scans. Alternatively, the plots can also be generated based on the detection information provided by the SSR 16, which includes the range, azimuth, amplitude, Doppler value, and height of aircraft that communicate with the SSR 16. The detection information from the PSR 14 and the SSR 16 can be merged by the data combiner and calibrator 20 and plots can be generated based on the merged information. Tracks of the detected targets can then be generated taking into account detection information related to the detected targets that are due to clutter. The tracks can then be classified by taking into account detection information related to the detected targets that are due to clutter. Further, it should be noted that in other embodiments different methods known to those skilled in the art may be used for the plot extractor 22, the tracker 24, and the classifier 26 that are suitable given the type of clutter described herein.

The output device 28 can provide information on the targets that are being detected, tracked and/or classified by the radar system 10. The output device 28 can be a monitor, a printer or other suitable output means. The output device 28 can receive classified tracks from the classifier 26 and provide output information on the classified tracks. In other embodiments, the output device 28 can receive information from other components of the radar system 10 and output this information. The height estimation module 18 can include a table that provides calibrated elevation angles at a given resolution that correspond to an object's delta gain and azimuth for a given combination of high and low beam patterns. The resolution can be 0.1 dB but can vary from 0.01 dB to 1 dB. Accordingly, there can be many height-estimation tables depending on parametric values that are used for the high and low beam patterns. These parametric values include high beam pattern (i.e. cosecant, etc.), low beam pattern (i.e. cosecant, etc.), high beam gain, low beam gain, and the offset angle between the high and low beam patterns. As mentioned before, the height estimation table 18 may instead include height values if the tangent operation is used on the range and azimuth values when the height estimation lookup table 18 is first created or calibrated. The values in the height estimation lookup table 18 can be initially determined using values provided by the PSR 14 and the SSR 16. The PSR 14 provides the delta gain for detected objects that correspond to aircraft in the surveillance area with transponders that respond to the polling of the SSR 16. The SSR 16 provides the range, azimuth, height and identity of these same aircraft. From the SSR range, and height, the elevation angle of the aircraft can be determined using the arctan trigonometric function, and this can be associated with the corresponding PSR delta gain value and used to build the height estimation lookup table 18. Accordingly, the civil ATC radar system merges the data provided by the PSR and SSR components to generate the lookup table 18. Merging this data in this fashion in a dynamic lookup table provides the ability to calculate the height of applicable PSR only traffic to a high degree of repeatability and accuracy. Furthermore, during operation, the data in the height estimation lookup table 18 can be continuously calibrated with the most current SSR data.

The SSR 16 transmits interrogation signals to, or polls, aircraft with transponders in the surveillance area. Upon receiving the interrogation signal, the transponder sends a coded reply signal back to the SSR 16. The coded reply signal typically includes information on the identity, range, azimuth, and height of the aircraft with respect to the SSR 16. The SSR 16 processes the coded reply signal to provide this information to the data combiner and calibrator 20.

The SSR 16 typically includes an SSR antenna 50, an SSR duplexer 52, an SSR transmitter 54, an SSR receiver 56, and an SSR signal processor 58. The control unit 12 controls the operation of the SSR 16 and can provide timing control signals to the SSR duplexer 52, the SSR transmitter 54, and the SSR receiver 56 to control the timing of the transmission and reception of signals. The SSR transmitter 54 can be configured to create the coded radar pulse signals that are to be transmitted and amplify these signals to a higher power level to provide adequate range coverage. The SSR antenna 50, SSR duplexer 52 and the SSR receiver 56 function similarly to the PSR antenna 30, the PSR receiver 36 and the PSR duplexer 32 with the exception that the SSR receiver 56 includes one processing path (i.e. the SSR receiver 56 and the SSR signal processor 58) and performs processing specific to the coded return signals provided by the transponders. These elements are well known to those skilled in the art.

The height estimation lookup table 18 can be periodically or continuously updated by the data combiner and calibrator 20 based on the PSR and SSR data for the beam patterns used for the high and low beams 400 and 402. If this dynamic calibration is not done, then the height estimation information would be based only on static calibration information which by nature can be vulnerable to several errors due to installation, component replacement/aging, the environment, and the like. Changes in the installation include the residual leveling of the PSR antenna 30 and the like, which can cause height estimation errors for certain areas of the surveillance area. Component replacement/aging involves component changes over time that creates errors for any calibration method that uses only initial calibration data. Environmental changes can cause radar beam bending that can result in height estimation errors. Accordingly, the radar system 10 periodically or continuously calibrates the data in the height estimation lookup table 18 to avoid these errors.

The height estimation lookup table 18 can be generated by using all combined reports that had a validated SSR altitude and a corresponding PSR unambiguous height estimation value. Multiple tables may be built in range or azimuth to cover any local geographic or system anomalies (e.g. four tables to cover quadrants 0-90, 90-180, 180-270 and 270-360 degrees). A fixed sample size (can be on the order of thousands such as 20,000) can be used. Calculated elevation angles values falling within the same resolution value (e.g. 0.1 dB) are averaged or the median obtained. The actual count of values used in each cells averaging is maintained to provide a "quality" assessment of the average provided. When generating the height estimation lookup table 18, each table entry value is compared to the table values on either side to ensure logical and reasonable progression of values in the table 18. Any "blank" table values can be assigned a value in the middle of the values on either side. The odds of "blank" values is statistically minimal as, in the example given, 20,000 samples are being applied to around 200 possible table entries.

The table generation process can be repeated on an on-going basis to provide a check of the accuracy of the values in the height estimation lookup table 18. A fixed sample size (e.g. 20,000) can be used to periodically (dependent on traffic activity) produce a new table for comparison with the current values in the height estimation lookup table 18. By comparing each value in the height estimation lookup table 18 to the newly generated table it will be possible to accumulate the deltas between the two and apply a warning to the radar operator when these accumulated errors exceed a threshold. At that point the operator will be allowed to use the new table if so required. It should be noted that bird clutter can be handled somewhat similarly to wind turbine clutter as explained above. Accordingly, detections due to bird clutter can be handled in the same fashion as detections due to wind turbine clutter. For instance, detections due to bird clutter can be discarded or can be retained for use by radar processing elements downstream from the PSR 14. Also, when detections due to bird clutter are found, the clutter detection feedback signal can indicate this to at least one of the low and high beam signal processors 40 and 44 so that the next strongest target detection can be looked at to determine if they are an aircraft target.

It should also be noted that values for the various thresholds and parameters used in the various embodiments described herein can be affected by the location of the radar system 10.

Accordingly, one method for determining values for these thresholds and parameters can be based on operating the radar system 10 based on real data, selecting various values for these parameters and thresholds and determining which values provide the best performance. In fact, it is well known to those skilled in the art that it is a well-known practice to routinely perform site optimization to select values for the thresholds and operating parameters. It should also be noted that although the radar system 10 is described in terms of an ATC radar having a PSR and an SSR, the methodology and components described herein are also applicable to other radar systems that have a dual beam operation for which calibration data can be obtained. FIG. 8 shows an exemplary computer that can perform at a portion of the processing described herein. The computer 500 includes a processor 502, a volatile memory 504, a non-volatile memory 506 (e.g., hard disk), AND a graphical user interface (GUI) 508 (e.g., a mouse, a keyboard, a display, for example). The non-volatile memory 506 stores computer instructions 512, an operating system 516 and data 518 including the Q files, for example. In one example, the computer instructions 512 are executed by the processor 502 out of volatile memory 504. In one embodiment, an article 520 comprises non-transitory computer-readable instructions.

Processing may be implemented in hardware, software, or a combination of the two. Processing may be implemented in computer programs executed on programmable computers/machines that each includes a processor, a storage medium or other article of manufacture that is readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and one or more output devices. Program code may be applied to data entered using an input device to perform processing and to generate output information. The system can perform processing, at least in part, via a computer program product, (e.g., in a machine-readable storage device), for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers)). Each such program may be implemented in a high level procedural or object-oriented

programming language to communicate with a computer system. However, the programs may be implemented in assembly or machine language. The language may be a compiled or an interpreted language and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. A computer program may be stored on a storage medium or device (e.g., CD-ROM, hard disk, or magnetic diskette) that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer. Processing may also be implemented as a machine-readable storage medium, configured with a computer program, where upon execution, instructions in the computer program cause the computer to operate.

Processing may be performed by one or more programmable processors executing one or more computer programs to perform the functions of the system. All or part of the system may be implemented as, special purpose logic circuitry (e.g., an FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit)).

Having described exemplary embodiments of the invention, it will now become apparent to one of ordinary skill in the art that other embodiments incorporating their concepts may also be used. The embodiments contained herein should not be limited to disclosed embodiments but rather should be limited only by the spirit and scope of the appended claims. All publications and references cited herein are expressly incorporated herein by reference in their entirety.

Claims

What is claimed is:

1. A method of processing radar data, the method comprising:

obtaining first and second sets of radar return signals concurrently;

removing, using a computer processor, wind turbine signals adaptively from the first and second sets of radar return signals;

detecting targets in the first and second sets of radar return signals; and

identifying detected targets due to clutter.

2. The method of claim 1, wherein the first set of radar return signals comprise low beam data and the second set of radar return data comprises high beam data.

3. The method of claim 1 , wherein the removing step further comprises:

estimating and determining the wind turbine signals from a first one of the first and second sets of return data and applying estimated parameters on the other of the first and second sets of return data.

4. The method of claim 3, wherein the removing step further comprises:

performing optimization to improve the wind turbine signals estimations prior to clutter cancellation.

5. The method of claim 2, wherein the removing step further comprises:

utilizing prior knowledge on a number of the wind turbines and geophysical locations of the wind turbines; and/or

utilizing the prior knowledge on the number of the wind turbines and the geophysical locations of the wind turbines and a candidate clutter signal height information to form an adaptive active turbine list.

6. The method of claim 1, wherein the identifying step further comprises:

determining the detected targets common in both the first and second sets of radar return signals;

comparing amplitudes associated with the common detected targets to an amplitude threshold; and

identifying the common detected targets with amplitudes greater than the amplitude threshold as potential detected targets.

7. The method of claim 6, wherein the identifying step further comprises:

estimating a height of the potential detected targets with amplitude only or with both amplitude and phase information; and

maintaining the potential detected targets having a height less than a height threshold as potential detected targets due to at least one of wind turbine clutter and bird clutter.

8. The method of claim 7, further comprising setting the height threshold as a function of a range of the potential detected targets based on height estimation accuracy.

9. The method of claim 7, wherein the identifying step further comprises:

comparing range and azimuth values of the potential detected targets due to wind turbine clutter with a range gate azimuth map having known locations of wind turbines in the vicinity of the radar system; and

maintaining the potential detected targets having range and azimuth values corresponding to a wind farm region as potential detected targets due to wind turbine clutter.

10. The method of claim 1 , wherein detecting targets in a given set of radar return signals for a range cell comprises:

pre-processing the given set of radar return signals;

performing Doppler processing on the pre-processed given set of radar return signals to produce several Doppler outputs;

performing CFAR detection on the several Doppler outputs to produce several CFAR detection results; and merging the CFAR detection results to obtain detection results for the range cell.

1 1. The method of claim 10, further comprising generating a CFAR threshold for performing the CFAR detection for a given Doppler output by:

averaging values in an early range window prior to the range cell to obtain a first average averaging values in a late range window after the range cell to obtain a second average; selecting the smaller of the first and second averages to produce an initial value;

determining a clutter level in a clutter map that corresponds to the range cell and the given Doppler output;

setting the CFAR threshold to the larger of the initial value and the clutter level; and adding a constant based on a desired false alarm rate to the CFAR threshold.

12. The method of claim 1 1 , wherein performing CFAR detection for a given Doppler output comprises:

generating a CFAR threshold based on a clutter map that corresponds to the given Doppler output, wherein the clutter map includes clutter information due to at least one of wind turbines and birds; and

excluding the wind turbine containing cells in the range windows prior to averaging.

13. The method of claim 1 , wherein obtaining the first and second sets of radar return signals concurrently comprises generating a high beam pattern and a low beam pattern for receiving radar return signals, wherein the high and low beam patterns overlap with one another and produce a stable delta gain pattern.

14. The method of claim 13, further comprising generating the high and low beam patterns to produce the stable delta gain pattern with an overlap region that includes the angle of ascent and descent of an aircraft.

15. The method of claim 1, further comprising using a Primary Surveillance Radar (PSR) to obtain the first and second sets of radar return signals concurrently, detect targets in the first and second sets of radar return signals, identify detected targets due to clutter, and generate first detection results, and using a Secondary Surveillance Radar (SSR) to generate second detection results, and combining the first and second detection results to produce a combined report.

16. The method of claim 1 , further comprising:

generating detection information based on the target detections in the first and second sets of radar return signals for a plurality of scans;

generating plots based on the detection information for the plurality of scans; and generating tracks of the detected targets taking into account detection information related to the detected targets that are due to clutter.

17. An article, comprising:

a computer readable medium containing stored non-transitory instructions that enable a machine to:

obtain first and second sets of radar return signals concurrently;

remove wind turbine signals adaptively from the first and second sets of radar return signals;

detect targets in the first and second sets of radar return signals; and

identify detected targets due to clutter.

18. A radar system comprising:

a processor:

a memory coupled to the processor, the memory and the processor configured to:

obtain first and second sets of radar return signals concurrently; detect targets in the first and second sets of radar return signals; and identify detected targets due to clutter.

19. The system of claim 18, wherein the system is configured to determine the detected targets common in both the first and second sets of radar return signals, compare amplitudes associated with the common detected targets to an amplitude threshold and identify the common detected targets with amplitudes greater than the amplitude threshold as potential detected targets due to wind turbine clutter and provide a clutter detection feedback signal to the first circuitry.