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Publication numberUS20060021498 A1
Publication typeApplication
Application numberUS 11/052,921
Publication dateFeb 2, 2006
Filing dateFeb 9, 2005
Priority dateDec 17, 2003
Publication number052921, 11052921, US 2006/0021498 A1, US 2006/021498 A1, US 20060021498 A1, US 20060021498A1, US 2006021498 A1, US 2006021498A1, US-A1-20060021498, US-A1-2006021498, US2006/0021498A1, US2006/021498A1, US20060021498 A1, US20060021498A1, US2006021498 A1, US2006021498A1
InventorsStanley Moroz, Myron Pauli, William Seisler, Duane Burchick, Mehmet Ertern, Eric Heidhausen
Original AssigneeStanley Moroz, Myron Pauli, William Seisler, Burchick Duane Sr, Ertern Mehmet C, Eric Heidhausen
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Optical muzzle blast detection and counterfire targeting system and method
US 20060021498 A1
An authomated system for remote detection of muzzle blasts produced by rifles, artillery and other weapons, and similar explosive events. The system includes an infrared camera, image processing circuits, targeting computation circuits, displays, user interface devices, weapon aim point measurement devices, confirmation sensors, target designation devices and counterfire weapons. The camera is coupled to the image processing circuits. The image processing circuits are coupled to the targeting location computation circuits. The aim point measurement devices are coupled to the target computation processor. The system includes visual target confirmation sensors which are coupled to the targeting computation circuits.
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1. (canceled)
2. An apparatus comprising:
a spectral filter;
a temporal filter;
a spatial filter;
an image processor cooperating with at least one of said spectral filter, said temporal filter, and said spatial filter to detect a flash event;
a targeting processor cooperating with said image processor to determine a target location based on the flash event; and
a gimbal cooperating with said targeting processor to slew toward the target location.
3. The apparatus according to claim 2, further comprising at least one of:
a target confirmation sensor cooperating with said targeting processor and with said gimbal; and
a counterfire device connected to said target confirmation sensor.
4. The apparatus according to claim 3, wherein said target confirmation sensor comprises one of a pair of binoculars and a telescope.
5. The apparatus according to claim 3, further comprising:
an aim point measurement device aligned with said target confirmation sensor.
6. The apparatus according to claim 2, wherein said image processor, for an image comprising a plurality of pixels, adjusts at least one of a pedestal value and a gain value of at least a portion of the plurality of pixels, thereby spreading a histogram of the image.
7. The apparatus according to claim 7, wherein said image processor comprises an exposure control.
8. The apparatus according to claim 2, wherein said spectral filter comprises a cold filter setting corresponding to at least one of a gunfire characteristic, an ordnance characteristic, a background clutter characteristic, and an atmospheric characteristic.
9. The apparatus according to claim 2, further comprising at least one of:
a range finder connected to said gimbal;
a magnetometer interfacing with said targeting processor;
a compass interfacing with said targeting processor; and
a global positioning satellite transceiver interfacing with said targeting processor,
wherein said targeting processor geolocates the flash event based at least in part from data from at least one of said range finder, said magnetometer, said compass, and said global positioning satellite transceiver.
10. The apparatus according to claim 2, further comprising:
a detection camera comprising a wide angle anamorphic lens cooperating with at least one of said spectral filter, said temporal filter, and said spatial filter.
11. The apparatus according to claim 2, further comprising:
an optical illuminator connected to said gimbal to identify the target.

The present invention relates to (1) an optical muzzle blast detection and counterfire targeting system for remotely detecting the location of muzzle blasts produced by rifles, artillery and other weapons and similar explosive events, especially sniper fire; and (2) a system for directing counterfire weapons on to this location.

Prior Art

Hillis U.S. Pat. No. 5,686,889 relates to an infrared sniper detection enhancement system. According to this Hillis patent, firing of small arms results in a muzzle flash that produces a distinctive signature which is used in automated or machine-aided detection with an IR (infrared) imager. The muzzle flash is intense and abrupt in the 3 to 5 mum band. A sniper detection system operating in the 3 to 5 mum region must deal with the potential problem of false alarms from solar clutter. Hillis reduces the false alarm rate of an IR based muzzle flash or bullet tracking system (during day time) by adding a visible light (standard video) camera. The IR and visible light video are processed using temporal and/or spatial filtering to detect intense, brief signals like those from a muzzle flash. The standard video camera helps detect (and then discount) potential sources of false alarm caused by solar clutter. If a flash is detected in both the IR and the visible spectrum at the same time, then the flash is mostly probably the result of solar clutter from a moving object. According to Hillis, if a flash is detected only in the IR, then it is most probably a true weapon firing event.

In Hirshberg U.S. Pat. No. 3,936,822 a round detecting method and apparatus are disclosed for automatically detecting the firing of weapons, such as small arms, or the like. According to this Hirshberg patent, radiant and acoustic energy produced upon occurrence of the firing of a weapon and emanating from the muzzle thereof are detected at known, substantially fixed, distances therefrom. Directionally sensitive radiant and acoustic energy transducer means directed toward the muzzle to receive the radiation and acoustic pressure waves therefrom may be located adjacent each other for convenience. In any case, the distances from the transducers to the muzzle, and the different propagation velocities of the radiant and acoustic waves are known. The detected radiant (e.g. infrared) and acoustic signals are used to generate pulses, with the infrared initiated pulse being delayed and/or extended so as to at least partially coincide with the acoustic initiated pulse; the extension or delay time being made substantially equal to the difference in transit times of the radiant and acoustic signals in traveling between the weapon muzzle and the transducers. The simultaneous occurrence of the generated pulses is detected to provide an indication of the firing of the weapon. With this arrangement extraneously occurring radiant and acoustic signals detected by the transducers will not function to produce an output from the apparatus unless the sequence is corrected and the timing thereof fortuitously matches the above-mentioned differences in signal transit times. If desired, the round detection information may be combined with target miss-distance information for further processing and/or recording.


According to the present invention, an infrared camera stares at its field of view and generates a video signal proportional to the intensity of light. The camera is sensitive in the infrared spectral band where the intensity signature of the flash to be detected minus atmospheric attenuation is maximized. The video signal is transmitted to an image processor where temporal and spatial filtering via digital signal processing to detect the signature of a flash and determine the flash location within the camera's field of view. The image processing circuits are analog and digital electronic elements. In another aspect and feature of the invention, the image processing circuits are coupled to target location computation circuits and flash location information is transmitted to the targeting location computation circuits. The targeting computation circuit is digital electronic circuitry with connections to the other devices in the system. The field of view of the camera is correlated to the line of sight of the confirmation sensor by using aim point measurement devices which are coupled to the target computation processor. The displays are video displays and show camera derived imagery superimposed with detection and aiming symbology and system status reports. The user interface devices are keypads and audible or vibrational alarms which control the operation of the system and alert the user to flash detections which are equated to sniper firing, for example. In still another aspect of the invention, the weapon aim point measurement devices include inertial measurement units, gyroscopes, angular rate sensors, magnetometer-inclinometers, or gimbaled shaft encoders. Visual target confirmation sensors are binoculars or rifle scopes with associated aim point measurement devices. Counterfire weapons contemplate rifles, machine guns, mortars, artillery, missiles, bombs, and rockets.


The basic objective of the present invention is to provide an automated and improved muzzle blast detector system and method which uses multi-mode filtering to eliminate and/or minimize false alarms.

Another object of the invention is to provide a muzzle blast detector which accurately locates direction and range to muzzle blast source.

Another object of the invention is to provide a sniper detection method and apparatus which uses temporal, spectral and spectral filtering to discriminate between

actual muzzle blasts and non-muzzle blast infrared generating events.


FIG. 1 is a general block diagram of a muzzle blast detection system incorporating the invention,

FIG. 2 is a further block diagram of the detection system of the invention,

FIGS. 3A and 3B are graphs of simulated event signatures and corresponding matched filter for 60 FPS video,

FIG. 4 is a diagrammatic representation of the event filter,

FIG. 5 illustrates a sample detection filter,

FIG. 6 is a circuit diagram of a detector with an adaptive threshold level,

FIG. 7 is a depiction of a low pass spatial filter response h (K,l),

FIG. 8 is a circuit diagram showing adaptive detection system with low pass filtered “[sgr]” and high pass filter e (2).

FIG. 9 illustrates the decision filter,

FIG. 10 illustrates the overall detection and location algorithm, and

FIG. 11 illustrates the video acquisition subscription.

FIG. 12 is a schematic diagram of an embodiment of the instant invention.

FIG. 13 is a flow chart of an embodiment of the instant invention.


The aspect of the invention comprises an infrared camera 10 connected to image processing circuits 11 and a video display 14 which may include an annunciator 14A to provide an immediate audible or tactile indication of the muzzle blast event. The camera 10 stares at a field of view, and the video signal is fed to the image processor 11. The pedestal and gain controls of the camera are controlled by the image processor.


The image processor outputs the live infrared video to the display. Concurrently algorithms to detect the presence of a muzzle flash in the image are executed on the image processor. When a muzzle flash is detected the image processor 11 overlays a symbol on the display around the pixel location where the flash was detected. The algorithms that detect the muzzle flash operate by processing several frames of video data through a temporal and spatial digital filter. The activity level at each pixel location is adaptively tracked and the effect of background clutter is reduced by varying the detection threshold at each pixel according to the past activity around that pixel location. The detection algorithms are described in more detail in the section entitled Detection of Short Duration Transient Events Against a Cluttered Background.

Automatic Pedestal and Gain

An algorithm is used for automatic adjustment of the pedestal and gain values of the imaging system to achieve high dynamic range. Additional user control over these settings allows certain regions of the image to be dark or saturated. This algorithm is described in the section entitled Automatic Pedestal and Gain Adjustment Algorithm.


The coordinates of the detected muzzle flash are fed to targeting circuitry 12 to guide a visual target confirmation sensor 13, such as binoculars or a telescope, and a counterfire weapon, such as a rifle, onto the target.

Weapon Aim Point to Camera Coordinate Calibration

Given weapon aim point measurement readings 15, the corresponding image coordinates in the camera field of view are derived. The aim point measurement devices generate an azimuth and elevation reading. The calibration procedure includes aiming the weapon at three known calibration points. These points are marked by the user on the display 14 using a cursor. The image coordinates and the aim point measurements for these points are used to generate a mathematical transformation so that, given any aim point measurement, it's corresponding image location can be calculated. Symbology denoting the current weapon aim point is displayed on screen 14, and the difference in target screen locations is used to guide the return fire shooter onto the target.

Visual Confirmation

An aim point measuring device 15 is aligned with the confirmation sensor. This device provides the azimuth and elevation (line of sight) of the sensor. The aim point measurement device 15 is correlated to the camera optical axis and orientation using a multipoint calibration procedure, thereby relating azimuth and elevation readings to camera pixel locations. The targeting processor calculates the difference between muzzle flash detection location and the instantaneous pointing location and displays guidance symbology to direct the confirmation sensor to the target.

Confirmation Sensor Aim Point to Camera Coordinate Calibration

The line of sight of the confirmation sensor is calibrated to camera coordinates using the three-point calibration algorithm used for calibrating the weapon aim points to camera coordinates. Either the same or different calibration points can be used for weapon to camera and confirmation sensor to camera calibration. Symbology denoting the current confirmation sensor line of sight is displayed on screen, and the difference in target screen locations is used to guide the observer onto the target.

Calibration Using Gimbaled Telescope with Encoders

A telescope, on a gimbal with shaft encoders, mounted on the camera is used to determine the location of the calibration points. The user points the telescope at a calibration point. The telescope gimbal is aligned with the camera, and the image coordinates of the telescope line of sight are known. By selecting three calibration points and aiming the weapon or confirmation sensor at these points the transformation between the aim point measurement devices and camera coordinates can be calculated.

User Interface

The user interface includes a keyboard KB and cursor controlled mechanism M to control the operation of the system, a video display screen 14, and a detection alarm 14A. The user is alerted to a detection through an audible alarm of a silent tactical vibration, or other type of silent alarm device which is triggered by the targeting processor. The user is then guided through symbology overlaid on the display to move the confirmation, sensor weapon until the line of sight is aligned with the detected flash.

Ring Display

A peripheral vision aiming device is also used to guide a confirmation sensor or weapon to the target. The aiming device consists of a ring of individual lights controlled by the targeting processor. The ring may be placed on the front of a rifle scope, in line with the rifle's hard sites or other locations in the peripheral view of the operator. When a detection is made, the targeting processor activates one or more lights to indicate the direction and distance the confirmation sensor/weapon must be moved to achieve alignment with the flash. The number of activated lights increases as the degree of alignment increases. All lights are activated when alignment is achieved.

The following section describes the adaptive algorithm for detection of rapid transient events where a noisy background is present. The theoretical background and a sample implementation are given.

It is desired to detect and locate transient events against a noisy background in real time. The detection and location of such an event requires a prior knowledge about the spectral, spatial and temporal signatures of typical events. It is also desirable to have information about the background conditions in which the detection system is expected to operate. This information consists of the spectral, spatial and temporal characteristics of the background.

If the statistics of the four-dimensional signal which is specified as the signature of a typical event (spectral, spatial and temporal axes) are known, and if the same statistics for various backgrounds are measured, it becomes a simple matter of applying standard stochastic analysis methods (matched filtering) in order to solve the problem. However, this information is not readily available and there are several other problems which make this approach unfeasible.

The first difficulty is that the instrumentation to simultaneously extract all components of signals that have spectral, spatial and temporal components is not readily available. Equipment is available to acquire simultaneously either the spectral and temporal (spectrometry), or the spatial and temporal (video) components from a scene. It is also possible, through the use of several imagers to acquire multispectral image sets, essentially sampling the scene at several spectral bands.

Operating at a suitably chosen fixed spectral band, the intensity variation as a function of time was the easiest component of the event signature to detect.

Detection Methods Which Deal Only with Spatially and Temporally Varying Signal Components at a Fixed Spectral Band

The concept of matched filtering can be used if the statistics of the events to be detected and backgrounds are available. However, many factors, such as humidity, ambient temperature, range, sun angle, etc. influence these statistics. It is not practicable to gather data for all combinations of rapid transient events and background scenes. Thus, for the detection algorithm to reliably work against different background environments, it has to adapt to these environments.

The Detection System

The video signal from the camera 10, under control of controller 18, is digitized 16 and supplied to an image processing system 17 and continuously stored in memory M at frame rates (FIG. 2). In this invention, the image processor 17 is adapted to operate on the latest and several of the most recent frames captured. Although in this case the processor operates on progressively scanned 256*256 pixel frames at a rate of 60 frames per second, the algorithm can be used at other resolutions and frame rates.

The camera 10 being used is a CCD imager, which integrates the light falling on each pixel until that pixel's charge is read out. The read out time is typically much less than the typical transient event duration. This means that the imager effectively has a 100% duty cycle, with no dead times between frames for each pixel. The camera pedestal and gain settings are set to fully utilize the dynamic range of the image processing system. The algorithms for this are described later herein.

The first stage of the detection algorithm includes a temporal Event Filter 20 which is tuned to detect rapid transient signatures, followed by a spatio-temporal Detection Filter designed to reject background clutter. The output of this first stage is a list of candidate event times and locations. These coordinates form the input to a logical processing stage which then estimates the probability of the candidate event actually being due to a single uncorrelated rapid transient.

The Event Filter 20

The event filter 20 is a finite impulse response matched filter which operates on each pixel in the sequence. The impulse response of the filter is derived by estimating the signature of the typical transition event.

The events to be detected typically have much shorter duration than the frame repetition rate. Therefore, most of the time the rapid transients occur wholly inside one frame. However, it is possible to have a single event overlapping two adjacent frames. The time of occurrence of a transient event and the frame times are uncorrelated processes, and the overlap can be modeled by considering the event time to be uniformly distributed over the frame interval.

A simple model of a rapid transient signature consists of a pair of exponentials, one on the rising edge and another on the falling edge of the event. FIG. 3 shows the case where a rising time constant [tgr] (r) of 0.125 mS and a falling time constant [tgr] (f) of 0.5 mS are chosen. This waveform is convolved with the rectangular window of the frame and the result integrated over successive frame periods reveals the optimal matched filter coefficients.

The event filter then is a tapped delay line finite impulse response filter and its output, the error signal, can be written as the simple convolution:

    • Get Mathematical Equation

Since h(k), the impulse response of the Event Filter is indexed only to the frame number, this filter is purely temporal and has no spatial effects.

The Detection Filter

The simplest detection scheme for a transient event consists of an event filter 20 followed by a threshold device (comparator 21, FIG. 5). This system works reasonably well in cases where the background scenery is not noisy and where false alarm rejection requirements are not demanding.

The simple detector approach is also useful in serving as a baseline to compare the performance of more complicated algorithms. A figure of merit can be devised for other algorithms by comparing their detection performance to the simple detector.

In order to reduce the false alarm rate additional processing is performed. The approach taken here is to use adaptive filtering methods to vary the decision threshold spatially, so that image areas of high activity have higher and areas of less activity have lower threshold levels. Thus, the threshold level becomes a varying surface across the image.

A good estimate of the activity level for each pixel in the image is the mean square of the signal e(i,j,n), the event filter output. Since this signal is already generated, its calculation imposes no additional computational burden. The calculation of the mean square however still needs to be performed.

Instead of the actual mean square computation to estimate the energy of the intensity signal at each pixel, a recursive estimate is used. Thus we define:
[sgr](i,j,n)=[mgr][sgr](i,j,n−1)+(1−[mgr]) e(i,j,n)   (2)
where [mgr] the learning rate is a constant between 0 and 1. A typical value for [mgr] is 0.95. The best choice for the learning rate will be determined depending on the stationarity of the background scene (in both the statistical and the physical senses).

The recursive formulation for [sgr](i,j,n) makes it easy to implement. The infinite impulse response filter 32 that implements this has a low pass transfer function, and thus tends to “average out” the activity at each pixel location over its recent past.

To simplify implementation, it is possible to remove the square-root operation 33 on the threshold surface, and compare the estimated variance of the signal e to the square of its instantaneous value. Thus, the output of the comparator essentially becomes a measure of the difference of the instantaneous energy in the signal to the estimated average energy at that pixel.

Some of the physical phenomena that cause false alarms are edge effects, thermal effects such as convection, camera vibration, and moving objects. A significant portion of these can be eliminated by performing a spatial low pass operation on the variance estimate signal a. This is to spread the threshold raising effect of high energy pixels to their neighbors. However, a pure low pass operation would also lower the a values at the peaks of the curves. To offset this a “rubber-sheeting” low pass filter is used. This is mathematically analogous to laying a sheet of elastic material over the threshold surface. The threshold surface thus generated is calculated by:
[thgr](i,j,n)=max[[sgr](i,j,n),[sgr] (LP)(i,j,n)]  (3)
where [sgr] (LP) is the low pass filtered estimated variance, calculated by the convolution:

    • Get Mathematical Equation

The low pass spatial filter 45 coefficients h(k,l) are chosen depending on the sharpness desired. A set of values which gives good results is generated using a normalized sinc function is plotted in FIG. 7.

A possible enhancement to the detection algorithm is the inclusion of a spatial high pass filter 42 to reject image events which occupy large areas. Depending on the application (i.e. whether rapid transient events which occupy relatively large areas are desired to be detected or not), such a filter may reduce the system's susceptibility to false alarms due to events which are not of interest. The block diagram of the detector incorporating these modifications is shown in FIG. 8.

It should also be noted that in the system shown the comparator 43 output is no longer a binary decision but a difference signal. While it is possible to use the compactors' binary output as a final decision stage, it is convenient to further process the output of the detection filter.

The Decision Filter (FIG. 9)

For each pixel, a value for the detector signal det(i,j,n) is generated at the frame rate. Thus, the data rate of the detector output is comparable to the raw image data rate. The detector signal is a measure of the likelihood that an event has occurred at the corresponding pixel. This information has to be reduced to a simple indication of the presence and location of the event. The decision filter performs the required operation.

The detector output can be filtered in several ways. The obvious and simple method is to compare it with a set threshold value. Another way is to first localize the possible location of the one most likely event in each frame, and then to decide whether it actually is present or not. This approach is simple to implement and results in significant reduction in the amount of data to be processed. Its limitation is that it does not allow the detection of multiple transient events occurring within a single frame.

The location of a single candidate transient event per frame is done in locator 50 by finding the pixel location with the maximum detector output. If this signal exceeds a detector threshold T, then a “Transient Detected In Frame” indication is output, otherwise the output indicates “No Transient Detected In Frame”.

The decision filter 51 operations are as follows: Get Mathematical Equation T ( n ) = [ agr ] T ( n - 1 ) + ( 1 - [ agr ] ) d ( n ) ( 6 )

This operation, similar to the calculation of [sgr], is a recursive implementation of an adaptive threshold. The learning rate [agr] (again chosen between 0 and 1 and typically about 0.9) determines the speed with which the system adapts to changes in the background levels.

The decision filter block diagram is shown in FIG. 9.

The overall block diagram of the adaptive detection algorithm is shown in FIG. 10.

Using the approach presented here, it is possible to determine the presence or absence of short duration transient events. The invention is especially useful when the background scene is cluttered and contains elements which have statistical properties similar to those of the events being searched for. This is done by utilizing as much of the available knowledge about the spectral, spatial, and temporal characteristics of the events to be detected.

Automatic Pedestal and Gain Adjustment Algorithm

The detection of a rapid transient event in a noisy background is significantly degraded if the full dynamic range of the imaging system is not used. This presents a simple algorithm for automatic adjustment of the pedestal and gain values of the imaging system to achieve high dynamic range. In some situations it is desired to have additional control over exposure to allow certain regions of the image to be dark or saturated. A version of the algorithm with exposure control is given below.

Automatic Pedestal and Gain Adjustment Algorithms

The pedestal and gain adjustment algorithm presented here assumes an 8 bit imaging system is being used. The response is assumed to be roughly linear. However, the algorithm will work well with nonlinear imagers as well. The image acquisition subsystem block diagram is shown in FIG. 11.

Two versions of the algorithm are presented here. The simpler first version automatically sets the pedestal and gain values to equalize the image so that all pixels lie throughout the full range of the imaging system. The coefficients of the system have to be adjusted so that the response is not oscillatory (i.e. their values have to be chosen so that the closed loop transfer function has magnitude less than unity). In the slightly more complex second version, the user is given an additional control to allow under- or over-exposure as desired.

The following procedure summarizes the detection system algorithm without exposure control:

Grab one frame of data. Within a region of interest (typically the whole picture minus a frame around the edges) count the number of saturated pixels (n (5at)) and the number at full darkness (n (zer)). Measure the value of the darkest pixel (botgap) and the distance between the brightest pixel and 255 (topgap). Change the pedestal and gain settings to spread the histogram of the image. Repeat for next frame.

The dynamic pedestal and gain equations are:
[Dgr] p=p(1)n−pbotgap
[Dgr] g=−g(1)n+g(2)topgap−k[Notidenticalwith]p
pedestal=pedestal+[Dgr] p
gain=gain+[Dgr] g

Optimal values for the tracking parameters p (1), p (2), g (1), G (2) and k depend on the camera response. However, since feedback is used, this effectively “linearizes” the control loop, and depending on the temporal response desired, suitable values can be derived empirically.

The following describes the detection algorithm with exposure control.

This version is slightly more complex in that it adds an exposure control input to the original algorithm. The variable exposure determines the amount of under- or overexposure desired. This operates in a manner analogous to the exposure control found in automatic cameras. When exposure is set at a positive value, the pedestal and gain dynamics are set to allow a number of pixels to stay saturated (overexposure). Similarly, a negative exposure control allows a number of pixels to stay at zero (underexposure). The dynamic equations are:
n(up)=n(zer)+min(exposure, 0)
n(down)=nsac−max(exposure, 0)
[Dgr] p=p(1)n(up)−p(2)botgap
[Dgr] g=−g(1)n(down)+g(2)topgap−k[Dgr]p
pedestal=pedestal+[Dgr] p
gain=gain+[Dgr] g

Thus, with a positive exposure setting, the only effect is at the top end of the digitization range, so that n (up) is not altered (it stays equal to n (zer)) but n (down) is less than n (sat). This means that a number of pixels (equal to the magnitude of exposure) are allowed to stay saturated. Conversely, with a negative exposure ndow is unaltered but n (up) is allowed to go to a negative number, signifying that a number of pixels are allowed to stay dark.

The above description of the VIPER suite incorporates by reference herein U.S. Pat. No. 6, 496,593 to Krone, Jr. et al.

Decreased response time for Confirmation

In FIG. 12, a standard two axis pan and tilt gimbal 1210 is operably connected to the Targeting Processor 1215. An alignment, including registration and calibration, is performed between the gimbal position and the detection camera pixel locations. The alignment is accomplished using reference sources located at a distance from the sensors. The gimbaled camera sensors are calibrated so that the differing fields of view are matched to each other. After this calibration, the gimbal can rapidly point at a given location corresponding to a triggering event. A standard joystick 1220 is interfaced to the Targeting Processor 1215 to enable the user to move the gimbal 1210 independently to locate areas of interest.

Day/Night Functionality

A Day/Night Color Vision System (“DNCVS”) 1225 is placed on the high speed gimbal 1210. This subsystem 1225 serves as an adjunct system to the instant VIPER suite. The DNCVS 1225 provides the user with a day/night “visual” validation of the triggering event. The DNCVS 1225 comprises standard multi-spectral cameras that are sensitive to both daytime and nighttime environments. Such multi-spectral cameras include, for example, standard long wave infrared, standard short wave infrared (“SWIR”, and standard visible band, e.g., video, cameras. The use of multiple cameras permits viewing of camouflage, cold targets, hot targets, and reflective (white/black) targets in several spectral bands. Use of multiple bands optimizes target contrast and provides better penetration through obscurants such as smoke and fog. Selection of the proper bands for the situation enables the operator to observe the scene for a wide variety of conditions. For day operations, the visible video cameras provide the best performance. For twilight operation, the SWIR cameras provide superior performance. For starless nights, the long wave IR cameras offer the best performance. Combining the sensors for transition periods, e.g., day to night, can give the best performance as the environmental conditions change.

Variable camera fields-of-regard are embodied by, for instance, standard zoom optics or standard controlled flip lenses. The operator may select either automated or manual zoom controls allowing optimization of the fields-of-regard.

The operator's user interface 1230 permits selection of specific cameras of the DNCVS 1225 that can be displayed. This display 1230 can be selected to be either monochromatic or color. Various false color display schemes are available. Color fusion schemes, such as described in U.S. patent application Ser. No. 09/840,235 to Penny G. Warren, entitled “Apparatus and Method for Color Image Fusion,” filed Apr. 24, 2001, and incorporated herein by reference, are selectable for combination of multiple cameras into a single display. Fusion of previously stored images with real-time sensor imagery is also available. Each camera can be optimized for maximum scene contrast by user-selected options. Both analog and digital sensor data is available for processing or storage. For highlighting features at long ranges super-resolution enhancements can be employed. Frame summation techniques can be employed for highlighting dim targets. Laser or other illuminators are used to highlight dim objects or designate an area of interest for external observers. Additionally, it is possible to convert individual wide-band cameras into a multi-color operation by use of laser (or other narrow-band) illuminators in an on-off contrast fashion. These capabilities are controlled by the operator through hardware and/or software interfaces.

The IR detection camera is mounted, for instance, on a plate along with the gimbal 1210. The rest of the sensors are mounted on the gimbal 1210. The Camera 1260 includes, a detection camera such as a midwave IR detection camera. Other cameras are attached to the gimbal 1210, for example, since their field of regard is much smaller than the detection camera 1260. The other cameras are included in the DNCVS 1225.

Video Storage

Camera imagery is also passed into a recording device 1235, e.g., a Video Storage Device. The storage device 1235 enables archiving and analysis of data and events at a later time.

Enhanced User Interface with External Display

An external portable display 1230 (e.g. a monocular with a shuttered eyecup) is linked to the Targeting Processor 1215. This enables multiple people in nearby locations to view the same real time data that is presented on the system display.


A standard Laser Range Finder 1240 is fixed to the gimbal 1210 permitting ranging to a designated object of interest. A standard magnetometer/digital compass 1245 and standard GPS 1250 are interfaced to the Targeting Processor 1215 providing positional reference of the detection system. The combination of the information from the magnetometer 1245, gimbal 1210, laser range finder 1240 and GPS 1250 provide the capability of geolocating the place where the event occurred. The specific place is then referenced and displayed on a stored map in the system and provided to the system operator. Standard commercial software is available for this function, such as Weapons Systems Mapping software produced by DCS Corporation. This information can be passed to external entities in order to enable them to react to the event.

Tailored Spectral Bands for Different Missions

Several narrow-band cold filter settings have been developed which optimize the performance of the present VIPER detection system. Cold filters are filters cooled down to avoid noise generated by a filter's heat. The noise otherwise drives down contrast. These spectral band settings are chosen based upon the characteristics of the gunfire or ordnance to be observed as well as the properties of the background clutter and the intervening atmosphere. For example, for urban operations, a narrowing of the midwave IR camera passband reduces the false alarms at the cost of shorter detection ranges. By choosing the spectral band, the instant VIPER system is optimized for daytime or nighttime; long-range or short-range detection; or urban vs. rural settings. Proper choice of narrow spectral bands enhances system operation when the system is on a moving platform. The optimization can be fixed for a given situation. A variable filter setting is employed if a standard tunable filter is available to adjust to the specific situation. Alternatively, multiple cameras with individually optimized filters are used instead.

Anamorphic Lens Improvement

A standard very wide angle anamorphic lens 1255 has been developed and implemented that provides a wide angle field of view in one dimension. This lens optimizes the field-of-regard of the detection camera 1260 eliminating the need for multiple cameras to provide the wide angular coverage

Increasing Re-active Coordination through Optical Illumination/Designation

Optical illuminators/designators 1265 are attached to the gimbal 1210. They can be aligned in such a fashion to enable the user to illuminate/designate the event of interest. This cues external entities to the existence/relative location of a possible target.

Perimeter Defense Operations

The high speed gimbal 1210 containing, or communicating with, the DNCVS 1225 can also be used as a Perimeter Defense surveillance subsystem. This allows the operator to do a sweep-scan or a step-stare over selected angular regions. The timing and selection of the coverage is operator-controlled. The Perimeter Defense surveillance subsystem enhances the situational awareness of the operator by highlighting events such as intrusions. Motion and scene change detection processing can be added to the Perimeter Defense surveillance subsystem to highlight features. The operator can examine the user display and decide to dwell on objects of interest within the Perimeter Defense coverage. Optionally, a triggering event, such as a muzzle flash, overrides the Perimeter Defense surveillance so that the event can be identified and/or targeted.

FIG. 13 shows an illustrative method according to the instant invention.

In Step S1310, a physical flash has occurred in the IR Detection Camera field of regard.

In Step S1320, the detection camera images the flash through a sequence of frames. The Image Processor then filters the imagery and determines if a shot has occurred. If so, it then passes a message to the Targeting Processor. In this instance, this is accomplished through an Ethernet interface between the Image Processor and Targeting Computer.

In Step S1330, after a detected shot, the Image Processor can alert the users with either a vibration, an audible alarm and or a visible cue to alert friendly forces in the area.

In Step S1340, the Targeting Processor display then alerts the user and updates the display to indicate such. For instance, this may be accomplished through adding the detected event to a list of already detected events and/or drawing an icon on a display representing the detection camera field of regard.

In Step S1350, in the case that multiple events occur in a short period of time, to prevent confusion of the operator, a Gimbal Slew Override allows the user to deal with individual events in a serial fashion.

In Step S1360, if the Gimbal Slew Override is on, the user is busy attending to a previous event. Thus the gimbal is not deviated from its current position. Meanwhile, the user has available a selection of Commands (STEP S1380) to help react to the previous event.

In Step S1370, if the Gimbal Slew Override is off, the Targeting Processor then drives the gimbal to the position corresponding to the alerted event. Imagery of the area of interest is displayed on the Target Processor display.

In Step S1380, the Available User Commands are a set of controls that help the user to adapt to various conditions. For instance, a user may select to view a different color of imagery based on day or night.

While the invention has been described and illustrated in relation to preferred embodiments of the invention, it will be appreciated that other embodiments, adaptations and modifications of the invention will be readily apparent to those skilled in the art.

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U.S. Classification89/41.06
International ClassificationF41G1/32
Cooperative ClassificationF41G3/147, G01S3/784
European ClassificationF41G3/14D