CN116299492A - Bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting - Google Patents
Bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting Download PDFInfo
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Abstract
The invention relates to a bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting, which utilizes the characteristic that the occurrence position of real features of the submarine in an imaging image is fixed and false features change along with the position of a receiving array, acquires the distribution features of pixel points of an acoustic image by adopting a statistical method, takes the statistical distribution probability of the pixel points as an image pixel weight, and realizes weighted splicing on multi-frame acoustic images. Comprising the following steps: modeling simulation of bistatic submarine scattering signals, acoustic imaging processing of received signals, statistical distribution processing of pixels and weighting, splicing and fusion of multi-frame acoustic images. Compared with the conventional bistatic acoustic imaging method, the method provided by the invention can effectively reduce false features and random noise caused by the inherent characteristics of the one-dimensional horizontal linear array in the acoustic imaging diagram, highlight real features in the acoustic image, and realize imaging and distinguishing of the seabed significant features.
Description
Technical Field
The invention belongs to the field of sonar imaging, and particularly relates to a bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting, which is suitable for the fields of submarine mapping, submarine target or topography (sea mountain and the like) imaging and identification and the like.
Background
The bistatic submarine topography acoustic imaging can enlarge the sonar operating distance, fully utilize submarine forward scattering signals and realize large-scale submarine acoustic imaging. Literature (Ratillal P, lai Y, symonds T, et al Long range acoustic imaging of the continental shelf environment: the Acoustic Clutter Reconnaissance Experiment 2001.[ J ]. Journal of the Acoustical Society of America,2005,117 (4 Pt 1): 1977-1998.), (Makris N C, chia C S, fialkowski L.T. bi-azimuthal scattering distribution of an abyssal hill [ J ]. The Journal of the Acoustical Society of America,1999,106 (5): 2491-2512.), (Swee Chia C, makris N C, fialkowski L T.A comparison of bistatic scattering from two geologically distinct abyssal hills [ J ]. The Journal of the Acoustical Society of America,2000,108 (5): 2053-2070.) proposes the use of a dual-base seafloor topography imaging system, using two vessels carrying vertical and horizontal towing arrays, respectively, the transmitting vessel being fixed to the test area, receiving the vessels traveling at different survey lines and receiving seafloor forward scatter signals, obtaining seafloor topography intensity distribution images. However, in the conventional bistatic acoustic imaging, due to the fact that left-right blurring is caused by the one-dimensional space characteristics of the receiving linear array, false features axisymmetric to the receiving array appear in the submarine topography imaging diagram, submarine real scattering features are not easy to distinguish under the condition that the prior inspection environment information is less, and the identification of submarine targets or topographic features in the acoustic image is affected.
The main methods for solving the left-right blurring phenomenon at present are as follows: 1) The receiving end adopts a double-level linear array and a multi-level linear array, and the time delay difference of signals reaching each receiving array is utilized to distinguish, so that the method has higher requirements on the performance of equipment and is more complex to realize in engineering technology; 2) Judging according to the incidence angle change trend of the submarine forward scattering signal by utilizing the maneuvering of the receiving ship, wherein the method needs to wait for the completion of the maneuvering of the receiving ship, and judges after the receiving towed line array is straightened, and has limitations and hysteresis; 3) And performing matrix estimation on the receiving towed line array, and then performing back-end processing.
Disclosure of Invention
Technical problem to be solved
Aiming at the false characteristics in the submarine topography imaging graph caused by the left-right blurring caused by the one-dimensional characteristics of the receiving linear array in the existing bistatic submarine topography imaging method, the invention provides the bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting.
Technical proposal
A bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting is characterized by comprising the following steps:
step 1: modeling the bistatic submarine forward scattering signal;
assuming that the transmitting array is arranged at the central origin of the observation area, the receiving array adopts an N-element horizontal linear array, and the array element distance is d. The transmitted signal adopts a center frequency f 0 The signal bandwidth is a linear frequency modulation signal of B, and the signal pulse width is T; setting p scattering coefficients as sigma on the sea floor p Simulating subsea forward scattering;
the transmitted signal is represented as
Wherein T represents time, T represents signal pulse width, and B represents signal pulse width;
when the receiving array is located at a certain line point, the receiving echo on the N (n=1, 2,) th array element is
Wherein n (t) is expressed as noise at the receive array element, σ p Represents the scattering coefficient, τ, of the p-th scattering point p Representing the time delay of propagation through the transmitting array to the p-th scattering point to the receiving array reference element (element number 1),
denoted as phase delay on the nth receive element, denoted as
Wherein θ p A signal incidence angle indicating a p-th scattering point;
step 2: after receiving the submarine forward scattering signals acquired by the towed line array, processing the received array element signals by adopting a conventional beam forming and matched filtering method to obtain time domain output signals of all beam angles, namely beam angle-time images; processing the beam angle-time image by adopting a conventional time domain bistatic acoustic imaging method to generate a submarine topography acoustic intensity image;
sub-step 1: for x in the previous step n (t) beamforming, the beam output of which can be expressed as
Wherein,,
weighting coefficients for the nth array element;
sub-step 2: carrying out matched filtering processing on the beam output in the sub-step 1, and taking an absolute value to obtain a beam angle-time image
Wherein R is s (t) is the autocorrelation function of the transmitted signal,
representing a convolution;
sub-step 3: the beam angle-time image is subjected to acoustic imaging processing by adopting a conventional time domain bistatic acoustic imaging method, and the imaging homing point transmission delay tau is calculated by the position of a transmitting array, the position of a receiving array and the coordinates (x, y) of an imaging homing point (x,y) And an incidence angle theta (x,y) The amplitude A (x, y) at the imaging homing point coordinates (x, y) can be expressed as
A(x,y)＝|y beam -MF(θ (x,y) ,τ (x,y) )| (6)
Step 3: carrying out pixel point statistical analysis on the current frame acoustic imaging image, adopting a sliding window local peak value to detect and process the acoustic intensity image, and synchronously generating a hot spot statistical image consistent with the dimension of an acoustic image matrix, wherein the hot spot statistical image consists of values 0 and 1, the value higher than a local threshold value is set as 1, and the value lower than the local threshold value is set as 0; performing corresponding element point multiplication operation on the original sound image and the hot spot statistical image to obtain a new image of the current frame
B i (x,y)＝A i (x,y).*I i (x,y) (7)
Wherein (x, y) is the pixel coordinates of the acoustic imaging map, B i (x, y) represents a new image obtained by the dot product operation of the ith frame, A i (x, y) represents the original sound image of the ith frame, I i (x, y) represents a hot spot statistical image corresponding to the ith frame of sound image;
step 4: image stitching is carried out on the acoustic images of all the measuring points by adopting a weighting method, and finally, submarine topography acoustic images of the observation area are generated;
the process of splicing the acoustic images of all the measuring points is as follows, and the total image after the acoustic images of the previous (i-1) frame are spliced is set as F i-1 (x, y), the new image obtained by the point multiplication operation of the ith frame is B i (x, y), then the total concatenation result F of i frames of acoustic images i (x, y) is expressed as
F i (x,y)＝w 1 (x,y)F i-1 (x,y)+w 2 (x,y)B i (x,y) (8)
Wherein w is 1 (x,y)、w 2 (x, y) is image F i-1 (x,y)、B i Weighting coefficients of (x, y) pixel points, the weighting coefficients being calculated by equation (9)
A computer system, comprising: one or more processors, a computer-readable storage medium storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods described above.
A computer readable storage medium, characterized by storing computer executable instructions that when executed are configured to implement the method described above.
Advantageous effects
The invention provides a bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting by utilizing the characteristic that the occurrence position of a submarine real feature in a topography imaging graph is fixed and a false feature changes along with the position of a receiving towing line array. According to the method, the sliding window peak value detection is adopted to process the submarine topography acoustic image generated by the conventional bistatic acoustic imaging method, statistical distribution characteristics of pixel points in acoustic images of different frames are obtained, the statistical distribution probability of the pixel points is introduced to serve as the pixel weight of the acoustic image, and multi-frame submarine topography acoustic images are subjected to weighted splicing, so that false characteristics in the submarine topography acoustic image are effectively restrained, real characteristics in the topography acoustic image are enhanced, imaging and distinguishing of submarine significant characteristics are achieved, and submarine topography acoustic imaging effects are improved.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a schematic diagram of a bistatic configuration of the method of the present invention.
FIG. 2 is a schematic flow chart of the method of the present invention.
Fig. 3 is a schematic diagram showing the distribution of a transmitting array, a receiving array and a preset seabed target in an implementation example of the method of the present invention.
Fig. 4a and 4b are single-frame acoustic imaging diagrams processed by a conventional imaging method at different receiving positions.
Fig. 5a and 5b are single-frame acoustic imaging diagrams obtained by adopting pixel statistics processing in the third step.
Fig. 6 is a graph stitching result of the conventional acoustic imaging method. Wherein fig. 6 (a) is a general mosaic of acoustic imaging maps of the observation area; FIG. 6 (b) is a partial enlarged view of the real feature area; fig. 6 (c) is a partial enlarged view of the dummy feature region.
FIG. 7 is a probability map of statistical distribution of pixel hot spots obtained by the method of the present invention.
FIG. 8 shows the result of the processing of the acoustic imaging graph according to the method of the present invention. Wherein fig. 8 (a) is a general mosaic of acoustic imaging maps of the observation area; fig. 8 (b) is a partial enlarged view of the real feature region.
Fig. 9 is a graph showing the results of image stitching after processing test data using a conventional acoustic imaging method.
FIG. 10 is a probability graph of statistical distribution of pixel hot spots obtained by processing test data according to the method of the present invention.
FIG. 11 is a graph showing the results of an imaging splice after processing test data using the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The invention provides a bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting, which comprises the steps of firstly constructing a bistatic submarine scattering signal model, adopting two boats to respectively bear a vertical transmitting array and a horizontal towing line array, fixing the transmitting boat at the center of an observation area and transmitting signals, receiving boat movements and receiving forward scattering signals containing submarine characteristic information; further adopting beam forming and pulse compression to obtain a beam angle-time image of sonar imaging, and adopting a conventional bistatic acoustic imaging method to obtain a submarine topography acoustic image corresponding to an observation area; the characteristic that the position of the real feature of the sea bottom in the landform imaging map is fixed and the fuzzy feature changes along with the position of the receiving linear array is utilized, the current acoustic image is processed by adopting sliding window peak detection, the distribution feature of the acoustic image pixel points is obtained by utilizing a statistical method, and corresponding hot spot statistical images (represented by numerical values 0 and 1) are synchronously generated; and finally, taking the statistical distribution probability of the pixel points as the weight of the pixels of the image, realizing weighted splicing on a plurality of frames of submarine landform acoustic images, effectively enhancing the real characteristics in the imaging diagram, and simultaneously inhibiting false characteristics and random noise.
As shown in fig. 1 and 2, a bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting comprises the following steps:
step one: modeling the bistatic submarine forward scattering signal;
as shown in FIG. 3, the transmitting ship is fixedly positioned in the center of the observation area, the depth is 60m, the receiving array adopts an N-element horizontal towing line array, and the array element distance is d. The transmitted signal adopts a center frequency f 0 And the signal bandwidth is B, and the signal pulse width is T. Setting p scattering coefficients as sigma on the sea floor p Is modeled as a point target for subsea forward scattering.
The transmitted signal is represented as
Fig. 3 is a schematic diagram showing the distribution of the transmitting array, the receiving array and the submarine targets in an example of the implementation of the method of the invention. Wherein # -is denoted as the transmitting matrix position, Δ is denoted as the horizontal receiving matrix position, →is denoted as the receiving matrix heading, and black +..
In the implementation example, the transmitting signal adopts a linear frequency modulation signal, the center frequency of the signal is 1800Hz, the bandwidth is 200Hz, and the pulse width is 1s. The range of the observation area is set to 20km multiplied by 20km, the sea depth is 1100m, the coordinates of the transmitting array are (0 km,0km and 60 m), the initial coordinates of the motion of the receiving array are (-6 km, -3km and 60 m), and the heading angle is 45 degrees. The seabed surface is provided with 9 point targets, the point target coordinate positions are respectively set as (-4 km,1000 m), (-4 km,5km,1000 m), (-4 km,6km,1000 m), (-5 km,4km,1000 m), (-5 km,1000 m), (-5 km,6km,1000 m), (-6 km,4km,1000 m), (-6 km,5km,1000 m), (-6 km,1000 m), and the point target scattering coefficient sigma p All set to 1, assuming that there is no channel distortion, the received echo on the nth (n=1, 2,..n.) element of the receiving array can be expressed as
Wherein n (t) is expressed as noise at the receive array element, σ p Represents the scattering coefficient, τ, of the p-th scattering point p Representing the time delay of propagation through the transmitting array to the p-th scattering point to the receiving array reference element (element number 1),
denoted as phase delay on the nth receive element, denoted as
Wherein θ p The signal incidence angle of the p-th scattering point is indicated. The received element signal in this example can be obtained by equation (3).
Step two: processing the received array element signals by adopting a conventional beam forming and matched filtering method to obtain time domain output signals of each beam angle, namely, beam angle-time images; and processing the beam angle-time image by adopting a conventional time domain bistatic acoustic imaging method to generate a submarine topography acoustic intensity image.
Sub-step 1: for x in the previous step n (t) beamforming, the beam output of which can be expressed as
Wherein,,
weighting coefficient for the nth array element.
Sub-step 2: carrying out matched filtering processing on the beam output in the sub-step 1, and taking an absolute value to obtain a beam angle-time image
Wherein R is s (t) is the autocorrelation function of the transmitted signal,
representing a convolution.
Sub-step 3: the beam angle-time image is subjected to acoustic imaging processing by adopting a conventional time domain bistatic acoustic imaging method, and the imaging homing point transmission delay tau is calculated by the position of a transmitting array, the position of a receiving array and the coordinates (x, y) of an imaging homing point (x,y) And an incidence angle theta (x,y) The amplitude A (x, y) at the imaging homing point coordinates (x, y) can be expressed as
A(x,y)＝|y beam-MF (θ (x,y) ,τ (x,y) | (6)
The result of conventional imaging with the receiving array at different positions according to step two is shown in fig. 4a and 4 b. The imaging diagram of FIG. 4 shows that the number of the bright spots appearing in the region of the x-axis range-4 km to-6 km and the y-axis range-4 km to-6 km is consistent with the target number of preset spots, and the positions of the bright spots are corresponding to the positions of the preset spots. Meanwhile, in the acoustic imaging diagrams of fig. 4a and fig. 4b, false features symmetric about the receiving array axis appear, and the appearance positions of the false features change with the position of the receiving array.
Step three: and carrying out pixel point statistical analysis on the current frame acoustic imaging image, adopting a sliding window local peak value to detect and process the acoustic intensity image, and synchronously generating a hot spot statistical image (which is composed of values 0 and 1, is set to be 1 above a local threshold value and is set to be 0 below the local threshold value) consistent with the dimension of the acoustic image matrix. Performing corresponding element point multiplication operation on the original sound image and the hot spot statistical image to obtain a new image of the current frame
B i (x,y)＝A i (x,y).*I i (x,y) (7)
Wherein (x, y) is the pixel coordinates of the acoustic imaging map, B i (x, y) represents a new image obtained by the dot product operation of the ith frame, A i (x, y) represents the original sound image of the ith frame, I i (x, y) represents the hot spot statistical image corresponding to the i-th frame of sound image.
According to the third step, the acoustic image obtained after the pixel statistics processing provided by the invention is shown in fig. 5a and 5 b. Similar to fig. 4, the number of bright spots appearing in fig. 5 is consistent with the number of preset spot targets, the positions are corresponding, the real features are more obvious, the false features are effectively restrained, and the background is clean.
Step four: and (3) repeating the steps 1-3, obtaining acoustic imaging results and hot spot statistical results of the receiving arrays at different positions, performing image stitching on acoustic images of all measuring points by adopting a weighting method, and finally generating submarine topography acoustic images of the observation area.
The process of splicing the acoustic images of all the measuring points is as follows, and the total image after the acoustic images of the previous (i-1) frame are spliced is set as F i-1 (x, y), the new image obtained by the point multiplication operation of the ith frame is B i (x, y), then the total concatenation result F of i frames of acoustic images i (x, y) is expressed as
F i (x,y)＝w 1 (x,y)F i-1 (x,y)+w 2 (x,y)B i (x,y) (8)
Wherein w is 1 (x,y)、w 2 (x, y) is image F i-1 (x,y)、B i Weighting coefficients of (x, y) pixel points, the weighting coefficients being calculated by equation (9)
According to the fourth step, fig. 6 shows the result of stitching the acoustic imaging map obtained by processing the conventional acoustic imaging method, fig. 7 shows the probability map of the statistical distribution of the pixel hot spots obtained by processing the method of the present invention, and fig. 8 shows the result of stitching the acoustic imaging map obtained by processing the method of the present invention. As can be seen from fig. 6 (a) to fig. 6 (c), in the imaging stitching results processed by the conventional acoustic imaging method, a high peak value appears at a preset target point, a side lobe of an imaging result of a real feature is higher, and obvious false features appear. As can be seen from the pixel hotspot statistical distribution probability map shown in FIG. 7, at the preset target position, the pixel hotspot statistical distribution probability reaches 100%, and the false feature is moved by the receiving array position, and the hotspot statistical distribution probability is about 40%. And (3) carrying out statistical analysis on each frame of acoustic image pixel point, adopting the hot spot statistical distribution probability as an acoustic image weighting coefficient, and finally obtaining an acoustic image splicing result as shown in fig. 8, wherein in fig. 8 (a) and (b), imaging results at a preset target point are clear, false features are effectively restrained, and an imaging image background is purer than that of fig. 6 (a).
In order to further verify the effectiveness of the method, 2021, a certain sea test data processing result of south China sea 06 is selected for explanation. The test adopts a transmitting array and a receiving array shown in fig. 1, wherein the transmitting array is a 10-element vertical array, the array element distance is 0.4m, the transmitting signal is a linear frequency modulation signal with the center frequency of 1800Hz, the bandwidth of 200Hz and the pulse width of 1s, the receiving array is a 96-element horizontal towing array, the array element distance is 0.416m, and the sampling frequency is 16kHz.
The test data is processed by selecting 40 frames of continuous data in a certain test line segment, and the imaging result obtained by final processing is shown in fig. 9-11 according to the flow and the steps two-four shown in fig. 2. To contrast the features in the topographical acoustic imaging map, the test area seafloor topography contour maps are superimposed in each of fig. 9-11. Fig. 9 is a graph showing the results of image stitching after processing test data using a conventional acoustic imaging method. FIG. 10 is a probability graph of statistical distribution of pixel hot spots obtained by processing test data according to the method of the present invention. FIG. 11 is a graph showing the results of an imaging splice after processing test data using the method of the present invention.
Comparing FIG. 9 with FIG. 10, it can be seen that a significant sound intensity region corresponding to the submarine topography appears in the range of x-5.5 km-7 km and y-4.6 km-8.6 km in FIG. 9, and the sound intensity distribution range corresponds to the topography trend of the test region; meanwhile, stronger false features appear in the range of-4 km to 5.5km of the x-axis and-6.5 km to-3.6 km of the y-axis. In fig. 11, the high-sound-intensity area corresponding to the topography of the test area is clear and obvious, the sound intensity distribution range corresponds to the topography trend of the test area, false features are effectively restrained, and the whole imaging background is clear and clean. The statistical distribution probability of the pixel hot spots in fig. 10 shows that the statistical distribution probability of the pixel hot spots corresponding to the topography of the test area reaches 90%, the distribution range corresponds to fig. 11, and the distribution probability of the pixel hot spots in the false feature area is about 55%, and the distribution range is affected by the transmitting and receiving positions.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made without departing from the spirit and scope of the invention.
Claims (3)
1. A bistatic submarine topography acoustic imaging method based on pixel statistical distribution weighting is characterized by comprising the following steps:
step 1: modeling the bistatic submarine forward scattering signal;
assuming that the transmitting array is arranged at the central origin of the observation area, the receiving array adopts an N-element horizontal linear array, and the array element distance is d. The transmitted signal adopts a center frequency f 0 The signal bandwidth is a linear frequency modulation signal of B, and the signal pulse width is T; setting p scattering coefficients as sigma on the sea floor p Simulating subsea forward scattering;
the transmitted signal is represented as
Wherein T represents time, T represents signal pulse width, and B represents signal pulse width;
when the receiving array is positioned at a certain measuring line point, the receiving echo on the nth array element is as follows:
wherein n (t) is expressed as noise at the receive array element, σ p Represents the scattering coefficient, τ, of the p-th scattering point p Representing the time delay of propagation through the transmitting array to the p-th scattering point to the receiving array reference element,
denoted as phase delay on the nth receive element, denoted as
Wherein θ p A signal incidence angle indicating a p-th scattering point;
step 2: after receiving the submarine forward scattering signals acquired by the towed line array, processing the received array element signals by adopting a conventional beam forming and matched filtering method to obtain time domain output signals of all beam angles, namely beam angle-time images; processing the beam angle-time image by adopting a conventional time domain bistatic acoustic imaging method to generate a submarine topography acoustic intensity image;
sub-step 1: for x in the previous step n (t) beamforming, the beam output of which can be expressed as
Wherein,,
weighting coefficients for the nth array element;
sub-step 2: carrying out matched filtering processing on the beam output in the sub-step 1, and taking an absolute value to obtain a beam angle-time image
Wherein R is s (t) is the autocorrelation function of the transmitted signal,
representing a convolution;
sub-step 3: the beam angle-time image is processed by acoustic imaging by adopting a conventional time domain bistatic acoustic imaging method, and imaging is obtained by calculating the position of a transmitting array, the position of a receiving array and imaging homing point coordinates (x, y)Return point transmission delay tau (x,y) And an incidence angle theta (x,y) The amplitude A (x, y) at the imaging homing point coordinates (x, y) can be expressed as
A(x,y)＝|y beam-MF (θ (x,y) ,τ (x,y) )| (6)
Step 3: carrying out pixel point statistical analysis on the current frame acoustic imaging image, adopting a sliding window local peak value to detect and process the acoustic intensity image, and synchronously generating a hot spot statistical image consistent with the dimension of an acoustic image matrix, wherein the hot spot statistical image consists of values 0 and 1, the value higher than a local threshold value is set as 1, and the value lower than the local threshold value is set as 0; performing corresponding element point multiplication operation on the original sound image and the hot spot statistical image to obtain a new image of the current frame
B i (x,y)＝A i (x,y).*I i (x,y) (7)
Wherein (x, y) is the pixel coordinates of the acoustic imaging map, B i (x, y) represents a new image obtained by the dot product operation of the ith frame, A i (x, y) represents the original sound image of the ith frame, I i (x, y) represents a hot spot statistical image corresponding to the ith frame of sound image;
step 4: image stitching is carried out on the acoustic images of all the measuring points by adopting a weighting method, and finally, submarine topography acoustic images of the observation area are generated;
the process of splicing the acoustic images of all the measuring points is as follows, and the total image after the acoustic images of the previous (i-1) frame are spliced is set as F i-1 (x, y), the new image obtained by the point multiplication operation of the ith frame is B i (x, y), then the total concatenation result F of i frames of acoustic images i (x, y) is expressed as
F i (x,y)＝w 1 (x,y)F i-1 (x,y)+w 2 (x,y)B i (x,y) (8)
Wherein w is 1 (x,y)、w 2 (x, y) is image F i-1 (x,y)、B i Weighting coefficients of (x, y) pixel points, the weighting coefficients being calculated by equation (9)
2. A computer system, comprising: one or more processors, a computer-readable storage medium storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of claim 1.
3. A computer readable storage medium, characterized by storing computer executable instructions that, when executed, are adapted to implement the method of claim 1.
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