CN111487589B - Target drop point positioning method based on multi-source sensor network - Google Patents
Target drop point positioning method based on multi-source sensor network Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
- G01S5/20—Position of source determined by a plurality of spaced direction-finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a target drop point positioning method based on a multi-source sensor network, which comprises the following steps: installing a sound array and a vibration array; powering on the sound array, the vibration array and the data acquisition equipment, and acquiring signals received by the array in real time; preprocessing a target vibration signal; preprocessing a target sound signal; extracting the number of longitudinal wave envelopes of the target vibration signal; judging the type of the target, and calculating the falling point coordinates of the target; and all the devices are powered off, and the sound array, the vibration array and the data acquisition device are recovered. The invention integrates the advantages of the sound falling point processing mode and the vibration falling point processing mode, accurately estimates the number of the information sources, and can calculate the coordinates of single-target falling points and multi-target continuous falling points and also calculate the coordinates of the falling points projected randomly by multiple targets. Meanwhile, the method is limited by the target time delay difference, and improves the positioning precision.
Description
Technical Field
The invention relates to the technical field of object drop point target positioning and array signal processing, in particular to a target drop point positioning method based on a multi-source sensor network.
Background
Air-to-ground strike training is an important subject for checking the force of the air force, and one of the most important indicators for checking pilot quality is the accuracy of the air-to-ground strike. Therefore, accurate detection and positioning of landing points of the aerial shells are required, which comprises the problems of single-target landing point positioning, multi-target continuous landing point positioning and multi-target random projection landing point positioning. The former two have been studied intensively at home and abroad, and the positioning accuracy is high, so that the accuracy of the meter level can be achieved. However, the multi-target random projection landing point is complex, and for this case, the existing external field detection means and advantages and disadvantages are as follows:
1. manual search: the method comprises the steps of entering a projection area after the task of the whole day is finished, searching all target falling points in the day, and recording the positions of the falling points by using a GPS; this method is inefficient and can be life threatening as the multiple drop points are unexplosive and encounter unexplosive targets.
2. Optical detection: the method is obtained by optical device convergence. Typically including visible light photometry and infrared light photometry; the method has the following defects: (1) For the bullet group, detection failure is caused by shielding of bullet dust on bullet points; (2) The visible light is invalid when the recognition capability is weak and the night is carried out due to the fact that the factors influenced by the environment are relatively large and bad weather such as sand dust occurs; the infrared light angle of vision is very small, receives the influence of surrounding target explosion, very easy omission target.
3. Acoustic detection: the method comprises a one-step positioning method, a first azimuth measurement method and a second positioning method through acoustic detection; the method has the following defects: (1) The method is effective for single targets or multiple targets falling to the ground continuously at a short time interval, but is ineffective for multiple targets scattered randomly, because the aeroelastic group is a plurality of aeroplanes at one time, and the complex conditions of aerial explosion, tiny explosion interval and the like exist. (2) The method is difficult to accurately estimate the number of the information source targets, the prior target number is needed to be provided, but the probability of the unexplosive bullets existing in the bullet group is very high, so that the explosion number cannot be accurately estimated, and the positioning accuracy of the landing points is greatly influenced.
4. Geophone detection: the method comprises the steps of arranging a geophone array and judging the position information of a source by receiving shock waves generated by target explosion. The method has the following defects: (1) The vibration wave velocity is greatly influenced by geological environment, and particularly in sites with uneven geology, the vibration wave velocity can fail; (2) When the multi-target explosion happens, main components of the earthquake waves and transverse wave parts are overlapped, so that the quantity of the information sources is difficult to distinguish, and the problem of the multi-target can not be solved by a single means.
Disclosure of Invention
In order to solve the problems, the invention provides a target landing point positioning method based on a multi-source sensor network, which can accurately estimate the number of information sources, improve the positioning precision of the multi-target landing points and solve the positioning problem of the multi-target random landing points.
In the process of the propagation of the seismic waves from the near to the far, the energy of the seismic waves is transmitted one by one among propagation medium points, so that the propagation process of the seismic waves generated by the landing of the target can be considered as a process of continuous disturbance. In the same propagation medium, the propagation speeds of different waves in the seismic waves generated by the target ground are different, the propagation speed of the P wave (longitudinal wave) is the fastest, the propagation speed of the S wave (transverse wave) is slower, and the propagation speed of the R wave (Rayleigh wave) is the slowest. The wave velocities of the same seismic wave component in different propagation media also have certain difference, and the wave velocity ratio of the longitudinal wave to the transverse wave is calculated according to the formula (1):
in the formula, v p Representing longitudinal wave velocity, v s Representing the transverse wave velocity, u represents the poisson's ratio of the propagation medium.
After the seismic wave propagates a certain distance, the wave components can be separated from each other, the P wave reaches the detection point at the earliest, the S wave is next, and the R wave finally reaches. As the longitudinal wave in the seismic wave carries 7% of energy, the transverse wave energy accounts for 26%, the energy of the Rayleigh wave accounts for about 2/3 of the total energy of the seismic wave, and the R wave component can not be utilized to solve the problem of a plurality of coherent signals, but the longitudinal wave and the transverse wave of the vibration signal have larger wave speed difference, and the longitudinal wave energy accounts for low components of the total energy of the vibration wave, so that the duration of the longitudinal wave in the time domain is short and only 0.15s, and the number of multi-target information sources can be solved by utilizing the number of the longitudinal wave envelopes.
Aiming at the time domain processing of vibration longitudinal waves, the traditional information theory method, the smooth rank method, the matrix decomposition method, the Gauss circle method, the regular correlation method and the like have high requirements on the signal to noise ratio, and the signal to noise ratio is required to be more than 0db, and the effect of 99% can be achieved only when the signal to noise ratio is more than 10db on the engineering.
In addition, although the signals caused by a plurality of coherent sources can be solved by utilizing the sound signals, the estimation criterion of the number of the sources and the targets is calculated to have deviation mostly, and if the estimation of the number of the sources is inaccurate, the accuracy of multi-target drop point positioning is seriously reduced; if the number of the information sources solved by the vibration longitudinal wave is used as a first test, the problem of positioning the landing points of the multi-target random projection can be well solved.
Based on the principle, the invention provides a target drop point positioning method based on a multi-source sensor network, which comprises the following steps:
step S1, installing a sound array and a vibration array;
step S2, powering on the sound array, the vibration array and the data acquisition equipment, and acquiring signals received by the sound array and the vibration array in real time;
s3, preprocessing a target vibration signal according to the signal received by the vibration array;
s4, preprocessing a target sound signal according to the signal received by the sound array;
s5, extracting the number of longitudinal wave packets of the target vibration signal according to the preprocessed target vibration signal;
s6, judging the type of the target according to the longitudinal wave envelope of the target vibration signal, and calculating the falling point coordinates of the target;
and S7, all the devices are powered off, and the sound array, the vibration array and the data acquisition device are recovered.
The sound array and the vibration array are both composed of cross arrays with the number of the cross arrays being more than 3 and the array elements being more than or equal to 8.
The sound array and the vibration array are mounted concentrically.
The step S3 includes:
step S31, determining the starting point Q of the target vibration signal i ；
Step S32, using the target vibration signal start point Q i Taking the target vibration signal as the center, and intercepting the target vibration signal;
and step S33, filtering and denoising the intercepted target vibration signal.
The starting point Q of the target vibration signal is determined i The method of (1) is as follows: detecting each array element signal of the vibration array by adopting a short-time average zero-crossing rate method, a maximum threshold-cross correlation method and an adaptive filtering method, judging that a target vibration signal arrives when the three methods detect that the array element signal contains the target signal, and taking a frame head of a starting frame of the target vibration signal as a starting point Q i 。
The method for intercepting the target vibration signal comprises the following steps: intercepting the starting point Q of the target vibration signal i First 30 seconds and start point Q i Signal data for the last 30 seconds.
The step S5 includes:
step S51, peak detection is performed on the preprocessed target vibration signal to obtain a peak sequence F bl ；
Step S52, calculating F bl Differential sequence F of sequences dbl Finding out the minimum interval d of the target falling points;
step S53, eliminating the differential sequence F dbl The transverse wave and Rayleigh wave components in the sequence F are obtained dblz ；
Step S54, the longitudinal wave sequence F dblz And dividing points smaller than the minimum interval d of the target falling points into a cluster, and dividing M 'clusters, and judging the number of vibration longitudinal wave envelopes to be M'.
The method for peak detection in step S51 is as follows: setting signal window C 1s Averaging M of window signals ym And take the peak value asThen to signal window C 1s The peak value of each data in the database is detected, and the recorded peak value is more than or equal to +.>Is a point of (2).
The longitudinal wave sequence F in the step S53 dblz The extraction method of (2) comprises the following steps: calculating the minimum time difference c of the longitudinal wave and the transverse wave of the vibration signal, and eliminating the differential sequence F dbl The sequence corresponding to the point greater than the minimum time difference c.
And the number of vibration longitudinal wave envelopes in the step S5 is the number of target signals.
When the target type in the step S6 is a single target, calculating a single target falling point coordinate; and when the target type in the step S6 is multi-target, calculating the coordinates of the multi-target falling points.
The single-target falling point coordinate calculation comprises the following steps:
step S611, extracting the time difference of the target vibration signal reaching each array element of the vibration array;
step S612, estimating the propagation speed of the vibration wave of the single target according to the time difference extracted in the step S611;
step S613, performing TDOA location calculation on the single target based on the propagation velocity of the shock wave.
The multi-target drop point coordinate calculation includes:
step S621, estimating azimuth angles of the multi-target signal falling points;
step S622, fusing the azimuth angles by using a joint probability density function to obtain coordinates of the target landing points;
in step S623, the extreme point position in the space is searched to obtain the coordinates of the multi-target landing point.
According to the invention, the sound drop point processing mode and the vibration drop point processing mode are combined, vibration signals generated by target explosion are analyzed, and the vibration longitudinal wave envelope number is extracted, so that the information source number is accurately estimated. The invention can calculate the coordinates of single-target landing points and multi-target continuous landing points, and can calculate the landing point coordinates of multi-target random projection. Meanwhile, the method is limited by the target time delay difference, and improves the positioning precision.
Drawings
Fig. 1 is a flow chart of a method for locating a target drop point based on a multi-source sensor network according to the present invention.
Fig. 2 is a schematic diagram of an acoustic shock array according to an embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention is given with reference to the accompanying drawings, so that the function and features of the present invention can be better understood.
The target drop point positioning method based on the multi-source sensor network, as shown in fig. 1, comprises the following steps:
step S1, installing a sound array and a vibration array. The sound array and the vibration array are both composed of cross arrays with the number of the cross arrays being more than 3 and the number of the array elements being more than or equal to 8. The number of the arrays is more than 3, so that a field can be surrounded; the number of the array elements is required to be larger than that of the information sources, and an array of 8, 12, 16, 20, 24, 28 or 32 array elements can be selected; the cross array can estimate the propagation velocity of the waveform and is easy to install. In addition, because the sound signals are used for multi-target calculation, the vibration signals are used for information source estimation, and in order to ensure that the sound vibration signals are in the same geographic position, the sound array and the vibration array are concentrically arranged, and meanwhile, the arrangement mode is convenient for equipment installation and recovery. In this embodiment, the array arrangement is shown in fig. 2, the sound array is composed of 4 24-element cross arrays, the vibration array is composed of 4 8-element cross arrays, the arrays are all installed in the safety outside the target area, and the 4 arrays surround the projection area in geographic positions.
And S2, powering up the sound array, the vibration array and the data acquisition equipment, and acquiring signals received by the sound array and the vibration array in real time.
Step S3, preprocessing a target vibration signal according to a signal received by the vibration array, wherein the step comprises the following steps:
step S31, determining the starting point Q of the target vibration signal i . The step is to detect whether the target vibration signal reaches the vibration array, i.e. whether the array element signals in the vibration array contain the target vibration signal. Whether 100% of the detection of each targeting data (shock wave generated by the landing explosion of the target) is completely leakless is a precondition for calculating the coordinates of the landing point of the target.
The method comprises the following steps: detecting each array element signal of the vibration array by adopting a short-time average zero-crossing rate method, a maximum threshold-cross correlation method and an adaptive filtering method, judging that the target vibration signal arrives if the three methods detect that the array element signal contains the target signal, and taking the frame head of the initial frame of the target vibration signal as a starting point Q i 。
The short-time average zero-crossing rate refers to the number of times that the amplitude of a signal in a unit time changes from positive to negative, and the frequency conversion of the signal can be roughly judged. Firstly, framing real-time array element signals, and then calculating the zero-crossing rate of data points of each frame according to a formula (2):
where N represents the total frame number, N represents the number of sampling points of one frame of data, in this embodiment, 200 is taken, x (m) represents the signal voltage value of the mth point, and sgn [ ] is a sign function.
Since the explosion caused by the target is a low-frequency signal and the short-time average zero-crossing rate is very high and can reach more than 8, the occasional noise signal can be reduced to about 5, so that the short-time average zero-crossing rate can be greatly reduced when the target vibration signal arrives. At the position ofIn this embodiment, when the target vibration signal arrives, a short-time average zero-crossing rate of 0 occurs for 10 consecutive frames, so that in the application scenario, when the short-time average zero-crossing rate is 0, the arrival of the target vibration signal can be determined, and a frame with the first short-time average zero-crossing rate of 0 is taken as a target vibration signal start frame, and the frame head of the start frame is taken as a target vibration signal start point Q i 。
The maximum threshold-cross correlation method is: real-time array element signals are processed by sliding window, and the signal window is set as C 1 Taking the signal peak value of the history experience as the maximum threshold value M of the signal; then a AND signal window C is set 1 Rectangular window C of the same length 2 Setting the amplitude value of the signal as a maximum threshold value M; finally to C 1 And C 2 Taking the correlation, the correlation coefficient is calculated according to the formula (2):
if the correlation coefficient ρ>0.5, then represents the signal window C 1 The vibration signal containing target explosion is taken as a target signal window, the arrival of the target vibration signal is judged, and the frame head of the initial frame of the target vibration signal is taken as a starting point Q i 。
The self-adaptive filtering method comprises the following steps: a) The real-time array element signals are subjected to framing treatment, and the frame length is generally 10ms-50ms. In this embodiment, the frame length is 10ms, the sampling rate is 20k/s, and then one frame is 200 sampling points. b) The amplitude (voltage value) of each frame of data is weighted and averaged to obtain the average value m i . c) Taking the mean value (m) 1 ,m 2 ,m 3 ,,,m 20 ) And calculates the mean value m of the 20 frame data point means z Sum of variances sigma z Then take the threshold y=m z +0.8*σ z As a minimum threshold for signal detection. Taking 20 frames of data is because 10 frames of data are too little to be trusted, while 30 frames of data are too much, which can lead to slow calculation, and other values, such as 21 frames, 22 frames, etc., can be taken according to actual situations and requirements. d) Comparing the average value m of each frame after 20 frames i Whether or not greater than y, when the signal frame is 5 frames in successionWhen the frame head of the initial frame of the target vibration signal is taken as a starting point Q i . The 5 frames are taken for judgment, which is obtained by combining the real explosion data, and other values can be taken for judging waveforms generated by different bullets.
And finally comparing whether the results obtained by the three methods are consistent, and judging that the target vibration signals arrive when the results are consistent, wherein the starting point of the target vibration signals received by each vibration array is extracted according to the method.
Step S32, using the target vibration signal start point Q i And taking the target vibration signal as the center, and intercepting the target vibration signal. In step S31, a start point Q of the target vibration signal is determined i The starting point Q i An approximate point in time when the target shock signal reaches the shock array is determined. In step S31, the number of sampling points is 200, the obtained initial frame is 200 sampling points, Q i The frame head is the 0 th sampling point, and the real starting point of the target vibration signal is between the 0 th sampling point and the 200 th sampling point. Thus Q i The deviation from the true starting point is about 100 sample points. As can be seen from comparison of the historical data, the maximum time for the single target vibration signal to continue (the target vibration signal reaches until the target vibration signal disappears) is 3s, and the maximum time for the multi-target vibration signal to continue is 5 seconds, so that the maximum time delay of the target vibration signal is less than 10 seconds. Thus, in order to analyze the target vibration signal and prevent erroneous judgment, the starting point Q of the target vibration signal is intercepted i First 30 seconds and start point Q i For the last 30 seconds, 60 seconds of vibration signal data are used for the next processing.
And step S33, filtering and denoising the intercepted target vibration signal. The original data collected by the vibration array not only contains the target original signal, but also contains environmental noise and noise generated by the influence of the equipment on the target signal, so that the intercepted target vibration signal needs to be subjected to smoothing treatment before calculation. In order to facilitate data processing for the subsequent calculation, the intercepted target vibration signals are subjected to mean value removal and normalization, and the target vibration signal filtering noise reduction received by each vibration array is processed according to the method.
And S4, preprocessing the target sound signal according to the signal received by the sound array. The processing of the sound signal is to perform noise reduction processing on the original signal containing noise, obtain data with better signal to noise ratio for subsequent calculation, and the processing process is consistent with the preprocessing process of the vibration signal, which is not described herein.
And S5, extracting the number of longitudinal wave envelopes of the target vibration signal according to the preprocessed target vibration signal by utilizing the characteristic that the longitudinal wave energy is low and the speed is faster than that of the transverse wave, so as to estimate the number of the information sources. Comprising the following steps:
step S51, peak detection is performed on the preprocessed target vibration signal to obtain a peak sequence F bl . The method comprises the following steps: taking the signal window C of the first signal frame larger than the threshold y 1s Averaging M of window signals ym And take the peak value asThen to signal window C 1s Peak detection is carried out on each data in the table, and the recorded peak value is greater than or equal to +.>If L points are assumed, it is denoted as F bl ＝[b 1 ,b 2 ,Kb l ],l＝1,2,KL。
Step S52, calculating F bl Differential sequence F of sequences dbl Finding out the minimum interval d of the target falling point by an abnormal point inspection method;
step S53, eliminating the differential sequence F dbl The transverse wave and Rayleigh wave components in (i) F dbl B corresponding to a point greater than the minimum time difference c of the longitudinal wave and the transverse wave c [ b ] c ,b c+1 ,b c+2 ,Kb l ]Removing to obtain longitudinal wave sequence F dblz . On the vibration waveform generated by the target landing explosion, a section of region with particularly low amplitude is arranged between transverse and longitudinal waves, and the region is the minimum time difference c. Under the condition that the transverse wave and longitudinal wave speeds are known, the time difference c is also calculated by the formula:d' is the true distance of the target landing point from the vibration array, v 1 For transverse wave velocity, v 2 Is the longitudinal wave velocity.
Step S54, the longitudinal wave sequence F dblz The points smaller than the minimum interval d of the target falling points are divided into a cluster, and if M 'clusters are divided, the number of the longitudinal wave envelopes can be judged to be M'. Let it be assumed that H 1 A vibration array, each array calculating the number of longitudinal wave envelopes M' h ，h＝1,2KH 1 To prevent the occurrence of overlapping of longitudinal waves in a single propagation direction, H is used 1 The vibration arrays surround the target projection area, and the maximum M 'is taken' h The value is taken as the number of longitudinal wave envelopes, i.e. the number of sources.
The signal source number estimation method can distinguish the signal sources when the signal to noise ratio is low, and has no signal to noise ratio limit. Even if the noise is large, when the target signal is on the fly, there can be a high effect due to the low frequency phenomenon of the shock wave. Through experiments, when the explosion time difference between targets is larger than 40ms, the method can be used for obtaining the number of the information sources.
Step S6, judging whether the target is a single target or a multi-target: when the longitudinal wave envelope of the target vibration signal is 1, judging that the target vibration signal is a single target, and performing step S610; if the longitudinal wave envelope of the target vibration signal is greater than 1, it is determined as multiple targets, and step S620 is performed.
In step S610, the coordinates of the falling point of the single object are calculated. Comprising the following steps:
in step S611, the time difference between the arrival of the target vibration signal at each array element of the vibration array is extracted. The time difference can reflect the azimuth information of the target landing point, and the method adopts a generalized cross-correlation method to calculate: firstly, performing average value removal processing on two array element signals, and enabling the center of the signal received by each vibration sensor to be on the x axis; secondly, calculating cross correlation coefficients TEMP of two array element signal sequences, and finding out the corresponding signal sequence position Max when the cross correlation coefficients are maximum; finally, calculating the time difference tdoa through a formula (4):
in the formula, l' represents the length of an array element signal, and fs represents the sampling rate set by the acquisition equipment when the array signal is acquired.
Step S612, estimating the propagation speed of the shock wave of the single target. In the present embodiment, the geometrical structure of the cross array is used to set the time difference between the four vibration sensors in the transverse direction to be τ x1,x2 ,τ x2,x3 ,τ x3,x4 The average array element time difference tau of the transverse vibration sensor x The method comprises the following steps:
similarly, let the time difference between four vibration sensors in the longitudinal direction be τ y1,y2 ,τ y2,y3 ,τ y3,y4 Average array element time difference tau of longitudinal vibration sensor y The method comprises the following steps:
from the collective relationship of the cross arrays, the following formula can be obtained:
wherein L' is the distance between each array element of the vibration array, θ is the azimuth angle of the target reaching the vibration array, v is the propagation speed of the target falling point reaching the vibration array, and each array is pre-measured according to the method. The calculation of the propagation speed of the vibration wave is simple, and equipment is not needed for obtaining the speed of the vibration wave.
In step S613, TDOA location calculation is performed based on the vibration wave propagation velocity measured in advance.
Assuming a common H 1 ' element vibration sensor, i-th element vibration sensor receives target signal r at k-th moment i (k) Can be expressed as:
r i (k)＝s(k-D i )+q i (k),i＝1,2,3KH 1 ' (8)
wherein D is i Representing the time from the generation of the shock signal until the arrival of the target at the array elements of the array, s (k-D i ) Indicating that the ith vibration sensor receives a real target signal without noise at the kth moment, q i (k) Representing additive noise.
Then the distance d from the ith vibration sensor to the 1 st sensor i，1 The method comprises the following steps:
where v denotes the propagation velocity of the target drop point to the vibrating array, D i，1 Representing the time difference from the ith vibration sensor to the 1 st vibration sensor, (x) i ,y i ) The coordinates of the i-th vibration sensor are expressed, and (x, y) indicates that the coordinates of the falling point of the unknown object are (x, y). From equation (8), it can be seen that by using the known v, the time difference D of the signals i,1 Array element coordinates (x i ,y i ) The final target drop point coordinates (x, y) are obtained.
The position of the landing point can be obtained by using the equation in the traditional small-caliber array, but for the mode of combining a plurality of small arrays in the invention, the speed of a target signal reaching different vibration arrays is inconsistent due to the influence of geological factors in a field region.
Thus defining the propagation velocity of the target drop point to the h vibration array as v h H= 1,2,3KH, H being the total number of shock arrays. Then equation (9) is changed to equation (10) and equation (11):
wherein d ih,11 Ith to ith sensor representing the h th vibration arrayDistance difference of 1 st element vibration sensor of 1 vibration array, D ih,11 Representing the time difference between the ith sensor of the h-th vibration array and the 1 st vibration sensor of the 1 st vibration array, (x) 11 ,y 11 ) Coordinates of 1 st element vibration sensor representing 1 st vibration array, (x) ih ,y ih ) Representing the coordinates of the ith transducer of the h-th shock array.
The product of the term transfer and the similar term combination is obtained by the formula (11):
in the method, in the process of the invention,
then if equation (11) is written in the form of a matrix, then there are:
Aθ＝B， (14)
θ＝|x y u p| T (16)
in equation (11), the target signal contains additive noise, thus redefining the time difference D ih,11 The method comprises the following steps:
in the method, in the process of the invention,represents a true value without noise, n ih,11 Representing the estimated error of the arrival time.
Based on the formula(15) To (17), estimating θ by standard least square method, using symbolsRepresenting the calculated result, the following formula:
establishing an error vector equation for the matrix form of equation (12):
in the method, in the process of the invention,indicating that no noise is present, the result of the settlement of equation (12), i.e.)>But the result of the calculation is erroneous due to noise in the system. Assuming that the error vector ε approximately obeys a Gaussian distribution, its covariance matrix ψ is expressed as:
wherein E [ ] represents a mathematical expectation,
q is time difference noise n subject to Gaussian distribution ih,11 Is a covariance matrix of (a).
Assuming that both the signal and noise are white noise and that the channels are independent of each other, Q is proportional to the diagonal matrix, thus bringing equation (21) into equation (19) yields the weighted least squares estimation target drop point result:
the result obtained in the formula (22) is a first target falling point coordinate estimated value, and an error equation is reconstructed and second estimation is performed by using the first estimated value and the relation between u and p in the formula (13):
in the method, in the process of the invention,representing the true value of the target drop point x, e 1 Representing error value, ++>Representation->Is a first parameter x of (a); />Representing the true value of the target drop point y, e 2 Representing error value, ++>Representation->A second parameter y of (2); />Representing the true value of u, e 3 Representing error value, ++>Representation->A third parameter u of (2); />Representing the true value of p, e 4 Representing error value, ++>Representation->Is a fourth parameter p.
Further, there are:
equation (24) is a standard form of the least squares method, where ln () represents a logarithmic calculation, [] T Representing the transpose, ψ, of the matrix 1 The calculation method of (a) is similar to the first estimation (reference formula (21)).
The final coordinates of the single target drop point can be finally obtained by the formula (24):
wherein, x and y are calculated coordinates of the falling point,representing the second estimate +.>Is>Representing the second estimate +.>(x) 1 ,y 1 ) Representing the coordinates of the first meta-sensor in the vibrating array.
In step S620, the coordinates of the landing points of the multiple targets are calculated. Comprising the following steps:
in step S621, the azimuth of the multi-target signal drop point is estimated. The invention adopts a broadband music algorithm based on coherent signals, and the explosion signal generated by the target is a broadband sound signal, so that the coherence and the frequency processing among the array element signals of the sound array are needed to be considered when the solution is carried out, and the invention has the following steps:
a) An acoustic signal model is built and an initial CBF process (conventional wave velocity formation algorithm, i.e., narrowband DOA algorithm) is performed:
establishing a broadband signal model, and assuming that the bandwidth of the sound signal is B and the number of sound arrays is H 2 With H 2 The 'element sound sensor' receives the following signals at the time t:
wherein τ h'i And the time delay between array elements is represented, s (t), n (t), and x (t) are the real signal, the noise signal and the received signal of the h' element sound sensor respectively.
The signal is transformed from the time domain to frequency to observe the frequency transformation of the signal. The whole wide frequency band is divided into J narrow frequency bands, and DFT conversion is carried out on the formula (26):
X h' (f j )＝A θ (f j )S h' (f j )+N(f j ),j＝1,2,KJ，h'＝1,2,3KH 2 '； (27)
wherein A is θ (f j ) Is an MxN direction matrix, where S (f j )，N(f j )，X h' (f j ) The real signal, the noise signal and the received signal of the h' array element corresponding to the j frequency band are respectively represented.
For the broadband signal model shown in formula (27), the array flow pattern matrix should be:
wherein A (f) j ) The representation direction matrix is the set of the information source directions corresponding to the jth frequency band, M' represents the target number, a m' (f) Representing the orientation of the mth target relative to the array, where τ km' Representing the time delay between the array elements.
B) Selecting a focusing matrix, and transforming the data on different frequency points to the same reference frequency point, wherein the expression of the focusing matrix is as follows:
T j ＝CQ 0 Q j H ； (29)
wherein C=I, represents an identity matrix, Q 0 Data covariance matrix P after denoising for reference frequency points 0 Feature vector, Q j Denoising the jth data frequency point to obtain a data covariance matrix P j Is described.
Focusing the frequency band to a single frequency point through a focusing matrix;
A(f 0 )＝T(f j )A(f j ) (30)
c) The method comprises the following steps of:
c1 From array data x (t) i ) To estimate the correlation matrix of the matrix:
where M is the length of the data sequence and R is the data x (t i ) And (5) a correlation matrix.
C2 Performing characteristic decomposition on R to obtain a characteristic value vector;
c3 Using the target number M' calculated in step S54, using P large eigenvalue vectors to construct a signal subspaceOr N-P small eigenvalues corresponding to eigenvectors to form a noise subspace +.>
C4 A) using search vector a (θ) to noise subspaceAnd (3) projection:
in p mu (θ) represents the projected 1-360 degree corresponding calculation result, a (θ) represents the search vector, v i Representing the feature vectors corresponding to the N-P small feature values.
C5 Calculating a spectrum peak to obtain the azimuth of the falling point of the target signal:
each sound array calculates the target drop point azimuth according to the above algorithm.
Step S622, fusion is performed on azimuth angles obtained by the plurality of sound arrays by using the joint probability density function to obtain coordinates of each target landing point, wherein the formula is as follows:
equation (34) is a standard form of probability density function, where θ 0 Representing variance, P m' And (θ) represents the azimuth of the mth source.
In step S623, the coordinates of the multi-target falling point can be obtained by searching the extreme point position in the space.
And S7, all the devices are powered off, and the sound array, the vibration array and the data acquisition device are recovered.
The invention provides a target falling point positioning calculation method based on a multi-source sensor network, which is provided from the angles of acoustic and vibrology application, and solves the problem of multi-target random projection falling point positioning which cannot be solved by single acoustic. The invention is limited by the time of the interval of multiple landing points to be smaller, is improved by 30 times compared with the prior minimum interval of 1.5 seconds, uses longitudinal waves to distinguish the number of target landing points, has the accuracy rate of 99.9 percent, and realizes the positioning accuracy within the range of 1Km to 1Km and within 5 meters. Therefore, the invention has more detectable drop point types, can solve the problem of positioning the drop points of multi-target random projection with denser and smaller interval, and has been successfully tested in actual outfield and used.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and various modifications can be made to the above-described embodiment of the present invention. All simple, equivalent changes and modifications made in accordance with the claims and the specification of the present application fall within the scope of the patent claims. The present invention is not described in detail in the conventional art.
Claims (11)
1. The target drop point positioning method based on the multi-source sensor network is characterized by comprising the following steps of:
step S1, installing a sound array and a vibration array;
step S2, powering on the sound array, the vibration array and the data acquisition equipment, and acquiring signals received by the sound array and the vibration array in real time;
s3, preprocessing a target vibration signal according to the signal received by the vibration array;
s4, preprocessing a target sound signal according to the signal received by the sound array;
s5, extracting the number of longitudinal wave envelopes of the target vibration signal according to the preprocessed target vibration signal;
s6, judging the target type according to the longitudinal wave envelope of the target vibration signal, and calculating single-target falling point coordinates when the target type is a single target; when the target type is multi-target, calculating multi-target falling point coordinates;
the single-target falling point coordinate calculation comprises the following steps:
step S611, extracting the time difference of the target vibration signal reaching each array element of the vibration array;
step S612, estimating the propagation speed of the vibration wave of the single target according to the time difference extracted in the step S611;
step S613, TDOA positioning calculation is carried out on the single target based on the propagation speed of the vibration wave;
the multi-target drop point coordinate calculation includes:
step S621, estimating azimuth angles of the landing points of the multiple target signals according to the preprocessed target sound signals;
step S622, fusing the azimuth angles by using a joint probability density function to obtain coordinates of the target landing points;
step S623, searching the extreme point position in the space to obtain the coordinates of the multi-target falling point;
and S7, all the devices are powered off, and the sound array, the vibration array and the data acquisition device are recovered.
2. The method for positioning a target landing point based on a multi-source sensor network according to claim 1, wherein the sound array and the vibration array are each composed of a cross array with a number of more than 3 and an array element of more than or equal to 8.
3. The method for locating a target landing point based on a multi-source sensor network according to claim 1, wherein the sound array and the vibration array are installed concentrically.
4. The method for positioning the target landing point based on the multi-source sensor network according to claim 1, wherein the step S3 comprises:
step S31, determining the starting point Q of the target vibration signal i ；
Step S32, using the target vibration signal start point Q i Taking the target vibration signal as the center, and intercepting the target vibration signal;
and step S33, filtering and denoising the intercepted target vibration signal.
5. The method for locating a target landing point based on a multi-source sensor network as claimed in claim 4, wherein the determining the starting point Q of the target vibration signal i The method of (1) is as follows: the short-time average zero-crossing rate method, the maximum threshold-cross correlation method and the self-adaptive filtering method are adopted for each array element signal of the vibration arrayDetecting, when all the three methods detect that the array element signal contains the target signal, judging that the target vibration signal arrives, and taking the frame head of the initial frame of the target vibration signal as a starting point Q i 。
6. The method for positioning a target landing point based on a multi-source sensor network according to claim 4, wherein the method for intercepting the target vibration signal is as follows: intercepting the starting point Q of the target vibration signal i First 30 seconds and start point Q i Signal data for the last 30 seconds.
7. The method for positioning the target landing point based on the multi-source sensor network according to claim 1, wherein the step S5 comprises:
step S51, peak detection is performed on the preprocessed target vibration signal to obtain a peak sequence F bl ；
Step S52, calculating F bl Differential sequence F of sequences dbl Finding out the minimum interval d of the target falling points;
step S53, eliminating the differential sequence F dbl The transverse wave and Rayleigh wave components in the sequence F are obtained dblz ；
Step S54, the longitudinal wave sequence F dblz And dividing points smaller than the minimum interval d of the target falling points into a cluster, and dividing M 'clusters, and judging the number of vibration longitudinal wave envelopes to be M'.
8. The method for positioning the target landing point based on the multi-source sensor network according to claim 7, wherein the method for detecting the peak value in step S51 is as follows: setting signal window C 1s Averaging M of window signals ym And take the peak value asThen to signal window C 1s The peak value of each data in the database is detected, and the recorded peak value is more than or equal to +.>Is a point of (2).
9. The method for positioning a target landing point based on a multi-source sensor network according to claim 7, wherein the longitudinal wave sequence F in step S53 dblz The extraction method of (2) comprises the following steps: calculating the minimum time difference c of the longitudinal wave and the transverse wave of the vibration signal, and eliminating the differential sequence F dbl The sequence corresponding to the point greater than the minimum time difference c.
10. The method for positioning a target landing point based on a multi-source sensor network according to claim 1, wherein the number of vibration longitudinal wave envelopes in step S5 is the number of target signals.
11. The method for positioning the target drop points based on the multi-source sensor network according to claim 1, wherein when the target type in the step S6 is a single target, single-target drop point coordinate calculation is performed; and when the target type in the step S6 is multi-target, calculating the coordinates of the multi-target falling points.
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