Gunshot direction finding method based on rising edge time of arrival estimation in multipath propagation environment

Abstract

The application discloses a gunshot direction-finding method based on rising edge wave arrival time estimation in a multipath propagation environment, which comprises the following steps: filtering microphone array data received in a multipath environment, and direction-finding on a gunshot trajectory shock wave signal and a muzzle wave signal in two frequency bands of high frequency and low frequency; detecting a rising edge of a pulse waveform of a gunshot signal pulse to obtain wave arrival time estimation of a starting rising edge of a gunshot signal direct wave of each array element; calculating a difference of the wave arrival time of the gunshot signal direct wave of each array element relative to a reference array element to obtain a microphone array wave arrival time difference vector; and solving a direction vector of the gunshot signal direct wave by using a least square method according to the microphone array wave arrival time difference vector and an array pattern coefficient matrix, and then obtaining angle estimation of the direction of the gunshot signal direct wave. The application greatly improves the anti-multipath interference capability of the wave arrival time difference estimation method in gunshot direction-finding calculation, and improves the gunshot sound source positioning accuracy in a multipath environment.

Description

Technical Field

[0001] This invention belongs to the field of gunshot direction finding and acoustic signal processing technology, and in particular, it is a gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment. Background Technology

[0002] Among various detection methods, gunshot localization technology based on acoustic principles has become an important means of enemy detection and localization due to its low power consumption, ease of operation, all-space detection capability, strong anti-interference performance, and superior performance under non-line-of-sight conditions. With the evolution of modern military strategy and tactics, the battlefield environment is undergoing profound changes. In addition to traditional plains warfare, urban street fighting and mountain warfare are gradually becoming important forms of modern warfare. In these complex combat environments, the towering buildings, intricate alleyways, and rugged mountain terrain severely affect the propagation direction, energy distribution, and spectral characteristics of acoustic signals. In these multipath environments, reflected wave signals travel along the propagation path to the microphone array. The final received signal is no longer a simple transformation of the direct signal after propagation attenuation, but a complex waveform formed by the superposition of the direct and reflected wave signals. The superimposed gunshot signal exhibits severe distortion in the time domain waveform. This distortion alters the time domain waveform and time-spectral characteristics of the original gunshot signal, thus affecting the accuracy of gunshot source localization and increasing the difficulty of accurately perceiving the enemy situation. Against this backdrop, acoustic signal processing in complex combat environments faces severe challenges. Accurate analysis and positioning of enemy targets, especially weapons of mass destruction, has become one of the key technologies that determine victory or defeat on the modern battlefield.

[0003] Among the many current technologies, estimating the angle between the target and the reference direction of the receiving array using the Time Difference of Arrival (TDOA) of the signal arriving at the microphone array is one of the commonly used methods. Microphone arrays, with their low cost and minimal electromagnetic radiation, provide a passive detection method and perform well in environments with high electromagnetic compatibility requirements. TDOA technology uses multiple microphones to measure the time difference of arrival of the same signal, thereby deducing the direction or location of the target signal source. Currently, most gunshot signal direction finding methods based on TDOA estimation have high positioning accuracy in ideal propagation environments. However, in multipath environments, due to the aliasing of reflected wave signals, the time-domain waveform and spectral energy distribution of the direct wave signal change significantly, resulting in a noticeable decrease in the effectiveness of conventional TDOA-based direction finding methods, making it difficult to provide accurate sound source localization. Early rising edge detection methods typically employed zero-crossing detection, estimating the TOA of the signal by recording the time of the zero-crossing sampling points. This method performed relatively stably in low signal-to-noise ratio environments, but the source localization error remained significant in multipath reverberation environments (Zeng Xiangyang, Cai Huaizhen. Indoor dual-microphone source localization based on rising zero-crossing detection [J]. Electroacoustic Technology, 2014(2):26-30.). To address this issue and enable the localization method based on TDOA estimation to be effectively applied in complex multipath environments such as cities and mountainous areas. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by providing a gunshot direction finding method based on rise edge time of arrival (TDOA) estimation for muzzle wave and ballistic shock wave signals in multipath propagation environments for small, variable-number array elements. A method for locating gunshot sources based on dual-threshold rise edge detection is proposed for gunshot detection systems with small, variable-number array elements. This method combines noise and peak thresholds to more accurately estimate the TDOA of the signal, thereby significantly enhancing robustness to multipath effects and effectively improving the accuracy of source direction finding in complex multipath environments.

[0005] The technical solution to achieve the objective of this invention is as follows: On one hand, a gunshot direction finding method based on rising edge time of arrival estimation is provided in a multipath propagation environment, the method comprising the following steps:

[0006] Step 1: In a multipath propagation environment, use a microphone array to collect gunshot signals. For each microphone array element channel, extract the muzzle wave signal pulse and the ballistic shock wave signal pulse of the gunshot.

[0007] Step 2: For each signal pulse extracted in Step 1, the TOA of the gunshot signal directly reaching the rising edge is obtained by detecting the rising edge of the pulse waveform in each microphone array element channel.

[0008] Step 3: Using the TOA of the rising edge of the direct arrival wave of the gunshot signal in all microphone array element channels obtained in Step 2, calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0009] Step 4: Based on the time difference of arrival (TDOA) of the direct wave of the gunshot signal relative to the reference element obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the least squares method.

[0010] Step 5: Calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0011] On the other hand, a gunshot direction finding system based on rise-edge time-of-arrival estimation is provided in a multipath propagation environment, the system comprising:

[0012] The first module is used to acquire gunshot signals using a microphone array, and to extract the muzzle wave signal pulse and the ballistic shock wave signal pulse for each microphone array element channel.

[0013] The second module is used to detect the rising edge of each signal pulse extracted by the first module and obtain the TOA of the gunshot signal in all microphone array channels by detecting the rising edge of the pulse waveform.

[0014] The third module is used to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal in all microphone array element channels obtained from the second module, and to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0015] The fourth module is used to calculate the direction vector s of the direct wave of the gunshot signal based on the time difference of arrival (TDOA) of each microphone array element relative to the reference array element obtained in the third module, using the least squares method.

[0016] The fifth module is used to calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0017] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment.

[0018] On the other hand, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment.

[0019] Compared with the prior art, the significant advantages of this invention are:

[0020] 1) A new strategy for effective gunshot signal direction finding in multipath propagation environments is proposed, namely, a gunshot signal direction finding method based on dual-threshold rising edge TDOA estimation. This method provides a more accurate estimation of the signal TDOA, thereby significantly enhancing the robustness to multipath effects and effectively improving the accuracy of sound source direction finding in complex multipath environments.

[0021] 2) By optimizing signal processing and multipath interference identification methods, the system's robustness in dynamic environments has been enhanced. Even in complex environments with strong reflections or scattering, it can still stably provide high-precision positioning results.

[0022] 3) The structure is simple. While improving the anti-multipath capability, it also ensures the real-time performance and computational efficiency of the algorithm, providing a reliable and robust solution for the efficient analysis and processing of large-scale field measured gunshot data.

[0023] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention.

[0025] Figure 2 This is a schematic diagram of the direction finding of a stereo array sound source in a small gunshot detection system in one embodiment, and a qualitative analysis diagram of the direction vector s in each quadrant. Figure 2 (a) in the diagram is a schematic diagram of the direction finding of a stereo array sound source in a small gunshot detection system. Figure 2 (b) in the figure is a qualitative analysis diagram of the direction vector s in each quadrant.

[0026] Figure 3 This is a time-domain waveform diagram of gunshot signal in an ideal environment and a time-domain waveform diagram of gunshot signal propagating through multipath propagation in one embodiment. Figure 3 (a) and (b) in the figure are the time-domain waveform and time-spectrum diagram of the measured muzzle wave signal of gunshot under multipath propagation environment, respectively.

[0027] Figure 4 This is a schematic diagram of gunshot signal rising edge detection in one embodiment. The gunshot signal TDOA is obtained based on the noise threshold and the peak value threshold. Figure 4 (a) and (b) in the figure are schematic diagrams of gunshot signal rising edge detection for the reference array element and other microphone array elements, respectively.

[0028] Figure 5 The study demonstrates the error between the method for estimating gunshot signal incident from different incident angles using rising edge detection and the actual angle in an environment with a signal-to-noise ratio of 10dB. The calculated average angle error is less than 5°. Detailed Implementation

[0029] This invention addresses the acoustic localization requirements by proposing an effective gunshot direction finding method in multipath propagation environments: a gunshot direction finding method based on time-of-arrival estimation of the rising edge in the time domain. The proposed method exhibits high localization accuracy for gunshot signals in multipath environments. Therefore, this invention enhances the system's robustness in dynamic environments, consistently providing high-precision localization results even in complex environments with strong reflections or scattering. This provides a high-speed, efficient, and reliable new approach and tool for sound source localization in complex multipath environments.

[0030] In one embodiment, combined Figure 1 This paper provides a gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment. The method includes the following steps:

[0031] Step 1: In a multipath propagation environment, use a microphone array to collect gunshot signals. For each microphone array element channel, extract the muzzle wave signal pulse and the ballistic shock wave signal pulse of the gunshot.

[0032] Step 2: For each signal pulse extracted in Step 1, the TOA of the gunshot signal directly reaching the rising edge is obtained by detecting the rising edge of the pulse waveform in each microphone array element channel.

[0033] Step 3: Using the TOA of the rising edge of the direct arrival wave of the gunshot signal in all microphone array element channels obtained in Step 2, calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0034] Step 4: Based on the time difference of arrival (TDOA) of the direct wave of the gunshot signal relative to the reference element obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the least squares method.

[0035] Step 5: Calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0036] Furthermore, in one embodiment, the microphone array in step 1 is a planar array consisting of no less than 3 microphones or a three-dimensional array consisting of no less than 4 microphones, and all microphone array element channels synchronously acquire gunshot signals and convert them into digital array signals.

[0037] Furthermore, in one embodiment, step 1, which involves extracting the muzzle wave signal pulse and the ballistic shock wave signal pulse for each microphone array element channel, specifically includes:

[0038] For each microphone array element channel, through a cutoff frequency of f lThe muzzle wave signal processing branch is obtained by using a low-pass filter, and then the muzzle wave signal pulse is extracted by a muzzle wave identification method based on multi-scale sub-band energy collection characteristics.

[0039] For each microphone array element channel, a cutoff frequency of f is used. h The high-pass filter is used to obtain the ballistic shock wave signal processing branch, and then the gunshot ballistic shock wave signal pulse is extracted by the ballistic shock wave identification method based on singular value decomposition.

[0040] Here, in some embodiments, the muzzle wave identification method based on multi-scale sub-band energy set characteristics specifically includes:

[0041] (1) Segment the continuous audio data based on the Gaussian mixture model and extract audio segments across the entire frequency band;

[0042] (2) The full-frequency band acoustic segment obtained in (1) is filtered by a multi-scale sub-band filter bank to obtain a sub-band acoustic segment;

[0043] (3) Align the starting points of the full-band acoustic segment extracted in (1) and the filtered sub-band acoustic segment in (2); specifically including:

[0044] (3.1) The sub-band audio segments after being filtered by the filter with a center frequency of 300Hz obtained in (2) are divided into equal-interval frames;

[0045] (3.2) According to the formula Calculate the energy of each segment, find the maximum energy value max(En) and the minimum energy value min(En), where En is the energy of the nth sub-segment with sound, N is the length of each sub-segment with sound, and xi is the value of the i-th point in each sub-segment with sound.

[0046] (3.3) Based on the maximum energy value max(En) and minimum energy value min(En) obtained in (3.2), an adaptive threshold is set. When the energy of three consecutive signal segments exceeds the threshold Threshold, the first segment of these three segments is marked as the starting segment. The threshold is calculated as follows:

[0047] Threshold=min(En)+τ×(max(En)-min(En))

[0048] In the formula, τ is a constant;

[0049] (3.4) Move the full-band acoustic segment obtained in (1) and the four sub-band acoustic segments after filtering in (2) forward by three segments from the starting segment position to search for the amplitude spectrum peak, find the first peak position, and record the peak position as the starting point;

[0050] (3.5) Based on the group delay characteristics of the four sub-band filters at their respective center frequencies, delay the offsets respectively to align with the time start of the audio segment;

[0051] (4) Perform variable-scale framing and feature extraction on the audio segment (3) after starting point alignment; specifically including:

[0052] (4.1) Perform variable-scale framing on the audio clip after alignment (3) and extract the 5 frames before the start of the alignment time.

[0053] (4.2) Extract the short-time energy as a feature from the above 5 frames respectively, and denote the short-time energy of the nth frame of the mth sub-band segment as:

[0054]

[0055] Where m = 1, 2, 3, 4 are the sub-band segment numbers; n = 1, 2, 3, 4, 5 are the corresponding frame numbers; N is the frame length. If the amplitude is a single sample point in the frame, then the short-time energy feature vectors obtained from the full-band acoustic segment and the sub-band acoustic segment are:

[0056]

[0057] (4.3) Extract the short-time energy ratio feature. Specifically, the short-time energy ratio is the ratio of the short-time energy of the acoustic segment in the sub-band output of the sub-band filter bank to the short-time energy of the acoustic segment in the full-band:

[0058]

[0059] Where m′=1,2,3,4 are the sub-band segment numbers; n=1,2,3,4,5 are the corresponding frame numbers. Then, the short-time energy ratio eigenvectors of the four sub-band audio segments and the full-band audio segments are:

[0060]

[0061] (4.4) The obtained short-time energy features and short-time energy ratio features are reassembled to obtain the energy set features. The multi-scale sub-band energy set features MS of each sound segment are represented as follows:

[0062]

[0063] (5) Based on the multi-scale sub-band energy collection features extracted in (4), the muzzle wave is classified to obtain the best recognition result.

[0064] Preferably, in some embodiments, the ballistic shock wave identification method based on singular value decomposition specifically includes:

[0065] 1. Signal preprocessing

[0066] Bandpass filtering: The acquired gunshot signal is first subjected to bandpass filtering. The passband frequency of the filter is 100Hz to 600Hz. The purpose is to suppress high-frequency noise and reflected shock waves while retaining the low-frequency signal of the muzzle shock wave. Signal framing: The filtered signal is then segmented into frames, each 0.5ms long, with 50% overlap between frames.

[0067] 2. Feature Extraction

[0068] Time-domain feature extraction: Extracting feature parameters from the time-domain waveform of a signal, mainly including:

[0069] The duration of the positive peak rise of the shock wave, T sr Positive pressure duration T s1 Negative pressure duration T s2 The duration T from the peak positive pressure to the peak negative pressure s3 ;

[0070] The maximum positive peak value P of the shock wave max Minimum value of negative peak P min Ratio of peak absolute value

[0071] The rate of rise of the shock wave under barometric pressure R up and positive pressure descent rate R down .

[0072] Duration T of the muzzle shock wave m The duration of the positive peak rise, T mr The duration of the positive peak, T m1 Duration of negative peak T m2 The duration T from the peak positive pressure to the peak negative pressure m3 .

[0073] Maximum pressure P of the muzzle shock wave max Minimum value P min Power P m Positive peak power P m1 negative peak power P m2 Power P from positive pressure peak to negative pressure peak m3 Positive pressure rise rate R up .

[0074] 3. Feature standardization

[0075] Standardization: The extracted raw features are standardized to eliminate the influence of different feature units and value ranges. The standardization formula is:

[0076]

[0077] Where, μi σ is the average value of the i-th feature. i Let be the standard deviation of the i-th feature.

[0078] 4. Singular Value Decomposition (SVD)

[0079] Correlation coefficient matrix calculation: Calculate the correlation coefficient matrix of the standardized feature matrix.

[0080] Singular Value Decomposition (SVD): Performing SVD on the correlation coefficient matrix yields singular values ​​and their corresponding left singular vectors. The SVD formula is:

[0081]

[0082] Where U is the left singular vector matrix, Σ is the singular value matrix, and V is the right singular vector matrix.

[0083] 5. Weighted vector fitting

[0084] Weight vector selection: Based on the contribution rate and cumulative contribution rate of the singular values, the left singular vectors corresponding to the main singular values ​​are selected as the weight vectors. Typically, the left singular vectors corresponding to the first few singular values ​​with the largest contribution rates are chosen.

[0085] Feature space mapping: Mapping the standardized feature matrix to the feature space and calculating the weighted values:

[0086] d i =||G T F i ′||2

[0087] Where G is the weight vector and Fi′ is the standardized feature vector.

[0088] 6. Threshold recognition training

[0089] Threshold setting: A recognition threshold is set based on the weighted values ​​of the training samples. Typically, the minimum weighted value is reduced by a certain percentage as the threshold.

[0090] Thr=α×min{d1,d2,K,d n}

[0091] Where α is a constant, usually taken as 0.8.

[0092] 7. Identification and Classification

[0093] Feature weighting: The feature vectors of the test samples are weighted, and the weight values ​​are calculated.

[0094] Flag = ||G T F′||2

[0095] Threshold comparison: The weighted value is compared with the identification threshold. If the weighted value exceeds the threshold, the signal is determined to be a target signal (shock wave or muzzle shock wave); otherwise, it is determined to be a non-target signal.

[0096] 8. Recognition Results

[0097] Output results: Based on the identification results, output the type of the signal (shock wave or muzzle shock wave) and record the time of arrival (TOA) of the signal.

[0098] Further, in one embodiment, step 2, which involves detecting the rising edge of the pulse waveform for each signal pulse obtained in step 1 to obtain the TOA (Time of Arrival) of the gunshot signal in all microphone array channels, specifically includes:

[0099] Step 2-1: For the gunshot signal pulse extracted in Step 1, define the number of microphone array elements as I, and the channel signal of the i-th microphone array element as x. i (n), the number of protection distance samples is n0; for each microphone array element channel, L sample data points are selected before the sample point number n of the signal to be measured to obtain the background noise level estimate N. i :

[0100]

[0101] Set the detection threshold for the first rising edge of the direct-path pulse of the gunshot signal:

[0102]

[0103] Where a < b, meaning the detection threshold value of the ballistic shock wave signal pulse is greater than that of the muzzle wave signal pulse, the signal amplitude is taken as the first time it exceeds the detection threshold T. w,i Sample number n 1,i The time of the first rising edge of the direct-arrival pulse of the gunshot signal is estimated, i.e., TOA. w,i =n 1,i i = 1, 2, ..., I;

[0104] Step 2-2, define T c For each microphone array channel, the TOA is estimated using the first rising edge time obtained in step 2-1, with a sample number slightly larger than the positive half-cycle width of the gunshot signal pulse. w,i Starting with TOA w,i +T c The endpoint is to search for the first peak value P of the gunshot signal pulse. p,i And set the detection threshold for the second rising edge of the direct-arrival pulse of the gunshot signal:

[0105] T p,i =cP p,i,0<c<1,i=1,2,…,I

[0106] At the pulse peak P p,i Previously, the signal amplitude was taken as exceeding the detection threshold T for the first time. p,i Sample number n 2,i As an estimate of the second rising edge time of the direct-arrival pulse of the gunshot signal, i.e., TOA p,i =n 2,i i = 1, 2, ..., I;

[0107] Step 2-3: For each microphone array element channel, based on the estimated rise times of the two gunshot signal direct wave pulses obtained in Step 2-1 and Step 2-2, the average of the two values ​​is used to obtain the TOA estimate of the gunshot signal direct wave.

[0108]

[0109] In the formula, TOA i This represents the TOA (Time of Arrival) of the gunshot signal corresponding to the i-th microphone array element channel, which is the time it reaches the rising edge of the wave.

[0110] Furthermore, in one embodiment, step 3 specifically includes:

[0111] For the TOA of the direct-arrival wave of the gunshot signal in all microphone array channels obtained in step 2, taking microphone array element 1 as the reference array element, calculate the TDOA of the direct-arrival wave of the gunshot signal relative to the reference array element for each microphone array element, and obtain the TDOA vector b = [Δτ] of the direct-arrival wave of the gunshot signal of the microphone array. 21 ,Δτ 31 ,...,Δτ I1 ] T ,in:

[0112] Δτ i1 =TOA i -TOA1, i = 2, 3, ..., I

[0113] In the formula, TOA i TOA1 represents the TOA of the direct arrival wave of the gunshot signal corresponding to the i-th microphone element channel, and TOA1 represents the TOA of the direct arrival wave of the gunshot signal corresponding to the reference element. I is the number of microphone elements.

[0114] Furthermore, in one embodiment, step 4, based on the time difference of arrival (TDOA) of the direct-arrival wave of the gunshot signal relative to the reference element obtained in step 3, calculates the direction vector s of the direct-arrival wave of the gunshot signal using the least squares method, specifically including:

[0115] Step 4-1, based on the coordinate vector m of each microphone array element i =[xi ,y i ,z i ] T For each element i = 1, 2, ..., I, with microphone 1 as the reference element, calculate the connection vectors from the reference element to the other elements to obtain the array coefficient matrix A of the microphone array.

[0116] A = [m2-m1,m3-m1,...m I -m1] T

[0117] Where, x i ,y i ,z i Let i represent the i-th microphone element, and I be the number of microphone elements.

[0118] Step 4-2: Using the microphone array configuration coefficient matrix A obtained in Step 4-1 and the direct wave TDOA vector b of the gunshot signal obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the least squares method.

[0119]

[0120] In the formula, For atmospheric sound speed, s x ,s y ,s z These are the components of the direction vector s in the x, y, and z directions, respectively.

[0121] Furthermore, in one embodiment, step 5, which involves calculating the angle estimate of the direct wave direction of the gunshot signal based on the direction vector s, specifically includes:

[0122] The azimuth angle θ of the direct wave of the gunshot signal is calculated based on the direction vector s as follows:

[0123]

[0124] Pitch angle for:

[0125]

[0126] Then according to s x and s y The sign value determines the quadrant in which the azimuth angle θ lies.

[0127] In one embodiment, a gunshot direction finding system based on rise-edge time-of-arrival estimation is provided in a multipath propagation environment, the system comprising:

[0128] The first module is used to acquire gunshot signals using a microphone array, and to extract the muzzle wave signal pulse and the ballistic shock wave signal pulse for each microphone array element channel.

[0129] The second module is used to detect the rising edge of each signal pulse extracted by the first module and obtain the TOA of the gunshot signal in all microphone array channels by detecting the rising edge of the pulse waveform.

[0130] The third module is used to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal in all microphone array element channels obtained from the second module, and to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0131] The fourth module is used to calculate the direction vector s of the direct wave of the gunshot signal based on the time difference of arrival (TDOA) of each microphone array element relative to the reference array element obtained in the third module, using the least squares method.

[0132] The fifth module is used to calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0133] Specific limitations regarding the gunshot direction finding system based on rising edge time of arrival estimation in a multipath propagation environment can be found in the limitations of the gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment described above, and will not be repeated here. Each module in the aforementioned gunshot direction finding system based on rising edge time of arrival estimation in a multipath propagation environment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0134] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements:

[0135] Step 1: In a multipath propagation environment, use a microphone array to collect gunshot signals. For each microphone array element channel, extract the muzzle wave signal pulse and the ballistic shock wave signal pulse of the gunshot.

[0136] Step 2: For each signal pulse extracted in Step 1, the TOA of the gunshot signal directly reaching the rising edge is obtained by detecting the rising edge of the pulse waveform in each microphone array element channel.

[0137] Step 3: Using the TOA of the rising edge of the direct arrival wave of the gunshot signal in all microphone array element channels obtained in Step 2, calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0138] Step 4: Based on the time difference of arrival (TDOA) of the direct wave of the gunshot signal relative to the reference element obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the least squares method.

[0139] Step 5: Calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0140] For specific limitations on each step, please refer to the limitations on the gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment mentioned above, which will not be repeated here.

[0141] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being implemented when executed by a processor:

[0142] Step 1: In a multipath propagation environment, use a microphone array to collect gunshot signals. For each microphone array element channel, extract the muzzle wave signal pulse and the ballistic shock wave signal pulse of the gunshot.

[0143] Step 2: For each signal pulse extracted in Step 1, the TOA of the gunshot signal directly reaching the rising edge is obtained by detecting the rising edge of the pulse waveform in each microphone array element channel.

[0144] Step 3: Using the TOA of the rising edge of the direct arrival wave of the gunshot signal in all microphone array element channels obtained in Step 2, calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element.

[0145] Step 4: Based on the time difference of arrival (TDOA) of the direct wave of the gunshot signal relative to the reference element obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the least squares method.

[0146] Step 5: Calculate the angle estimate of the direction of the direct wave of the gunshot signal based on the direction vector s.

[0147] For specific limitations on each step, please refer to the limitations on the gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment mentioned above, which will not be repeated here.

[0148] As a specific example, the invention will be further described and verified in detail in one embodiment.

[0149] Since the muzzle wave signal of gunshot originates from the direction of gunfire, the following discussion focuses on the muzzle wave signal and uses a specific example of the muzzle wave signal received by a four-element planar array to illustrate the present invention. The analysis of the ballistic shock wave signal of gunshot can be derived similarly. The specific implementation steps are as follows:

[0150] Step 1: For multipath propagation environments, a microphone array is used to collect gunshot signals; for each microphone array element channel, the gunshot muzzle wave signal pulse is extracted through low-pass filtering and muzzle wave identification processing.

[0151] Step 1-1: For multipath propagation environments, a planar array of no fewer than three microphones or a three-dimensional array of no fewer than four microphones is used. All microphone array element channels synchronously acquire gunshot signals and convert them into digital array signals. In a specific embodiment of this invention, for a four-element planar microphone array, it is assumed that the position vector m of the i-th microphone is... i =[x i ,y i ,z i ] T If i = 1, 2, 3, 4, and it is a planar array, then the microphone z... i =0, i=1,2,3,4.

[0152] Steps 1-2: For each microphone array element channel, use a cutoff frequency of f. l A low-pass filter is used to obtain the muzzle wave signal processing branch. The muzzle wave signal pulse is extracted using a muzzle wave identification method based on multi-scale sub-band energy collection characteristics. In a specific embodiment of this invention, the cutoff frequency of the low-pass filter is 800Hz.

[0153] Step 2: For the gunshot signal pulse extracted in Step 1, obtain the TOA (Time of Arrival) of the direct-arrival wave of the gunshot signal in all microphone array channels by detecting the rising edge of the pulse waveform.

[0154] Step 2-1: For the gunshot signal pulse extracted in Step 1, define the number of microphone array elements as I, and the channel signal of the i-th microphone array element as x. i (n), the number of protection distance samples is n0; for each microphone array element channel, L sample data points are selected before the sample point number n of the signal to be measured to obtain the background noise level estimate N. i In this example, the number of microphone array elements is 4, the number of selected sample points L is 50, and the number of protection distance sample points n0 is 2000.

[0155]

[0156] Since the detection threshold for ballistic shock wave signal pulses is greater than that for muzzle wave signal pulses, in this example, the noise threshold for ballistic shock wave signals is generally set to 10 times the background noise floor; the noise threshold for muzzle wave signals is set to 3 times the background noise floor, i.e.:

[0157]

[0158] The signal amplitude is taken as the first time it exceeds the detection threshold T. w,i Sample number n 1,i The time of the first rising edge of the direct-arrival pulse of the gunshot signal is estimated, i.e., TOA. w,i =n 1,i i = 1, 2, 3, 4.

[0159] Step 2-2, define T c The number of samples is slightly larger than the positive half-cycle width of the gunshot signal pulse. In a specific embodiment of the present invention, for the gunshot muzzle wave signal T... c The value is selected as 0.00013s for the gunshot ballistic shock wave signal T. c The value is selected as 0.00006s. For each microphone array element channel, the TOA is estimated using the first rising edge time obtained in step 2-1. w,i Starting with TOA w,i +T c The endpoint is to search for the first peak value P of the gunshot signal pulse. p,i And set the detection threshold for the second rising edge of the direct-arrival pulse of the gunshot signal:

[0160]

[0161] At the pulse peak P p,i Previously, the signal amplitude was taken as exceeding the detection threshold T for the first time. p,i Sample number n 2,i As an estimate of the second rising edge time of the direct-arrival pulse of the gunshot signal, i.e., TOA p,i =n 2,i i = 1, 2, 3, 4.

[0162] Step 2-3: For each microphone array element channel, based on the estimated rise times of the two gunshot signal direct wave pulses obtained in Step 2-1 and Step 2-2, the average of the two values ​​is used to obtain the TOA estimate of the gunshot signal direct wave.

[0163]

[0164] Step 3: Using the TOA of the direct-arrival wave rising edge of the muzzle wave signal in all microphone array channels obtained in Step 2, and taking microphone array element 1 as the reference array element, calculate the TDOA of the direct-arrival wave of the muzzle wave of each array element relative to the reference array element, and obtain the TDOA vector b = [Δτ] of the direct-arrival wave of the muzzle wave of the microphone array. 21 ,Δτ 31 ,Δτ 41 ] T ,in:

[0165] Δτ i1 =TOA i -TOA1, i = 2, 3, 4

[0166] Step 4: Using the direct wave TDOA of the gunshot signal relative to the reference element obtained in Step 3, the direction vector s of the direct wave of the gunshot signal is calculated using the LS method. Then, the angle estimate of the direction of the direct wave of the gunshot signal is calculated based on the direction vector s.

[0167] Step 4-1, based on the coordinate vector m of each microphone i =[x i ,y i ,z i ] T For each element i = 1, 2, 3, 4, with microphone 1 as the reference element, calculate the vectors connecting the reference element to the other elements to obtain the array coefficient matrix A of the microphone array.

[0168] A = [m2-m1, m3-m1, m4-m1] T

[0169] Step 4-2: Using the microphone array configuration coefficient matrix A obtained in Step 4-1 and the direct wave TDOA vector b of the gunshot signal obtained in Step 3, the direction vector of the direct wave of the gunshot signal is calculated using the LS method.

[0170]

[0171] In the formula, The speed of sound at atmosphere.

[0172] Step 4-3: Using the direction vector s of the direct wave of the gunshot signal obtained in Step 4-2, calculate the azimuth angle of the direct wave of the gunshot signal.

[0173]

[0174] Pitch angle:

[0175]

[0176] Then according to s x and s yThe sign value determines the quadrant in which the azimuth angle θ lies.

[0177] Figure 2 (a) is a schematic diagram of the stereo array sound source direction finding of a small gunshot detection system, with the sound source signal elevation angle being... The azimuth angle is θ. Figure 2 (b) in the figure is a qualitative analysis diagram of the direction vector s in each quadrant.

[0178] Figure 3 (a) and (b) in the figure are the time-domain waveform and time-frequency spectrum of the measured muzzle wave signal under multipath propagation environment, respectively. It can be seen that multipath propagation causes the muzzle wave signal to fluctuate, and the corresponding spectrum no longer presents a single pulse signal. The time-domain and frequency-domain characteristics of the signal are greatly affected.

[0179] Figure 4 This diagram illustrates rising edge detection of muzzle wave signals in a multipath environment. Threshold 1, marked in the diagram, is a noise detection threshold set based on the background noise floor, while threshold 2 is a peak detection threshold set based on the first peak value of the signal's rising edge. By jointly detecting these two thresholds, two TOA estimation points for the signal can be determined. Finally, by calculating the average of these two TOA estimation points, the final estimate of the signal's TOA based on rising edge detection is obtained.

[0180] Figure 5 This paper demonstrates the direction-finding error of gunshot muzzle wave signals estimated based on rise-edge detection under different incident angles. In a multipath propagation environment with a signal-to-noise ratio of 10 dB, the incident angle ranged from 0° to 360° in 15° increments. For each incident angle, 50 experiments were repeated using the angle-finding method based on rise-edge detection estimation, and the average error between the estimated and actual angles was calculated. Statistical results show that the average error of the gunshot direction-finding angle estimated based on rise-edge arrival time is less than 5°, exhibiting good direction-finding accuracy.

[0181] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention without departing from its spirit and scope should be included within the protection scope of the present invention.

Claims

1. A gunshot direction finding method based on rising edge time of arrival estimation in a multipath propagation environment, characterized in that, The method includes the following steps: Step 1: In a multipath propagation environment, use a microphone array to collect gunshot signals. For each microphone array element channel, extract the muzzle wave signal pulse and the ballistic shock wave signal pulse of the gunshot. Step 2: For each signal pulse extracted in Step 1, the TOA (Time of Arrival) of the gunshot signal's direct arrival wave rise in all microphone array channels is obtained through pulse waveform rising edge detection; specifically including: Step 2-1: For the gunshot signal pulse extracted in Step 1, define the number of microphone array elements as follows. , No. The signal of each microphone element channel is The number of protection distance sampling points is For each microphone array element channel, select the sample number n before the number of the signal sample to be measured. Background noise level estimation is obtained from sample data. : Set the detection threshold for the first rising edge of the direct-path pulse of the gunshot signal: in That is, if the detection threshold value of the ballistic shock wave signal pulse is greater than that of the muzzle wave signal pulse, the signal amplitude is taken as the first time it exceeds the detection threshold. Sample number As an estimate of the first rising edge time of the direct-arrival pulse of the gunshot signal, i.e. ; Step 2-2, Define For each microphone array channel, the number of samples is slightly larger than the positive half-cycle width of the gunshot signal pulse, estimated using the first rising edge time obtained in step 2-1. Starting from, with The endpoint is to search for the peak value of the first gunshot signal pulse. And set the detection threshold for the second rising edge of the direct-arrival pulse of the gunshot signal: At the peak of the pulse Previously, the signal amplitude was taken as exceeding the detection threshold for the first time. Sample number As an estimate of the second rising edge time of the direct-arrival pulse of the gunshot signal, i.e. ; Step 2-3: For each microphone array element channel, based on the estimated rise times of the two gunshot signal direct wave pulses obtained in Step 2-1 and Step 2-2, the average of the two values ​​is used to obtain the TOA estimate of the gunshot signal direct wave. In the formula, This represents the TOA (Time of Arrival) of the gunshot signal corresponding to the i-th microphone array element channel, which is directly reached by the rising edge of the wave. Step 3: Using the TOA of the rising edge of the direct arrival wave of the gunshot signal in all microphone array element channels obtained in Step 2, calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element. Step 4: Based on the Time Difference of Arrival (TDOA) of the direct-arrival wave of the gunshot signal relative to the reference element obtained in Step 3, the direction vector of the direct-arrival wave of the gunshot signal is calculated using the least squares method. ; Step 5, based on the direction vector Calculate the angle estimate of the direction of the direct wave of the gunshot signal.

2. The gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment according to claim 1, characterized in that, The microphone array mentioned in step 1 is a planar array consisting of no less than 3 microphones or a three-dimensional array consisting of no less than 4 microphones, and all microphone array element channels synchronously acquire gunshot signals and convert them into digital array signals.

3. The gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment according to claim 1, characterized in that, Step 1, which involves extracting the muzzle wave signal pulse and the ballistic shock wave signal pulse for each microphone array element channel, specifically includes: For each microphone array element channel, the cutoff frequency is... The muzzle wave signal processing branch is obtained by using a low-pass filter, and then the muzzle wave signal pulse is extracted by a muzzle wave identification method based on multi-scale sub-band energy collection characteristics. For each microphone array element channel, a cutoff frequency of [frequency value missing] is used. The high-pass filter is used to obtain the ballistic shock wave signal processing branch, and then the gunshot ballistic shock wave signal pulse is extracted by the ballistic shock wave identification method based on singular value decomposition.

4. The gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment according to claim 1, characterized in that, Step 3 specifically includes: For the TOA of the direct-arrival wave of the gunshot signal in all microphone array channels obtained in step 2, taking microphone array element 1 as the reference array element, calculate the TDOA of the direct-arrival wave of the gunshot signal for each microphone array element relative to the reference array element, and obtain the TDOA vector of the direct-arrival wave of the gunshot signal of the microphone array. ,in: In the formula, This represents the TOA (Time of Arrival) of the gunshot signal corresponding to the i-th microphone array element channel, which is the time from the rising edge of the wave. This indicates the TOA (Time of Arrival) of the gunshot signal corresponding to the reference array element, which is the time it reaches the rising edge of the wave. This represents the number of microphone array elements.

5. The gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment according to claim 4, characterized in that, Step 4 involves calculating the direction vector of the direct-arrival wave of the gunshot signal based on the Time Difference of Arrival (TDOA) of each microphone element relative to the reference element obtained in Step 3, using the least squares method. Specifically, it includes: Step 4-1, based on the coordinate vector of each microphone element = Using microphone 1 as the reference array element, the connection vectors from the reference array element to the other array elements are calculated to obtain the array coefficient matrix of the microphone array. : in, These are the i-th microphone elements. The number of microphone array elements. Step 4-2, using the microphone array array coefficient matrix obtained in Step 4-1 and the direct-arrival wave TDOA vector of the gunshot signal obtained in step 3 The direction vector of the direct wave of the gunshot signal was calculated using the least squares method. : In the formula, The speed of sound at atmosphere. Direction vectors Components in the x, y, and z directions.

6. The gunshot direction finding method based on rise edge time of arrival estimation in a multipath propagation environment according to claim 5, characterized in that, Step 5 describes the method based on the direction vector. Calculating the angle estimation of the direct wave direction of the gunshot signal specifically includes: According to the direction vector Calculate the azimuth angle of the direct wave of the gunshot signal. for: Pitch angle for: Then according to and The sign value determines the azimuth angle The quadrant in which it is located.

7. A gunshot direction finding system based on rise-edge time-of-arrival estimation in a multipath propagation environment, based on the method of any one of claims 1 to 6, characterized in that, The system includes: The first module is used to acquire gunshot signals using a microphone array, and to extract the muzzle wave signal pulse and the ballistic shock wave signal pulse for each microphone array element channel. The second module is used to detect the rising edge of each signal pulse extracted by the first module and obtain the TOA of the gunshot signal in all microphone array channels by detecting the rising edge of the pulse waveform. The third module is used to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal in all microphone array element channels obtained from the second module, and to calculate the time difference of arrival (TDOA) of the direct arrival wave of the gunshot signal relative to the reference array element for each microphone array element. The fourth module is used to calculate the direction vector of the gunshot signal direct wave based on the time difference of arrival (TDOA) of each microphone element relative to the reference element obtained in the third module, using the least squares method. ; The fifth module is used to determine the direction vector. Calculate the angle estimate of the direction of the direct wave of the gunshot signal.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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