Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

Abstract

In conventional weapon test field, the parameter of projectile explosion position is an important index to measure the control ability and damage efficiency of fuze. Due to the random deviation of gun firing and the difference of detection performance of fuze itself, the projectile explosion position is uncertain and relatively scattered in terminal ballistic, which is easy to miss the projectile explosion information. In order to obtain the parameters of projectile explosion position in large area of in terminal ballistic, this paper constructs a projectile explosion position testing system with distributed multiple acoustic sensor basic arrays, establishes the calculation model of projectile explosion position by a basic array, researches a multi-source data fusion algorithm based on the measurement data of multiple array acoustic basic arrays and gets real projectile explosion coordinates. To improve the measurement accuracy, we use the wavelet transform filtering method to filter the output signal of the acoustic sensors, and propose the time extraction algorithm of the projectile explosion acoustic signal with the wavelet modulus maximum. Through experiment and analysis, we collect the sound signal of the actual projectile explosion and verify the rationality and correctness of the proposed calculation model and algorithm in this paper.

An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD

“…By resolving the spatial coordinates linked to the peak value in Formula ( 17), we can derive the actual target position P(x, y, z) through the data fusion of target positions across distributed multi-acoustic basic arrays. The fusion calculation results are expressed by Formula (18).…”

“…There are many existing studies on the recognition and processing of acoustic sensor signals, such as wavelet transform [15][16][17], support vector machine [18], and particle swarm optimization [19]. In [19], Jiang et al employed the wavelet transform filtering technique to process the output signal from acoustic sensors.…”