Joint Bernoulli Filtering and MIMO Processing for Detection of Moving Targets in Shallow Ocean Environments

Huangyu Dai,Peng Zhang,Xiang Pan,Zhongyong Chen,Zhongdi Liu

doi:10.1109/access.2021.3110876

Abstract

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

Highlights

Detection of underwater targets is a challenging task in shallow ocean environments due to multipath propagation resulting in time-delay spread of waveforms
Based on our previous work on Multipleinput multiple-output (MIMO) detection, this paper focuses on the detection [1]–[3] of underwater moving targets using an improved Bernoulli filtering algorithm
The δ-generalized labeled multi-Bernoulli (δ-generalized labeled multiBernoulli (GLMB)) filter [25] is introduced in this subsection, which will be combined with CBMEMBer filter to get a better result in distributed MIMO detection system

Summary

INTRODUCTION

Detection of underwater targets is a challenging task in shallow ocean environments due to multipath propagation resulting in time-delay spread of waveforms. Based on our previous work on MIMO detection, this paper focuses on the detection [1]–[3] of underwater moving targets using an improved Bernoulli filtering algorithm. The main contributions of this paper are as follows: (1) A processing framework based on combination of MIMO sonar and Bernoulli filter is proposed to exploit target diversity and moving cues for the enhancement of target detection in shallow ocean environments. A new filter called δcardinality balanced generalized labeled multi-Bernoulli (δCBGLMB) filter is proposed to improve the detection ability with a lower computational cost It first extracts part of the measurement sets through the traditional CBMEMBer filter and uses the GLMB filter to track targets.

DISTRIBUTED MIMO

MULTI-TARGET MULTI-BERNOULLI FILTER

LABELED MULTI-BERNOULLI FILTER

SYSTEM DESIGN OF SEA TRIAL

TARGET DETECTION AND TRACKING

CONCLUSION