Joint Detection Threshold Optimization and Illumination Time Allocation Strategy for Cognitive Tracking in a Networked Radar System

Radar network configuration and power allocation are of great importance in military applications, where the entire surveillance area needs to be searched under resource budget constraints. To pursue the joint antenna placement and power allocation (JAPPA) optimization, this paper develops a JAPPA strategy to improve target detection performance in a widely distributed multiple-input and multiple-output (MIMO) radar network. First, the three variables of the problem are incorporated into the Neyman–Pearson (NP) detector by using the antenna placement optimization and the Lagrange power allocation method. Further, an improved iterative greedy dropping heuristic method based on a two-stage local search is proposed to solve the NP-hard issues of high-dimensional non-linear integer programming. Then, the sum of the weighted logarithmic likelihood ratio test (LRT) function is constructed as optimization criteria for the JAPPA approach. Numerical simulations and the theoretical analysis confirm the superiority of the proposed algorithm in terms of achieving effective overall detection performance.

Section: Discussionmentioning

confidence: 99%

Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network

Xie

Zhang

2022

“…H 1 : r(t) = Ĥs(t) + n(t) , Target exists at delayτ. (9) and the probability density function can be expressed as:…”

Section: Lrt Detector Of the Hybrid Distributed Pa-mimo Radar Systemmentioning

confidence: 99%

“…To cope with complex targets and environments [4] and seek breakthroughs in target detection theory and technology [5], regime modification and resource management for radar are being continuously and intensively carried out [6][7][8]. Maximizing the cognitive operation capability [9,10], optimizing the utilization of available resources [11,12], and improving the target detection capability of radar systems are fundamental topics and practical and urgent tasks faced in the field of radar information processing and optimal resource management [13,14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Configuration of Array Elements for Hybrid Distributed PA-MIMO Radar System Based on Target Detection

Xie

Zhang

et al. 2022

Self Cite

This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, the radar system signal model and array space configuration model for the PA-MIMO radar are established. Then, a novel likelihood ratio test (LRT) detector is derived based on the Neyman–Pearson (NP) criterion in a fixed noise background. It can jointly optimize the coherent processing gain and spatial diversity gain of the system by implementing subarray level and array element level optimal configuration at both receiver and transmitter ends in a uniform blocking manner. On this basis, three typical optimization problems are discussed from three aspects, i.e., the detection probability, the effective radar range, and the radar system equipment volume. The approximate closed-form solutions of them are constructed and solved by the proposed quantum particle swarm optimization-based stochastic rounding (SR-QPSO) algorithm. Through the simulations, it is verified that the proposed optimal configuration of the hybrid distributed PA-MIMO radar system offers substantial improvements compared to the other typical radar systems, detection probability of 0.98, and an effective range of 1166.3 km, which significantly improves the detection performance.

“…Remote sensing image change detection (CD) focuses on identifying changes in phenomena or objects that have occurred in the same geographical area at different times [1]. Because synthetic aperture radar (SAR) images are independent of weather and atmospheric conditions [2][3][4], SAR image CD has attracted increasing attention. Recently, SAR image CD has been widely used in a diversity of studies, such as disaster assessment [5], urban research [6], environmental monitoring [7], and forest resource monitoring [8].…”

Section: Introductionmentioning

confidence: 99%

PCBA-Net: Pyramidal Convolutional Block Attention Network for Synthetic Aperture Radar Image Change Detection

Xia

2022

Synthetic aperture radar (SAR) imagery change detection (CD) is still a crucial and challenging task. Recently, with the boom of deep learning technologies, many deep learning methods have been presented for SAR CD, and they achieve superior performance to traditional methods. However, most of the available convolutional neural networks (CNN) approaches use diminutive and single convolution kernel, which has a small receptive field and cannot make full use of the context information and some useful detail information of SAR images. In order to address the above drawback, pyramidal convolutional block attention network (PCBA-Net) is proposed for SAR image CD in this study. The proposed PCBA-Net consists of pyramidal convolution (PyConv) and convolutional block attention module (CBAM). PyConv can not only extend the receptive field of input to capture enough context information, but also handles input with incremental kernel sizes in parallel to obtain multi-scale detailed information. Additionally, CBAM is introduced in the PCBA-Net to emphasize crucial information. To verify the performance of our proposed method, six actual SAR datasets are used in the experiments. The results of six real SAR datasets reveal that the performance of our approach outperforms several state-of-the-art methods.