Error Reduction by Reflected Signals in Automotive Radar Network Systems

This paper proposes a binary linear programming formulation for multiple target assignment of a radar network and demonstrates its applicability to obtain optimal solutions using an off-the-shelf mixed-integer linear programming solver. The goal of radar resource scheduling in this paper is to assign the maximum number of targets by handing over targets between networked radar systems to overcome physical limitations such as the detection range and simultaneous tracking capability of each radar. To achieve this, time windows are generated considering the relation between each radar and target considering incoming target information. Numerical experiments using a local-scale simulation were performed to verify the functionality of the formulation and a sensitivity analysis was conducted to identify the trend of the results with respect to several parameters. Additional experiments performed for a large-scale (battlefield) scenario confirmed that the proposed formulation is valid and applicable for hundreds of targets and corresponding radar network systems composed of five distributed radars. The performance of the scheduling solutions using the proposed formulation was better than that of the general greedy algorithm as a heuristic approach in terms of objective value as well as the number of handovers.

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Joint Dwell Time and Bandwidth Optimization for Multi-Target Tracking in Radar Network Based on Low Probability of Intercept

Sensors ◽

10.3390/s20051269 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1269

Author(s):

Lintao Ding ◽

Chenguang Shi ◽

Wei Qiu ◽

Jianjiang Zhou

Keyword(s):

Lower Bound ◽

Target Tracking ◽

Bandwidth Allocation ◽

Dwell Time ◽

Network Systems ◽

Bandwidth Optimization ◽

Radar Network ◽

Multi Target Tracking ◽

Low Probability ◽

Low Probability Of Intercept

Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer–Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.

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Encultured minds, not error reduction minds

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002826 ◽

2020 ◽

Vol 43 ◽

Author(s):

Robert Mirski ◽

Mark H. Bickhard ◽

David Eck ◽

Arkadiusz Gut

Keyword(s):

Free Energy ◽

Error Reduction ◽

Energy Principle ◽

Free Energy Principle ◽

Current Article

Abstract There are serious theoretical problems with the free-energy principle model, which are shown in the current article. We discuss the proposed model's inability to account for culturally emergent normativities, and point out the foundational issues that we claim this inability stems from.

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Development of a rapid hydration protocol: A unique strategy for chemotherapy error reduction

Journal of Pediatric Oncology Nursing ◽

10.1053/jpon.2002.0190066a ◽

2002 ◽

Vol 19 (2) ◽

pp. 66-67

Author(s):

Susan DiTaranto

Keyword(s):

Error Reduction

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Review on Fog Based Spectrum Sensing for Artificial Intelligence

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183816 ◽

2018 ◽

pp. 66-70

Author(s):

A. Rethina Palin ◽

I. Jeena Jacob

Keyword(s):

Bandwidth Allocation ◽

Situational Awareness ◽

Cluster Head ◽

Mesh Network ◽

Network Systems ◽

Markov Logic ◽

Wireless Mesh ◽

Local Environments ◽

Reactive Routing ◽

The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

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