Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

A Multi-Flow Information Flow Tracking Approach for Proving Quantitative Hardware Security Properties

Information Flow Tracking(IFT)is an established formal method for proving security properties related to confidentiality,integrity,and isolation.It has seen promise in identifying security vulnerabilities resulting from design flaws,timing channels,and hardware Trojans for secure hardware design.However,existing IFT methods tend to take a qualitative approach and only enforce binary security properties,requiring strict non-interference for the properties to hold while real systems usually allow a small amount of information flows to enable desirable interactions.Consequently,existing methods are inadequate for reasoning about quantitative security properties or measuring the security of a design in order to assess the severity of a security vulnerability.In this work,we propose two multi-flow solutions—multiple verifications for replicating existing IFT model and multi-flow IFT method.The proposed multi-flow IFT method provides more insight into simultaneous information flow behaviors and allows for proof of quantitative information flow security properties,such as diffusion,randomization,and boundaries on the amount of simultaneous information flows.Experimental results show that our method can be used to prove a new type of information flow security property with verification performance benefits.