Search Results for author:
Found 15 papers, 1 papers with code
Subspace-Based Detection in OFDM ISAC Systems under Different Constellations
This paper investigates subspace-based target detection in OFDM integrated sensing and communications (ISAC) systems, considering the impact of various constellations.
Team AcieLee: Technical Report for EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023
In this report, we describe the technical details of our submission to the EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023, by Team "AcieLee" (username: Yuqi\_Li).
Poisson Conjugate Prior for PHD Filtering based Track-Before-Detect Strategies in Radar Systems
A variety of filters with track-before-detect (TBD) strategies have been developed and applied to low signal-to-noise ratio (SNR) scenarios, including the probability hypothesis density (PHD) filter.
The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking
This paper develops a general trajectory probability hypothesis density (TPHD) filter, which uses a general density for target-generated measurements and is able to estimate trajectories of coexisting point and extended targets.
Recent Advances for Quantum Neural Networks in Generative Learning
Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.
Subspace-Based Detection and Localization in Distributed MIMO Radars
In this paper, we consider a distributed multiple-input multiple-output (MIMO) radar which radiates waveforms with non-ideal cross- and auto-correlation functions and derive a novel subspace-based procedure to detect and localize multiple prospective targets.
Multi-target Joint Tracking and Classification Using the Trajectory PHD Filter
To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter.
Trajectory PHD Filter with Unknown Detection Profile and Clutter Rate
Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation.
Trajectory PHD and CPHD Filters with Unknown Detection Profile
These filters are referred to as the unknown TPHD (U-TPHD) and unknown TCPHD (U-TCPHD) filters. By minimizing the Kullback-Leibler divergence (KLD), the U-TPHD and U-TCPHD filters can obtain, respectively, the best Poisson and independent identically distributed (IID) density approximations over the augmented sets of trajectories.
Continuous-discrete multiple target tracking with out-of-sequence measurements
When we receive an OOS measurement, the optimal Bayesian processing performs a retrodiction step that adds trajectory information at the OOS measurement time stamp followed by an update step.
Weak target detection with multi-bit quantization in colocated MIMO radar
We consider the weak target detection problem with unknown parameter in colocated multiple-input multiple-output (MIMO) radar.
Principled information fusion for multi-view multi-agent surveillance systems
A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents.
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation
Firstly, we extend the concept of JMS to the multi-trajectory scenario of maneuvering target and derive the TPHD recursion for the proposed JMS model.
Supervised Learning Based Online Tracking Filters: An XGBoost Implementation
The target state filter is an important module in the traditional target tracking framework.
Detecting retail products in situ using CNN without human effort labeling
CNN is a powerful tool for many computer vision tasks, achieving much better result than traditional methods.
