Welcome! I'm Yongjin Kim.

I am an electrical engineering student interested in guidance and control, trajectory optimization, and nonlinear and adaptive control for aerospace systems.

Full rocket and exhaust plume during liftoff from a wet launch pad
34th NURA Rocket Competition

02 / Research

Areas of interest

My interests center on aerospace guidance, trajectory design, and control under nonlinear and changing flight conditions.

Guidance and Control

Guidance laws and control-system design for aerospace vehicles.

Trajectory Optimization

Optimal trajectory generation under vehicle dynamics and mission constraints.

Nonlinear and Adaptive Control

Robust flight control across nonlinear dynamics and changing operating conditions.

Digital Hardware

RTL accelerators and compute-efficient architectures for embedded systems.

03 / Projects

Selected engineering work

Each entry separates implemented work from ongoing development and documented study.

Rocket and ground-level exhaust during a NURA launch test

Flight systems

Rocket Avionics and Validation

Built telemetry ground software and developed an STM32F405 replay platform for repeatable avionics firmware validation.

STM32C/C++TelemetryHILS

Navigation

INS/GNSS Integrated Navigation

Evaluated a loosely coupled extended Kalman filter using reference position, velocity, and attitude data.

MATLABEKFSensor Fusion
View repository

Digital hardware

Tiny Neural Network Accelerator

Implemented FC1-Norm-ReLU-FC2 inference with a reusable 4x4 weight-stationary systolic array.

VerilogRTLSystolic Array
View repository

Embedded optimization

Zynq FFT Software Optimization

Completed and benchmarked a 64-point radix-2 DIF FFT on a Zybo Z7-20, comparing direct DFT, C FFT, and ARM implementations.

CARMZynq-7000
View study

Computer architecture

Five-Stage Tiny CPU

Implemented a 16-bit pipelined CPU with arithmetic, memory, branch instructions, and multi-stage data forwarding.

VerilogPipelineForwarding
View architecture
YOLO person detection during an indoor DJI Tello test

Computer vision

Vision-Based Drone Following

Demonstrated live YOLO person detection and bounding boxes on Tello video.

PythonYOLOUbuntu
View prototype
WineQueen embedded hardware prototype

Embedded vision

WineQueen

Developed YOLOv8 bottle-mouth detection and Arduino-driven control software for automated wine sealing and opening.

YOLOv8ArduinoControl
View project

04 / Experience

Education and experience

ASEC Rocket Team

Electronics Team Member, Konkuk University

Intelligent Navigation and Control Laboratory

Undergraduate Research Assistant, Konkuk University

Konkuk University

B.S. Candidate, Electrical and Electronics Engineering

Recognition4th Place / Sponsor Award34th NURA Rocket Competition

05 / Contact

For research and engineering conversations, reach me by email or through the profiles below.