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Kalman Filtering Inertial Navigation Jobs in Virginia

Bowhead

Aerospace Engineer (GNC)

King George, VA

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

SAIC

GNC Aerospace Engineer

Dahlgren, VA

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

SAIC

GNC Aerospace Engineer

Dahlgren, VA · On-site

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

SAIC

GNC Aerospace Engineer

Dahlgren, VA · On-site

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

Forterra

Navigation Lead

Arlington, VA · On-site

$180K - $300K/yr

About the role Forterra is seeking a Navigation Lead to lead the design, development, and ... Drive algorithmic choices for Kalman filtering, non-linear optimization, sensor calibration, and ...

V2X

Aerospace Engineer-Dahlgren, VA.

Dahlgren, VA · On-site

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

V2X

Aerospace Engineer-Dahlgren, VA.

Dahlgren, VA · On-site

... Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S ...

Auterion

GNC Engineer

Arlington, VA · On-site

Develop and maintain guidance, navigation, and control (GNC) capabilities within PX4 and ... Understanding of Kalman filtering and other state estimation algorithms * Strong written and verbal ...

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Kalman Filtering Inertial Navigation information

What are some common challenges faced when implementing Kalman Filtering for inertial navigation systems?

One common challenge in this role is managing sensor noise and drift, which can significantly affect the accuracy of inertial navigation solutions. You'll need to carefully tune filter parameters and sometimes integrate additional sensor data (like GPS or magnetometers) to improve robustness. Collaboration with hardware teams is typical, as understanding sensor characteristics is crucial for optimal filter performance. Additionally, real-time processing constraints often require you to optimize algorithms for efficiency without sacrificing accuracy.

What are the key skills and qualifications needed to thrive as a Kalman Filtering Inertial Navigation Engineer, and why are they important?

To thrive as a Kalman Filtering Inertial Navigation Engineer, you need a solid background in control systems, signal processing, and estimation theory, generally supported by a degree in electrical engineering, aerospace engineering, or a related field. Proficiency with MATLAB, Python, sensor fusion algorithms, and experience implementing Kalman filters in embedded systems are typically required. Strong analytical thinking, attention to detail, and effective communication skills help professionals collaborate across multidisciplinary teams and solve complex problems. These skills and qualifications are vital to ensure accurate navigation solutions and robust system performance in real-world applications.

What is Kalman Filtering in inertial navigation?

Kalman Filtering in inertial navigation refers to the use of a mathematical algorithm—the Kalman filter—to estimate the position, velocity, and orientation of a moving object by processing data from inertial sensors such as accelerometers and gyroscopes. The Kalman filter improves the accuracy of navigation by optimally combining sensor measurements and correcting for errors and noise. This technique is essential in applications like aerospace, robotics, and autonomous vehicles, where precise movement tracking is required. By continuously updating estimates as new sensor data arrives, Kalman filtering helps provide reliable and real-time navigation solutions.

What is the difference between Kalman Filtering Inertial Navigation vs INS Algorithm Developer?

AspectKalman Filtering Inertial NavigationINS Algorithm Developer
CredentialsEngineering degrees, knowledge of Kalman filters, navigation systemsEngineering degrees, expertise in navigation algorithms, sensor fusion
Work EnvironmentResearch labs, aerospace, defense, autonomous vehicle developmentSoftware development, simulation, embedded systems in similar industries
Industry UsageDesign and implementation of navigation systems using Kalman filtersDeveloping algorithms for inertial navigation systems, improving accuracy

Kalman Filtering Inertial Navigation focuses on applying Kalman filters to process sensor data for navigation accuracy, while INS Algorithm Developers design and optimize algorithms for inertial navigation systems. Both roles require similar technical skills but differ in their primary focus: one on filtering techniques, the other on algorithm development.

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Kalman Filtering Inertial Navigation jobs near you

Aerospace Engineer (GNC) with Security Clearance

UICGS and Bowhead Family of Companies

Dahlgren, VA • On-site

Other

Posted 12 days ago

Job description

Overview Bowhead seeks a GNC (Guidance, Navigation, and Control) Aerospace Engineer to support hypersonic research efforts of the NSWCDD in Dahlgren, VA. This role provides technical expertise across system modeling, simulation, algorithm development, data analysis, and integration activities in support of weapon system development, performance assessment, and mission engineering. The Engineer will directly support government technical leads and collaborate with associated subsystem SMEs to deliver analytical rigor, modeling capability, and engineering insight into guidance, navigation, and control solutions used in government test and evaluation environments.

Responsibilities • Develop, implement, and refine GNC algorithms for guidance laws, control loops, navigation filters, and state estimation. • Support weapon system modeling and simulation, including 6 DOF (six degrees of freedom) simulation environments and subsystem integration. • Perform trajectory analysis, mission performance evaluations, and control system stability assessments.

• Develop navigation solutions integrating IMUs, GPS, INS, seekers, and other sensor modalities. • Conduct Monte Carlo analysis, uncertainty modeling, and system performance sensitivity studies. • Implement GNC prototypes in MATLAB/Simulink, Python, C/C++, or equivalent simulation frameworks.

• Analyze telemetry, flight test data, and HWIL/SIL outputs to validate system performance. • Support requirements decomposition, interface definition, and subsystem integration with E20 system engineering teams. • Document models, test results, algorithm behavior, and performance in technical reports and presentations.

• Participate in technical reviews, design sessions, and engineering working groups with government and contractor personnel. Qualifications • Bachelor's degree in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Physics, or related discipline or a Master's degree (preferred) with coursework such as Modern Control Systems, Modern (or Optimal) Control Theory, MIMO Control, Linear Systems Theory, Robotics, [Optimal] State Estimation, or courses with Sensing, Navigation, Localization, or Mapping in the name. • 4+ years experience in GNC engineering, missile guidance, flight controls, or related system modeling.

• Expertise in state estimation (e.g. Kalman filters for tracking or inertial navigation) with the ability to evaluate real multi-source measurement datasets processed with legacy or novel candidate-developed state estimation algorithms in MATLAB and C++ and translate the evaluations into reports and briefing material. • Proficiency in MATLAB/Simulink and at least one programming language (Python or C/C++).

• The successful candidate shall have experience with guidance algorithms (proportional navigation, pursuit guidance, optimal guidance, intercept geometry), navigation & estimation, Kalman filtering (EKF/UKF), inertial navigation, GPS/INS integration, sensor fusion, control engineering, PID, LQR, state space control, autopilot design, stability & robustness analysis, M&S (Modeling & Simulation), MATLAB/Simulink, Python (NumPy/SciPy), C/C++ for embedded or real time simulation, and familiarity with 6 DOF modeling.• Ability to work with E20 technical SMEs to develop or refine analytical tools, performance models, or algorithmic improvements. • Ability to obtain and maintain a DoD Secret clearance (current Secret preferred). Preferred Qualifications • A background in aerospace vehicles.

• Familiarity with hardware in the loop (HWIL) or software in the loop (SIL) environments is a plus. • Experience supporting missile systems, unmanned systems, or weapons/flight dynamics programs. • Experience supporting NSWCDD, NAVSEA, or other DoD weapons engineering divisions.

• Experience with aerospace flight test data analysis or guidance law verification. • Knowledge of seeker modeling, sensor suite integration, or autopilot tuning. SECURITY CLEARANCE REQUIRED: Must be able to obtain and maintain a security clearance at the Secret level.

US Citizenship is a requirement for Secret clearance at this location. Physical Demands: • Must be able to lift up to 25 pounds • Must be able to stand and walk for prolonged amounts of time • Must be able to twist, bend and squat periodically #LI-MN1

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