In the modern engineering and software landscape, estimation isn't just a technical step — it's a strategic decision that shapes the success of projects. Developers, engineers, and project managers constantly face the challenge of balancing cost, performance, and long-term value. One powerful but often under-leveraged tool in this balancing act is all-state estimation.

While all-state estimation is commonly known in control theory and robotics, its implications go far beyond mathematical modeling. When applied correctly, it can be a key differentiator in projects estimated for profit versus those driven by budget constraints.

? What is All-State Estimation?

All-state estimation refers to the process of estimating all internal state variables of a dynamic system using measurable outputs, inputs, and mathematical models. These state variables — such as velocity, position, pressure, or internal charge — often cannot be directly measured due to sensor limitations, cost, or physical constraints.

Instead, systems like observers or filters (e.g., Kalman Filter, Luenberger Observer, Extended Kalman Filter) are used to reconstruct these unmeasured states in real time, enabling accurate control, monitoring, and optimization.

? Why It Matters in Project Estimation

When developers and engineers begin a project, they're typically estimating effort and cost based on available resources, timeframes, and deliverables. However, the technical decisions — like whether or not to implement all-state estimation — can significantly affect the project’s scope, complexity, and value.

This is where two major estimation approaches diverge:

1. Estimating for Budget

Projects that are budget-driven typically focus on:

• Keeping costs as low as possible

• Delivering a functional minimum viable product (MVP)

• Avoiding advanced or “non-essential” components that may add complexity or time

In these cases, all-state estimation may be deprioritized unless it is mission-critical. Developers might rely on:

• Direct sensor measurements only

• Simplified logic or control systems

• Static models or heuristics

While this can reduce short-term costs, it may:

• Limit system robustness

• Increase reliance on expensive or bulky sensors

• Make long-term scaling or optimization harder

Example: In a budget-constrained industrial automation project, a basic PID controller using only position feedback may be deployed, skipping velocity or acceleration estimation entirely.

2. Estimating for Profit

Profit-driven projects focus on:

• Delivering a high-performing, scalable, and future-proof solution

• Reducing long-term maintenance and operational costs

• Creating competitive advantages through smart technologies

Here, all-state estimation is often embraced as an investment. Benefits include:

• Enhanced control accuracy

• Fewer sensors (replacing hardware with software estimation)

• Better diagnostics and predictive maintenance

• Adaptive behavior in dynamic environments

Example: In autonomous drones, estimating full-state variables (position, velocity, orientation, wind effects) using Extended Kalman Filters is essential for stable flight and mission success — skipping this isn’t an option.

? Developers' Role in Profit vs. Budget Estimations

Developers are often caught in the middle of engineering ambition and business constraints. Their ability to communicate the value proposition of techniques like all-state estimation is critical.

When working in budget-driven projects:

• Developers must identify which parts of the system must be estimated

• Consider using simpler observers (e.g., Luenberger Observer) instead of complex filters

• Seek trade-offs that maintain acceptable performance without excessive cost

When working in profit-oriented projects:

• Developers should advocate for implementing full-state estimators where possible

• Use state estimation to reduce hardware complexity or sensor count

• Emphasize system resilience and long-term maintainability

⚖️ Profit vs. Budget: Trade-Off Table for All-State Estimation

? Case Study: Mobile Robot Navigation

Let’s look at a simplified case of a mobile robot used for warehouse navigation:

Budget-Driven Version:

• Uses wheel encoders for speed and distance.

• No estimation of slip, heading error, or battery state.

• Performance suffers on slippery floors or with uneven loading.

• Cheap to build, but requires frequent recalibration and user intervention.

Profit-Driven Version:

• Implements an Extended Kalman Filter to estimate position, velocity, orientation, and battery level.

• Uses fewer sensors but compensates with smarter algorithms.

• More reliable in different environments, self-corrects over time.

• Higher development cost, but better performance, lower maintenance.

? Strategic Decision-Making: When to Include All-State Estimation

Including all-state estimation should be a strategic decision, not just a technical one. Consider the following:

• Is the system safety-critical? (e.g., medical devices, autonomous vehicles)

• Will the system operate in dynamic or uncertain environments?

• Are there constraints on sensors, wiring, or maintenance?

• Does long-term optimization matter more than short-term cost?

If the answer to one or more of these is “yes,” all-state estimation likely justifies its cost — even in budget-sensitive projects.

? Closing Thoughts

All-state estimation is more than an academic technique — it’s a practical lever for performance, reliability, and cost-efficiency. Whether you're estimating for profit or budget, understanding the trade-offs involved in implementing state estimation is essential.

For developers and project managers alike, the key is to:

• Recognize when state estimation adds strategic value

• Communicate that value in terms of business outcomes

• Use the right level of estimation complexity for the project's goals