Signal-Processing

Introduction Infinite Impulse Response (IIR) filters are the workhorses of real-time DSP systems. They offer superior frequency selectivity with fewer coefficients compared to FIR filters, making them ideal for resource-constrained embedded applications. However, every DSP engineer who has implemented IIR filters in fixed-point systems has encountered the dreaded instability: filters that work perfectly in floating-point simulation suddenly oscillate, saturate, or produce garbage output when deployed to hardware. This isn’t just academic—it’s a production-stopping problem that has derailed countless projects. The issue stems from the fundamental tension between IIR filters’ recursive nature and the limited precision of fixed-point arithmetic. In this article, we’ll dissect exactly why this happens and provide practical solutions you can implement today. ...

Introduction Every DSP engineer has faced this scenario: you implement a textbook FFT-based tone detector, validate it with synthetic signals, and watch it fail spectacularly when deployed on real-world data. The culprit? Spectral leakage – that subtle but devastating artifact that transforms clean frequency bins into misleading spectral smears. While spectral leakage is well-documented in theory, its practical impact on tonal detection systems is often underestimated until it causes false alarms, missed detections, or incorrect frequency measurements in production systems. ...

Introduction Many signal processing workflows are built through experimentation. Engineers inspect spectra, adjust parameters, and repeat analysis until the output appears satisfactory. While this approach can work for exploratory research, it often produces unstable pipelines in production systems. Small changes in signal conditions may produce dramatically different results. Deterministic DSP pipelines address this problem by structuring signal analysis and decision logic explicitly. What This Article Covers This article explains: why trial-and-error DSP workflows fail what deterministic DSP pipelines are how signal characterization enables automation how verification improves reliability Trial-and-Error DSP Workflows In many engineering environments, signal processing pipelines evolve incrementally. ...

Introduction Tonal interference appears in many engineering measurement systems. Switching regulators introduce narrowband spurs, rotating machines produce harmonic vibration components, and electromagnetic coupling injects periodic interference into sensor signals. These narrowband spectral components are often referred to as tones. Even when their amplitude is small, they can significantly degrade measurement accuracy or corrupt downstream signal processing pipelines. Detecting these tones reliably is therefore a fundamental step in many DSP workflows. ...

Introduction Narrowband tonal interference appears in many real-world DSP systems. Common sources include: switching power supply spurs rotating machinery harmonics clock leakage in mixed-signal electronics EMI coupling in sensor pipelines These tones contaminate measurements and often degrade downstream signal processing. A typical engineering response is simple: compute a PSD find the largest spectral peak insert a notch filter While this method works for clean signals, it often fails in realistic environments where noise, drift, and spectral variance dominate. ...