Performance Trending What to Track and Why

Ground station performance is not a single number—it’s a set of signals that indicate whether your links are healthy, predictable, and improving over time. Performance trending means collecting the right measurements across RF, baseband, network, and operations, then analyzing changes so you can catch degradations early, validate upgrades, and explain outcomes to customers and mission teams.

What Performance Trending Means
Why Trending Matters for Ground Stations
Core Metrics: Signal Quality and Link Margin
RF Chain Health Metrics
Baseband and Protocol Metrics
Antenna Pointing and Tracking Metrics
Data Delivery and Network Metrics
Operations Metrics: Scheduling and Incident Signals
How to Build a Trending Program
How to Use Trends to Drive Decisions
Performance Trending FAQ
Glossary

Performance trending is the ongoing practice of measuring link and station behavior over time to understand what “normal” looks like and detect meaningful change. It answers questions like:

Are we losing link margin compared to last month?
Are passes getting shorter because acquisition is slower?
Did a hardware swap actually improve throughput?
Is performance different by satellite, band, elevation, or weather?

The goal is to turn raw telemetry into actionable insight—before customers notice a problem.

Link issues often start as small degradations: a slowly failing LNA, a connector with water ingress, a drifting reference oscillator, or a tracking bias that grows over weeks. Trending helps you catch these early because the change is visible before it becomes a clear outage.

Trending also supports accountability. When you run a fleet of ground stations, you need consistent ways to compare sites, validate maintenance impact, and explain performance to customers with data rather than anecdotes.

Core Metrics: Signal Quality and Link Margin

These are the most universal signals to trend because they reflect the total RF and baseband chain:

Received signal level: carrier power or input level at key points in the chain (with enough context to compare consistently).
SNR / C&N0 / Eb/N0: signal-to-noise indicators that correlate strongly with decoding success and throughput.
BER / FER: bit and frame error rates before and after forward error correction, where available.
Link margin: estimated headroom above the minimum required for the chosen modulation and coding.
Fade indicators: correlations between signal quality and weather, elevation angle, or known interference windows.

Trend these per pass and normalize by factors like elevation angle and expected spacecraft EIRP so comparisons remain meaningful.

RF Chain Health Metrics

RF issues often show up as slow drift. Useful RF-chain trends include:

LNA health proxies: noise floor changes, gain changes, or unexpected temperature correlations.
Converter stability: frequency offset drift, spurs, or phase noise indicators if measured.
Amplifier behavior: output power vs commanded, reflected power, temperature, and any foldback events.
Switching and routing: insertion loss changes after RF switch paths, waveguide work, or feed changes.
Environmental measurements: cabinet temperature, humidity, radome/rain sensor states, and power supply stability.

The most valuable RF trends are the ones tied to specific failure modes you can act on.

Baseband and Protocol Metrics

Baseband trends help distinguish “RF is bad” from “the modem and protocol stack are unhappy.” Track:

Lock events: acquisition time, loss-of-lock counts, and reacquisition frequency.
Decoder performance: FEC statistics, CRC failures, dropped frames, and buffer underruns/overruns.
ModCod selection: the modulation/coding used over time (especially for adaptive systems).
Throughput efficiency: delivered payload throughput vs theoretical for the chosen waveform and symbol rate.

These metrics become especially important when you support multiple spacecraft vendors or multiple waveform profiles.

Antenna Pointing and Tracking Metrics

Tracking problems can look like “low SNR” unless you trend the right signals. Useful metrics include:

Acquisition time: time from AOS to stable lock; slow acquisition often indicates pointing bias or scheduling delays.
Pointing error: estimated az/el error where measurement exists (step-track residuals, monopulse error, or equivalent).
Peak vs average signal: peak carrier vs average during the pass; a widening gap can indicate tracking instability.
Mechanical health: servo current, wind stow events, limit hits, backlash indicators, and encoder drift.

Segment by elevation angle because low-elevation performance often behaves differently and is more exposed to multipath and obstructions.

Data Delivery and Network Metrics

Customers often experience “performance” as data delivery, not RF metrics. Track:

Time-to-data: time from end of pass (or from frame receipt) to data availability in the customer’s system.
Delivery success rate: percentage of scheduled passes that produced complete, usable datasets.
Packet loss and retransmits: on internal backhaul and customer handoff links.
Storage and pipeline latency: queue depth, processing time, and failure counts in ingestion workflows.
Capacity headroom: bandwidth utilization and whether congestion correlates with delays.

This layer is where RF engineering meets product reliability.

Operations Metrics: Scheduling and Incident Signals

Operational trends reveal whether performance issues are actually process issues:

Pass success rate: completed vs scheduled, with failure reasons categorized consistently.
Operator interventions: frequency of manual actions required to complete contacts.
Alarm rates: recurring alarms that correlate with lower performance or higher failure probability.
Mean time to detect and resolve: detection latency and time-to-recovery for link-impacting incidents.
Maintenance outcomes: before/after deltas tied to specific work orders.

The key is consistent taxonomy. If every failure is logged differently, trending becomes noise.

A practical program usually starts small and expands:

1) Define “top outcomes”: uptime, pass success, delivered throughput, time-to-data.
2) Pick a minimal metric set: SNR (or equivalent), BER/FER, acquisition time, throughput, delivery latency, failure reason.
3) Normalize the data: by satellite, band, elevation, modulation, and weather where possible.
4) Set baselines: establish normal ranges and seasonal patterns for each site and band.
5) Add alert thresholds: based on deltas from baseline, not just absolute values.

The most successful trending programs focus on metrics that directly lead to action.

How to Use Trends to Drive Decisions

Trending is useful when it changes behavior:

Preventive maintenance: replace components when trends indicate drift, not after a failure.
Capacity planning: add antennas, backhaul, or compute before congestion shows up as customer impact.
Upgrade validation: prove that a new LNA, modem profile, or tracking model improved performance with before/after comparisons.
Customer reporting: explain variability (weather, elevation, spacecraft mode) with data rather than speculation.
Root cause analysis: narrow the problem domain quickly—RF vs tracking vs baseband vs delivery pipeline.

What’s the single most important metric to trend?

If you can only pick one, trend a signal-quality indicator (SNR, C&N0, or Eb/N0) per pass, normalized by elevation angle. It’s often the earliest sign of link degradation and correlates strongly with decode success and throughput.

How do I avoid false alarms in trending?

Compare against a baseline for the same satellite, band, and elevation range. Many “changes” are normal variation driven by geometry, spacecraft mode, or weather. Delta-based alerts (change from baseline) are usually more reliable than static thresholds.

Why is acquisition time so useful?

Acquisition time compresses multiple systems into one signal: scheduling readiness, antenna pointing accuracy, receiver configuration, and RF conditions. It often catches problems earlier than a simple “pass failed” metric.

Should trending focus on averages or worst cases?

Both. Averages show slow drift and improvement; worst cases reveal operational risk. For customer reliability, tail behavior (the bad 5–1%) often matters more than the mean.