UWB Ranging Node: Precise ToF with Timestamps and Secure Ranging
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A UWB ranging node delivers reliable distance by running a controlled timestamp loop (typically DS-TWR), then reducing error with clock/antenna-delay management and reporting CIR-based quality flags so upper layers can degrade safely under NLOS or power events.
What This Page Solves: Definition, Boundary, Success Metrics
A UWB ranging node is defined by a closed engineering loop: transmit/receive UWB pulses, capture reliable timestamps (ToF), compute distance with DS-TWR, attach quality evidence (CIR + flags), and enforce secure ranging (auth/STS). System-level RTLS engines and anchor deployment are explicitly out of scope.
Definition (as a closed loop, not a glossary)
UWB ranging node = UWB transceiver (RF/BB + CIR + timestamp registers) + host MCU/SoC (TWR sequencing, scheduling, logging) + clocking (XTAL/TCXO/PLL + drift/CFO handling) + security (auth + secure ranging primitives such as STS) + diagnostics (CIR/first-path confidence, NLOS indicators, error codes) + optional IMU hooks (event trigger and time-alignment only).
- Primary objective: stable distance under real multipath/NLOS conditions, not just a “mean LOS number”.
- Primary artifact: distance + quality flags + logs that allow field debugging and production control.
Boundary (what must NOT be expanded here)
Allowed: ToF (SS/DS-TWR), timestamp chain, clock drift/CFO, CIR interpretation, antenna delay calibration, secure ranging/auth, NLOS handling, node-local logs and verification.
Banned: RTLS location engines (AoA/AoD/TDoA), anchor placement, multi-anchor network sync, gateway aggregation, and unrelated wireless stacks.
Success metrics (measurable, comparable, and decision-driving)
- Accuracy vs tail: mean LOS error is not enough; track p95/p99 (or worst-case bins) and failure rate.
- Update rate & duty cycle: the chosen Hz sets airtime, collision probability, and peak current stress.
- Power reality: average current must be paired with TX/RX burst peak and brownout evidence.
- Robustness: multipath/NLOS requires quality flags; otherwise “pretty numbers” hide wrong distance.
- Security level: link encryption alone is not secure ranging; relay/replay resistance must be designed explicitly.
| Typical target | Engineering meaning (what must be controlled) |
|---|---|
| 10 cm @ LOS | Requires antenna delay calibration and stable timestamp detection; otherwise fixed bias dominates the number. |
| p95 < 30 cm | Requires CIR/first-path confidence and NLOS flags; tail behavior is driven by multipath and threshold instability. |
| 1 Hz vs 10 Hz | Higher Hz increases airtime and peak current exposure; scheduling and retries must be bounded and logged (avoid “silent failure”). |
| Stable over temperature | Requires clock drift/CFO strategy (XTAL vs TCXO choice + runtime correction) and temperature-indexed calibration points. |
| Secure ranging enabled | Requires STS/auth workflow and anti-replay/anti-relay considerations; plan for latency/power overhead and versioned key policies. |
| Peak current bounded | Requires verified supply headroom during TX bursts; store brownout/reset causes and timestamp anomalies as diagnostics evidence. |
Block-diagram style; minimal labels; key artifacts: timestamps, CIR, quality flags, secure ranging, logs.
Requirements Decomposition: Use-Case → KPIs → Error-Budget Entry
Engineering requirements must translate into an error budget. The goal is not to list scenarios, but to map each scenario to KPI combinations and then to the dominant error buckets: timestamp chain jitter, clock drift/CFO, multipath/NLOS, antenna delay, and digital thresholds.
Step 1 — Bucket scenarios by “ranging behavior” (not by product category)
Scenarios often look different on the surface, but share the same ranging pain points. Group them by the stresses they create:
- Short-range high certainty: close interaction and confirmation distances. Dominant risk: fixed bias and detection thresholds.
- Medium-range low power: periodic ranging with strict battery budget. Dominant risk: duty-cycle trade-offs and missed first-path.
- Dynamic motion / fast updates: moving nodes or rapid approach. Dominant risk: scheduling, collisions, and unstable CIR under motion.
- Industrial multipath / NLOS: metal reflections and human blocking. Dominant risk: tail behavior (p95/p99) and false confidence.
Step 2 — Split KPIs into mean, tail, and failure rate (the “hidden” spec)
A single accuracy number rarely predicts field behavior. The KPI set should include:
- Mean accuracy (LOS): useful for baseline, but easy to “win” while still failing in real environments.
- Tail stability (p95/p99): where multipath/NLOS dominates; this is what drives user experience and safety margins.
- Failure rate: timeout/no first-path/invalid timestamps. A system with “good mean” but high failure rate is not robust.
- Update rate & duty cycle: drives airtime, retries, and peak current exposure; needs a bounded scheduler and retry policy.
- Security requirement: secure ranging adds overhead; treat it as a KPI constraint, not an afterthought.
- “Accuracy = mean” → hides tail spikes; p95/p99 and failure rate must be tracked.
- “Encrypted link = secure ranging” → does not stop relay/mafia fraud; secure ranging primitives must be designed.
- “Distance jumps = algorithm issue” → often threshold/CIR/antenna delay/clock drift evidence is missing.
Step 3 — Build an error-budget entry model (five buckets + observable evidence)
Once KPIs are set, the shortest path to stability is to map symptoms to the dominant bucket and collect evidence that confirms it. The five-bucket model below is designed to remain node-local and testable.
- Bucket A — Timestamp chain jitter: SFD/STS detection stability, timestamp register variance, first-path confidence behavior.
- Bucket B — Clock drift/CFO: temperature-correlated distance drift, residual CFO estimates, PLL/oscillator sensitivity.
- Bucket C — Multipath/NLOS: CIR shape changes, peak/first-path ratio, path ambiguity flags and tail spikes.
- Bucket D — Antenna delay & assembly: fixed bias, device-to-device offset, enclosure/hand effects shifting delays.
- Bucket E — Digital thresholds & policies: sudden jumps after config changes, threshold-induced first-path switching, retry explosions.
The next chapters expand each bucket: timestamp math and DS-TWR (H2-3), error budget mechanics (H2-4), clocking (H2-5), RF/antenna delay (H2-6), parameter choices (H2-7), and validation/diagnostics (H2-11).
Single-node viewpoint: map requirements into five measurable error buckets and chapter pointers.
Ranging That Actually Ships: ToF and SS/DS Two-Way Ranging
This chapter keeps ranging theory at the point where a node can implement it: packets on air, hardware timestamps, and how DS-TWR reduces clock-bias sensitivity compared with SS-TWR. Multi-anchor TDoA and network synchronization remain out of scope.
ToF in practice: a distance estimate driven by detection events
Time-of-Flight (ToF) converts propagation time into distance. In a real node, ToF is not measured as a continuous waveform delay; it is built from discrete detection events (SFD or STS) that produce hardware timestamps. When the detection point is unstable, distance becomes unstable even if the link “looks fine”.
- What matters: where the timestamp is latched (event definition) and how repeatable that event is.
- What does NOT matter: MCU read latency (reading registers later does not change the latched time).
SS-TWR vs DS-TWR: a decision view (bias sensitivity vs overhead)
SS-TWR uses fewer messages, but is more exposed to uncompensated clock effects. DS-TWR adds one message and forms a combination of timestamps that better cancels clock bias terms.
- SS-TWR (2 messages): lower airtime and lower peak exposure, but higher sensitivity to clock drift and processing asymmetry.
- DS-TWR (3 messages): higher airtime, but improved robustness against clock bias and better repeatability under temperature variation.
- Engineering trade: DS-TWR typically wins for tail stability (p95/p99), while SS-TWR can fit ultra-tight duty-cycle cases.
TDoA requires network-level synchronization and multi-anchor timing control. Only node-local TWR is covered here.
Where timestamps are taken: TX/RX edges and the register path
Hardware timestamps are typically latched at well-defined PHY events: TX timestamp at a transmit boundary and RX timestamp at a stable detection point (SFD or STS correlation peak). These timestamps enter the ranging calculation; the MCU reads and logs them for verification and debugging.
- Stable detection: SFD/STS stability dominates ranging repeatability.
- Evidence to log: per-exchange timestamps, fail codes, and at least a compact CIR quality summary.
Block-timeline style; minimal labels; hardware-latched TX/RX timestamps (SFD/STS) are the inputs.
Timestamp Chain & Error Budget: Where “Certainty” Gets Lost
When a link is up but distance drifts or jumps, the root cause is usually not the ToF formula. The cause is an end-to-end timestamp chain plus stacked error sources: clock terms, channel terms, and implementation terms.
The timestamp chain (RX front-end → register latch)
A ranging node turns an RF waveform into a timestamp through a deterministic chain: RF front-end → correlator / matched filter → SFD/STS detection → digital counter latch → timestamp register → MCU log. The distance result inherits the stability of the detection and latch points.
- Stable latch: repeatable SFD/STS detection produces repeatable timestamps.
- Unstable latch: first-path ambiguity and threshold switching produce “distance jumps”.
Error buckets (clock / channel / implementation)
Field behavior becomes explainable when errors are grouped into buckets with observable evidence. Each bucket suggests what to log and what to tighten first.
- Clock-related: ppm/temperature drift, CFO residuals, PLL phase noise → shows as temperature-correlated drift and update-rate sensitivity.
- Channel-related: multipath/NLOS, pulse distortion → shows as tail spikes, first-path confidence collapse, and CIR shape changes.
- Implementation-related: antenna delay, calibration, detection thresholds, temperature effects → shows as fixed bias, device-to-device spread, or sudden jumps after config changes.
Practical error-budget template (measure → log evidence → reduce)
An error budget must be actionable. The template below ties each error source to symptoms, log fields, measurement methods, and reduction levers. This keeps optimization focused and prevents “blind tuning”.
| Error source | Field symptom | Evidence to log | How to measure | How to reduce |
|---|---|---|---|---|
| Clock drift / CFO | slow distance drift vs temperature | temperature, CFO estimate/residual, timestamp variance | temperature sweep, hold distance constant, observe drift | TCXO or correction strategy, calibration points, bounded updates |
| Multipath / NLOS | tail spikes, “good mean but bad p99” | CIR summary, first-path confidence, peak/first-path ratio | LOS vs NLOS A/B tests, metal/human blocking trials | quality flags, threshold tuning policy, NLOS downgrade behavior |
| Antenna delay | fixed offset, enclosure/hand effect shift | per-unit offset, antenna version, mechanical configuration | golden reference, swap enclosure conditions, compare offsets | calibrate antenna delay, control assembly, lock RF BOM variants |
| Detection thresholds | sudden “distance jumps” after config changes | threshold settings, first-path index, retries/fail codes | parameter sweeps, controlled SNR tests, replay logs | versioned configs, bounded adaptive policies, stable detection points |
DS-TWR reduces clock-bias terms, but does not eliminate channel ambiguity (multipath/NLOS) or implementation bias (antenna delay). Those must be handled with evidence, calibration, and stable detection policy.
Stacked view; minimal labels; shows Clock / Channel / Implementation share under LOS, low-SNR, and NLOS.
Clocking (Node-Local): XTAL/TCXO/PLL, CFO Correction, Drift & Aging
Accurate ranging depends on a clean, observable, and correctable timebase. DS-TWR reduces some bias terms, but timestamp stability still degrades when oscillator drift, CFO residuals, and PLL noise increase. Only node-local clocking is covered (no PTP/SyncE or network synchronization).
Why a “correctable clock” matters more than a “perfect clock”
A ranging node converts RF detection events into hardware timestamps. The distance result inherits short-term jitter (timestamp noise) and long-term drift (temperature and aging). A clock strategy is strong when it provides stable behavior and a clear correction loop (evidence → estimate → small adjustment).
- Short-term: PLL phase noise and timestamp latch repeatability drive p95/p99 distance stability.
- Long-term: temperature drift and aging drive slow distance drift and device-to-device spread.
- Key point: MCU read latency does not change the latched timestamp; detection/latch stability does.
XTAL vs TCXO: choose by constraints (accuracy / boot time / power)
XTAL and TCXO are not “better vs worse”; they are different constraint optimizers. The selection should be driven by the target ranging stability, warm-up/lock time, and power budget for repeated measurements.
- XTAL path: lowest cost/power, but drift and warm-up behavior must be managed by calibration and bounded correction.
- TCXO path: improved temperature stability and repeatability, typically better tails, with added cost and startup considerations.
- PLL role: a cleaner / more stable derived clock can reduce timestamp jitter, but lock and spurs must be verified.
How CFO and drift still impact DS-TWR timestamps
DS-TWR combines multiple TX/RX timestamps to reduce clock-bias sensitivity, but residual CFO/drift still affects the effective timing of detection events and the stability of the timestamp counter. In field data this often shows up as: stable mean distance but degraded tail stability (p95/p99) and temperature-correlated drift.
- CFO residual: small frequency error that remains after estimation/correction; appears as slow drift or update-rate sensitivity.
- Temperature drift: distance slowly walks with temperature even when geometry is fixed.
- Aging: long-term shift that accumulates over months; shows as calibration “going stale”.
Node-local correction strategy (boot → temperature points → runtime trim)
A practical correction plan is layered, with explicit evidence and bounded adjustments. The goal is to improve repeatability without chasing noise.
- Boot calibration: establish a usable baseline after power-up; log the calibration version and environmental conditions.
- Temperature-point calibration: build a temperature-to-correction map; verify hysteresis and warm-up behavior.
- Runtime micro-trim: small, bounded updates using CFO evidence or statistical residuals; avoid large steps that amplify tails.
Only node-local timing is covered. Multi-node synchronization (PTP/SyncE) and network timing belong to the Edge Timing & Sync page.
Debug table: field symptom → clock evidence → first action
Clock issues often masquerade as channel issues. The table below ties visible symptoms to evidence that should be logged, and a first corrective action that is safe and bounded.
| Field symptom | Clock evidence to check | Likely interpretation | First action (bounded) |
|---|---|---|---|
| Distance drifts with temperature | temperature vs distance correlation, CFO residual trend, timestamp variance | temperature drift dominates; correction map is missing or stale | add temperature-point calibration; verify warm-up window; log calibration version |
| p95/p99 worsens at higher update rate | PLL lock status (if available), timestamp jitter proxy, CFO residual at duty-cycle edges | short-term jitter / lock behavior affects detection repeatability | verify lock/warm-up; bound runtime trim; avoid aggressive re-lock toggling |
| Large device-to-device spread | per-unit offsets, calibration status, temperature behavior vs unit ID | mix of clock drift and implementation bias; antenna delay may dominate | separate clock vs antenna delay by controlled tests; freeze RF BOM variants (see H2-6) |
| Distance jumps after firmware/config change | correction parameters, thresholds, CFO settings, calibration version | correction step is too large or inconsistent between modes | version parameters; clamp step size; rollback to last known-good config for A/B |
Minimum “clock pack” to log per ranging exchange: temperature, correction version, CFO estimate/residual (if available), and timestamp variance proxy.
Flow + control-loop blocks; minimal labels; text ≥ 18px; no network-level sync.
RF Front-End & Antenna: Matching, Group Delay, Antenna Delay, Assembly Consistency
When the same UWB IC shows different distance bias after antenna or enclosure changes, the root cause is usually a mix of antenna delay, pulse distortion (group delay ripple), and chassis/ground effects that reshape the CIR. This chapter stays UWB-specific and focuses on evidence and repeatable build rules.
RF impact point: first-path detectability and CIR stability
UWB ranging is not “signal strength measurement”. It is dominated by whether the first path can be detected consistently. Any RF change that reshapes the pulse or shifts the detection threshold can move the detected first path, creating distance jumps or heavier tails.
- Evidence: first-path confidence, peak/first-path ratio, first-path index stability, retry/fail codes.
- Field signature: “mean looks OK” while p95/p99 breaks under NLOS/metal/human proximity.
Matching + bandwidth: why group delay ripple matters for pulses
A matching network can improve return loss, but the time-domain pulse can still be distorted when group delay ripple is large. Distortion broadens or shifts correlation peaks, making the detection point more sensitive to thresholds and multipath.
- Return loss: affects effective energy and SNR margin.
- Group delay ripple: reshapes pulses, changes correlation peak geometry, and affects first-path repeatability.
Antenna delay: why “fixed distance bias” appears after changes
Antenna delay is a hardware timing offset contributed by the antenna, feed structure, and the local reference environment. It often manifests as a nearly constant distance offset across the operating range. Changing antenna type, placement, enclosure spacing, or ground reference can shift this delay and therefore shift measured distance.
If distance bias moves as a whole after an antenna/enclosure change, treat antenna delay and assembly consistency as first suspects. Use a golden reference and controlled A/B swaps to separate delay bias from channel effects.
Enclosure, routing, and ground reference: how CIR gets reshaped
Metal proximity, cable routing, and ground reference changes can create additional reflections and modify the apparent first-path peak. The effect is visible in CIR signatures and first-path confidence collapse. RF co-existence issues that matter for ranging are the ones that move detection behavior or increase jitter, not generic EMC topics.
- Chassis proximity: can raise multipath components and shift first-path detectability.
- Digital noise coupling: can increase timestamp jitter proxies and reduce confidence.
- UWB-focused coexistence: look for neighbor activity correlated with confidence drops or retries.
Build rules: freeze variables that change delay and CIR
Repeatability requires assembly-level consistency. Freeze the variables that shift antenna delay or reshape the near-field environment.
- Antenna geometry: type, placement, keep-out, and reference ground layout.
- Feed & routing: length, bends, and proximity to noisy digital rails.
- Enclosure spacing: plastic thickness, metal clearance, and mechanical tolerance stack-up.
- Version control: lock RF BOM variants and record build identifiers in logs.
Three mini-panels: group delay ripple, antenna delay bias, chassis/ground multipath; minimal labels; text ≥ 18px.
This chapter stays UWB-specific. Generic EMC compliance strategies belong to the EMC / Surge for IoT page.
PHY Parameters: Preamble, PRF, Data Rate, SFD/STS — Tune for Measurability
PHY parameters are not “faster is better”. They should serve two goals: detection reliability (stable timestamps and CIR capture) and diagnostic visibility (node-side evidence that remains comparable across builds). This chapter stays strictly on node-local measurability and production profiles.
Measurability first: the hidden cost of “aggressive” settings
An aggressive configuration can look great in average throughput but fail in the field because retries, missed detections, and tail instability dominate power and latency. A robust profile maximizes successful exchanges and keeps quality metrics interpretable.
Preamble length: reliability margin vs duty-cycle efficiency
Preamble is a practical knob for detection margin. Longer preambles usually improve robustness under weak links, body blockage, or metal-rich multipath, while shorter preambles reduce air-time but tighten the detection margin.
- Longer preamble: more correlation energy → fewer misses → better p95/p99 stability under stress.
- Shorter preamble: higher potential update rate, but missed detections and retries can erase gains.
- Logging cue: track retry rate and first-path confidence when changing preamble.
PRF: multipath separability and first-path behavior (not just “resolution”)
PRF changes the timing structure that the receiver uses to detect and correlate signals. In practice it influences how multipath components appear in CIR and how stable the first-path decision becomes across environments.
- Multipath-rich scenes: PRF interacts with correlation behavior and can change peak/first-path ratios.
- Threshold sensitivity: a “marginal” setting can turn small CIR changes into large first-path jumps.
- Do not tune alone: validate PRF together with preamble and SFD/STS settings to keep metrics comparable.
Data rate: update rate is only real when detection success stays high
Higher data rate reduces packet time, but also tightens the margin for reliable detection and CIR capture. When the channel degrades, aggressive data rate often shifts the failure mode from “slightly noisy” to “missed or repeated”.
- High data rate: less air-time per exchange, but tails often worsen in weak links and NLOS.
- Moderate data rate: better measurability; logs remain stable and comparable across deployments.
- Proof point: compare p95/p99 distance and retry rates, not only mean update rate.
SFD and STS: stable timestamp points + a bridge to secure ranging
SFD is where practical timestamp capture becomes stable. STS (Scrambled Timestamp Sequence) reinforces measurability by supporting trustworthy timestamp sequences and enabling secure-ranging hooks. This chapter only covers the node-visible effects and logging requirements—security protocol details belong to later chapters.
- SFD role: a consistent detect event supports repeatable timestamp latching.
- STS role: ties to secure ranging; changing STS behavior should be versioned and logged.
- Logging cue: record STS enable state, error codes, and quality flags for each exchange.
Node-side CIR quality metrics (only what the node can compute)
Choose a small set of CIR-derived indicators that remain stable across builds. These metrics should explain failures and correlate with field symptoms without requiring a full RTLS engine.
- Peak amplitude (overall margin indicator)
- First-path confidence (detectability / stability)
- Peak-to-first ratio (multipath dominance proxy)
- Retry / fail codes (operational health)
Production consistency: define and freeze a default profile
A production profile should keep quality metrics comparable and reduce “mystery variability”. Freeze what changes measurability, guard what can be tuned, and leave only bounded adaptivity.
| Profile layer | What belongs here | Why | Required logging |
|---|---|---|---|
| Freeze (factory) | core preamble/PRF/data rate set, SFD/STS behavior, quality metric definitions | keeps measurability stable and logs comparable across devices and time | profile ID, build ID, firmware version |
| Guard (field, with limits) | bounded preamble adjustments, bounded data-rate changes under poor links | allows controlled robustness tuning without breaking diagnostic comparability | reason code, before/after profile, time window |
| Adaptive (node, bounded) | small step changes driven by quality flags, with hard ceilings | prevents runaway complexity while responding to real channel stress | step size, trigger flag, upper/lower bounds |
Every profile is a trade: range ↔ update rate ↔ power, with NLOS robustness typically increasing cost in at least one axis. Use quality metrics to keep the trade measurable.
Causal chain blocks; minimal labels; text ≥ 18px; no RTLS engines.
This chapter does not cover RTLS engines (TDoA/AoA/AoD), anchor deployment, or network time sync. It only covers node-local measurability.
IMU Hooks (No Positioning Engine): Triggers, Time Alignment, Minimal Fusion Interface
IMU integration at the node should not “explode system complexity”. The IMU’s job is to provide triggering, motion state, and time-alignment confidence so upper layers can consume better-quality ranging data. This chapter explicitly excludes AoA/AoD localization engines.
Why add IMU at all: three node-local roles
A ranging node benefits from IMU information even without any localization engine. The value is practical: reduce wasted exchanges, improve interpretability, and gate unstable conditions.
- Trigger: motion or shock interrupts can start short ranging bursts instead of always-on updates.
- State: a simple motion-state machine informs duty-cycle and “trust level” tags.
- Hint: provide flags that help upper layers filter out events likely degraded by rapid motion.
Trigger strategy: bounded bursts instead of constant ranging
Use IMU interrupts and thresholds to gate ranging. The policy should be predictable, bounded, and versioned. A safe design uses a small set of trigger reasons and a strict ceiling for maximum burst rate.
- Ceiling: cap the burst window and max update rate.
- Versioning: log the trigger reason and policy version.
- Separation: IMU changes duty-cycle and flags, not core PHY profile definitions.
Node-local time alignment: IMU interrupt timestamp vs UWB timestamp
IMU events are timestamped in the MCU domain, while UWB timestamps are captured in the UWB timebase. The goal is not global absolute time—it is a bounded, observable alignment error that upper layers can trust.
- Record offsets: measure and record a stable mapping offset between MCU-time and UWB-time domains.
- Expose confidence: publish an alignment_quality flag so consumers know if alignment is tight or coarse.
- Avoid overreach: do not introduce network timing or multi-node sync here.
Minimal consumable interface: what upper layers should receive
Keep the interface small and stable. The node should output a minimal set that upper layers can consume without binding to a specific localization engine.
“Coordinates” and localization solver outputs are out of scope. The interface carries only state and confidence signals.
Complexity control: three rules that prevent “system blow-up”
IMU hooks are valuable only when they remain predictable and bounded. These rules keep integration stable across firmware releases.
- Rule 1: IMU changes duty-cycle and flags, not the definition of the PHY profile itself.
- Rule 2: every policy has hard bounds (max burst time, max update rate, max power).
- Rule 3: every state transition is logged (reason + version) for post-mortem analysis.
Single-node blocks: triggers, alignment mapping, minimal outputs; text ≥ 18px; no AoA/AoD engine.
This chapter defines IMU hooks and minimal outputs only. AoA/AoD fusion and positioning engines are intentionally excluded.
Crypto/Auth for Secure Ranging: Replay, Relay, Session Keys, and STS in Practice
Encrypted communication is not the same as secure ranging. Secure ranging must protect freshness, time-of-flight integrity, and configuration integrity against attackers who can still pass “normal crypto” checks. This chapter focuses on node-local, ranging-related mechanisms and evidence.
Why “encrypted packets” can still produce fake distance
Encryption protects confidentiality and integrity of messages. Distance fraud attacks can still succeed if an attacker can replay old valid exchanges, relay challenges to a real device, or force a downgrade to weaker ranging settings. Secure ranging requires binding the ranging-critical fields to freshness and authenticated state.
Threat model (node-centric): what can happen even with crypto
Secure ranging is about the attacker’s ability to affect the time-of-flight decision. The most common classes below should be treated as distinct engineering problems, each with different evidence signals.
- Replay: old valid messages are injected again. Symptoms: repeated counters/nonces, “valid decode” but stale session.
- Relay (mafia fraud): challenges are forwarded to a real device. Symptoms: timing consistency anomalies and unusual processing gaps.
- Impersonation: a fake node claims a valid identity. Symptoms: auth failures, key-version mismatch, STS validation errors.
- Downgrade: the link is forced into weaker settings (e.g., STS disabled). Symptoms: config negotiation changes and “compat mode” counters.
Engineering landing zone: STS + challenge/response (freshness binding)
Practical secure ranging uses two anchors: freshness (nonce/counter) and binding (ranging-critical fields are authenticated under the session state). STS supports trustworthy timestamp sequences and reduces the chance that a distance is accepted without a valid, session-bound signal structure.
Bind freshness (nonce/counter), session_id, peer_id, key_version, STS mode/config summary, and a compact digest of ranging-critical fields so distance cannot be accepted under stale or downgraded state.
Identity and keys (minimal set, ranging-related only)
Keep the node implementation minimal but robust. The goal is not a full enterprise PKI stack; it is a small, versioned set of identity and key state that can prevent replay and downgrade at ranging time.
- device_id / peer_id: stable identifiers used to bind sessions and logs.
- session_id: short-lived session context; every ranging exchange references it.
- key_version: supports rotation; reject stale versions and log mismatches.
- anti-rollback: prevent firmware/config from reverting to weaker security levels; keep monotonic version evidence.
Performance impact: latency, power, and configuration complexity
Security typically adds steps and compute. The right engineering move is to treat security level as a profile dimension: measure the cost in extra air-time, CPU cycles, and state transitions, then cap the complexity with a frozen default and bounded policies.
- Latency: extra handshake steps reduce achievable update rate under tight duty-cycle limits.
- Power: compute + air-time increase; the average can rise sharply in burst-heavy modes.
- Complexity: more configurations require versioning and logs to keep diagnosis possible.
Decision table: Threat → Protection → Cost (plus evidence)
Use a simple engineering table to decide what to enable by default and what to guard. The evidence column is critical: without logs and flags, secure ranging failures look like “random link issues”.
| Threat | Protection (node-level) | Cost (engineering) | Evidence to log |
|---|---|---|---|
| Replay | freshness (nonce/counter) + session binding; reject stale state | small state storage; strict counter handling | session_id, nonce/counter, replay_reject_code |
| Relay | challenge/response with tight time consistency; STS mode enforced | extra steps; tighter timing constraints; more diagnostics | timing_anomaly_flag, auth_fail_code, sts_fail_code |
| Impersonation | peer identity binding + session key derivation; reject key_version mismatch | identity state management; rotation handling | peer_id, key_version, auth_fail_code |
| Downgrade | security_level lock + anti-rollback; reject weaker configs unless explicitly guarded | config policy + version monotonicity | downgrade_flag, policy_version, config_digest |
Block diagram only; minimal labels; text ≥ 18px; no PKI/TLS deep-dive; no RTLS engine.
This chapter covers secure ranging mechanisms and node evidence only. It intentionally excludes PKI/TLS/cloud provisioning, anchors, and RTLS engines.
Low Power and Duty-Cycling (Node View): Wake Policies, Burst Scheduling, Peak Current, and Brownout Evidence
UWB peak current can be high. Battery life becomes controllable when duty-cycling is treated as a policy with hard bounds, and when power integrity issues are separated from “link issues” using node-side evidence (brownout flags, reset causes, and ranging-quality logs). This chapter does not dive into PMIC topologies.
Make battery life a policy: three knobs that stay measurable
A practical node design controls lifetime via three measurable knobs: interval (how often ranging runs), burst window (how long high-rate updates last), and event triggers (when to wake for ranging).
Duty-cycling modes: interval, burst, and event-driven scheduling
Use a small set of modes that can be reasoned about and logged. The most reliable field behavior comes from bounded policies rather than ad-hoc adjustments.
- Interval mode: predictable updates; easiest to budget.
- Burst mode: short high-rate window for interaction; must have a strict ceiling.
- Event mode: wake on IMU/button/external interrupt; best for low average power.
Always cap maximum burst duration, maximum update rate, and minimum cool-down time. Log the trigger reason and the policy version.
Energy budgeting that works in practice: “energy per exchange”
The most transferable budgeting method is to treat each ranging exchange (or burst) as a repeatable energy event. Once measured, lifetime becomes a controllable parameter: reduce exchange energy, reduce exchange count, or both.
- Measure: record active window duration and a representative current profile for the exchange.
- Count: log how many exchanges happen per hour/day, including retries.
- Control: tie policy to quality flags so retries do not silently dominate energy.
Peak current and power integrity: how droop turns into timestamp anomalies
Voltage droop during high-current TX/RX or compute windows can destabilize detection and timestamp capture. In the field, this often presents as “random distance jumps” or “unexplained retries” unless power evidence is logged.
- Droop → detect instability: SFD/STS decisions become threshold-sensitive; first-path becomes less stable.
- Droop → digital upset: timebase or state machines can reset/timeout, causing missing or inconsistent timestamps.
- Key separation: power issues must be proven via brownout/reset evidence, not guessed from link behavior.
Evidence logging: the minimum set to distinguish power vs channel vs policy
A small, consistent evidence set prevents “mystery bugs”. These fields should be logged alongside ranging quality flags and policy context, so failures can be bucketed correctly.
Minimal state machine (node-only): predictable scheduling and traceability
A minimal state machine makes behavior stable across releases and enables traceability. Each transition should be logged with a reason code, so the duty-cycle policy remains inspectable in real deployments.
- Sleep → Wake: by trigger_reason (IMU/button/interrupt) or interval timer.
- Wake → Burst: bounded by ceilings; exits on time or quality decline.
- Burst → Cooldown → Sleep: enforce minimum cool-down to avoid thermal/power stress.
Timeline blocks (no complex waveforms); text ≥ 18px; node evidence taps; no PMIC topology deep-dive.
This chapter focuses on node-level duty-cycling, peak-current symptoms, and evidence logging. PMIC/harvesting design details are intentionally excluded.
Verification & Debug: Reading CIR, Handling NLOS, Calibrating Offsets, and Controlling Production Consistency
The goal is field-proof evidence, not paper-perfect models. This chapter turns “range jitter” into repeatable buckets using CIR/diagnostics, then prevents fake confidence via detect→degrade→flag, and finally locks consistency with calibration records + fixture thresholds + a stable log schema.
Allowed: CIR/first-path metrics, thresholds, NLOS detect→degrade→flag, antenna-delay & temperature buckets,
fixture/golden unit, production thresholds, log schema, example MPNs for validation.
Banned: RTLS engines (TDoA/AoA/AoD), anchors deployment, network sync, cloud provisioning/PKI deep-dive,
PMIC topology/energy-harvesting deep-dive.
CIR/Diagnostics: the 5 metrics that carry the most evidence
The CIR is useful only when the node collects the same metrics under a frozen profile (preamble/PRF/SFD/STS/threshold settings). Start with a short, high-signal checklist that maps directly to failure buckets.
| Metric (node-side) | What it means (field) | Common bucket | Actionable next step |
|---|---|---|---|
| first_path_conf / FP confidence | How stable/credible the detected first path is | NLOS, heavy multipath, threshold drift | Freeze thresholds; compare LOS reference; enable NLOS flagging |
| peak_amp | Peak strength (use together with noise_floor) | Distance/attenuation, antenna mismatch | Check matching/ground/assembly; compare golden unit |
| noise_floor | Raised baseline often signals interference or coupling | Interference / digital coupling / threshold too low | Raise threshold cautiously; correlate with retries and power events |
| peak_to_first ratio | Large ratio suggests “first path is not the strongest” (typical NLOS) | Multipath / NLOS | Detect→degrade→flag; do not promise cm-level accuracy |
| threshold_id + hit rate | Too high: missed detections. Too low: false triggers | Config-induced jitter / “random” drops | Lock a default profile; version the threshold set |
Always log a short window under a frozen profile (e.g., N exchanges) before changing any threshold or PRF/preamble setting. Without freeze-and-sample, comparisons become invalid.
NLOS policy: Detect → Degrade → Flag (never output fake precision)
NLOS is not solved by “better math” inside a tag. The node must detect suspicion, reduce the promise (output contract), and expose quality flags so upper layers can behave safely.
| Detect (evidence) | Degrade (output contract) | Flag (what to report) | What to log |
|---|---|---|---|
| low first_path_conf + high peak_to_first | Lower update rate; increase averaging window; cap retries | nlos_flag=1, confidence tier (coarse) | first_path_conf, peak_to_first, retry_count, profile_id |
| unstable FP index (jumps) + rising noise_floor | Output “invalid / low confidence” instead of cm-level number | quality_flags: interference_suspect | noise_floor, threshold_id, fail_code |
| high retries + timing anomalies (with clean power evidence) | Switch to safer profile; limit burst duration | quality_flags: unstable_link | retry_count, fail_code, policy_version |
Calibration: separating fixed bias from environment-driven variance
Calibration should produce versioned artifacts that can be audited in the field. The key is to remove repeatable offsets (antenna delay / assembly bias) and avoid silently changing thresholds without traceability.
| Calibration item | Why it matters | Artifact to store | Production control |
|---|---|---|---|
| Antenna delay (Tx/Rx or equivalent bias) | Directly becomes a fixed distance offset when antenna/ground/assembly changes | antenna_delay_ver + bias value(s) | spot-check per lot; reject outliers vs golden unit |
| Temperature buckets (coarse points) | Thresholds/paths drift with temperature; keeping “policy+threshold ver” prevents chaos | temp_bucket + threshold_set_ver | verify across min/room/max; store bucket ID in logs |
| Assembly consistency sampling | Housing/ground/placement can shift CIR shape; affects first-path stability | lot_id + stats (mean/σ) + fail_code | AQL sampling; tighten mechanical tolerances when σ grows |
Production consistency: fixture, golden unit, thresholds, and log schema
Production control is a repeatability problem. The fixture fixes geometry; the golden unit fixes expectations; thresholds fix pass/fail; and the log schema makes failures searchable across lots.
- Fixture: repeatable geometry + optional “controlled obstruction” coupon for NLOS sanity checks.
- Golden unit: a known-good node run on every line shift; its logs define normal bounds.
- Thresholds: set acceptance windows for first_path_conf, noise_floor, peak_to_first, retry_count.
- Log schema: fixed fields enable grep-style triage and trend charts later.
profile_id, sts_mode, security_level, session_id, antenna_delay_ver, threshold_set_ver, temp_bucket, first_path_conf, peak_amp, noise_floor, peak_to_first, retry_count, fail_code, nlos_flag, quality_flags, reset_cause
Reference materials (MPN examples) for verification rigs and repeatable logs
The list below provides concrete, commonly used reference parts for building a repeatable verification loop (golden unit, profile freeze, known-good RF path, and consistent host control). Use these as examples; availability and package suffixes should be verified per region.
| Category | MPN examples | Why it helps H2-11 | Typical usage |
|---|---|---|---|
| UWB transceiver IC | Qorvo DW3110, DW3210 | Stable reference for CIR/first-path diagnostics and secure-ranging modes | Golden design baseline; compare profiles/threshold sets |
| Integrated UWB module | Qorvo DWM3001C (module; includes nRF52833 + antenna) | RF path is validated/calibrated; reduces “board RF uncertainty” during debug | Golden unit; quick LOS/NLOS fixture baselines |
| Dev kit | Qorvo DWM3001CDK | Repeatable host control + logs; accelerates threshold and schema freeze | Line-side golden log generator; regression tests |
| UWB IC (IoT family) | NXP Trimension SR040, SR150 | Alternative reference platform for interop baselines and profile sanity checks | Cross-vendor profile validation; evidence-field alignment |
| Clock parts (examples) |
38.4 MHz crystal: NDK NX3225SA-38.4M, Abracon ABM8-38.400MHZ TCXO example: Epson TG-5035CJ (frequency per design) |
Keeps profile/threshold experiments comparable; reduces “clock drift” confounding | Golden unit stability; temperature bucket validation |
| Debug/programming (examples) |
SWD cable: Tag-Connect TC2030-IDC RF test connector: Hirose U.FL-R-SMT-1 |
Improves repeatability of firmware/profile loading and RF sampling during debug | Line-friendly flashing; consistent RF probing points |
For H2-11, the “best” reference material is the one that produces stable, searchable logs under a frozen profile. Prioritize repeatability (golden unit + fixture + schema) over theoretical perfection.
Simplified, visual-first diagram for SEO-friendly extraction; labels are metrics/flags; text ≥ 18px.
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FAQs (x12): Debuggable ranging, not vague “it works”
Each answer stays inside node scope: timestamps, PHY params, CIR evidence, antenna delay calibration, secure ranging basics, IMU hooks, duty-cycling evidence, and production consistency.
Banned: RTLS engines (TDoA/AoA/AoD), anchor deployment, network sync (PTP/SyncE), cloud PKI deep-dive, PMIC topology deep-dive.
1
Why is DS-TWR usually less sensitive to temperature drift than SS-TWR?
Maps to: H2-3 (TWR sequences) + H2-5 (clock drift & temperature buckets)
temp_bucket vs range bias/scatter, plus stable reply_delay configuration.
2
The link works, but range is always off by a fixed value—what is the first suspicion?
Maps to: H2-6 (antenna/ground delay) + H2-11 (calibration & production evidence)
antenna_delay_ver, lot-to-lot mean shift, golden-unit comparison logs.
3
Range “jumps” when update rate increases—what are the three most common root-cause buckets?
Maps to: H2-2 (requirements→budget) + H2-7 (PHY params) + H2-10 (duty-cycle & power evidence)
first_path_conf, retry_count, reset_cause vs burst timing.
4
How does CFO/clock drift show up in ranging, and how is it measured and mitigated?
Maps to: H2-4 (timestamp chain & budget) + H2-5 (node-internal correction)
temp_bucket and range bias.
5
In CIR, what does “unstable first path” usually mean?
Maps to: H2-7 (detectability & thresholds) + H2-11 (LOS/NLOS readout & flags)
first_path_conf, peak_to_first, noise_floor, threshold_id.
6
Same hardware, but enclosure or hand-grip changes the range—how to isolate antenna vs ground-reference issues?
Maps to: H2-6 (RF front-end, antenna delay, enclosure/ground coupling)
peak_amp, noise_floor, and mean bias.
7
With STS enabled, does range become more stable or does packet loss increase—and why?
Maps to: H2-7 (PHY parameters & detectability) + H2-9 (secure ranging basics & cost)
retry_count and first_path_conf before/after STS enable.
8
Why can an encrypted link still be vulnerable to relay attacks, and what must secure ranging add?
Maps to: H2-9 (threats, STS + challenge/response, anti-downgrade)
security_level, sts_mode, policy_version, and session freshness markers.
9
What is the minimal IMU integration, and what “fusion” crosses scope into an RTLS engine?
Maps to: H2-8 (IMU hooks: triggers, time alignment, minimal outputs)
motion_state + time-aligned interrupt timestamp + quality_flags.
10
What symptoms do peak-current brownouts/resets create in ranging, and how to preserve evidence?
Maps to: H2-10 (duty-cycle & power evidence) + H2-11 (logs, fixtures, consistency)
reset_cause, brownout flag, retry_count, fail codes vs burst timing.
11
What quality flags should NLOS detection output so upper layers do not misuse the range?
Maps to: H2-11 (Detect→Degrade→Flag, minimal schema)
nlos_flag, confidence_tier, quality_flags, policy_version.
12
In production, which parameters must be locked, and which can be self-calibrated?
Maps to: H2-7 (profile freeze) + H2-11 (calibration artifacts & consistency)
profile_id, threshold_set_ver, antenna_delay_ver, temp_bucket.