feat: add refinement windows for local analysis

cmd/sdrd/pipeline_runtime.go

@@ -236,10 +236,19 @@ func (rt *dspRuntime) buildRefinementInput(surv pipeline.SurveillanceResult) pip
 	if len(scheduled) == 0 && len(plan.Selected) > 0 {
 		scheduled = append([]pipeline.ScheduledCandidate(nil), plan.Selected...)
 	}
+	windows := make([]pipeline.RefinementWindow, 0, len(scheduled))
+	for _, sc := range scheduled {
+		windows = append(windows, pipeline.RefinementWindow{
+			CenterHz: sc.Candidate.CenterHz,
+			SpanHz:   sc.Candidate.BandwidthHz,
+			Source:   "candidate",
+		})
+	}
 	input := pipeline.RefinementInput{
 		Candidates: append([]pipeline.Candidate(nil), surv.Candidates...),
 		Scheduled:  scheduled,
 		Plan:       plan,
+		Windows:    windows,
 		SampleRate: rt.cfg.SampleRate,
 		FFTSize:    rt.cfg.FFTSize,
 		CenterHz:   rt.cfg.CenterHz,
@@ -284,7 +293,7 @@ func (rt *dspRuntime) refineSignals(art *spectrumArtifacts, input pipeline.Refin
 		centerHz = rt.cfg.CenterHz
 	}
 	snips, snipRates := extractSignalIQBatch(extractMgr, art.iq, sampleRate, centerHz, selectedSignals)
-	refined := pipeline.RefineCandidates(selectedCandidates, art.spectrum, sampleRate, fftSize, snips, snipRates, classifier.ClassifierMode(rt.cfg.ClassifierMode))
+	refined := pipeline.RefineCandidates(selectedCandidates, input.Windows, art.spectrum, sampleRate, fftSize, snips, snipRates, classifier.ClassifierMode(rt.cfg.ClassifierMode))
 	signals := make([]detector.Signal, 0, len(refined))
 	decisions := make([]pipeline.SignalDecision, 0, len(refined))
 	for i, ref := range refined {

internal/pipeline/phases.go

@@ -24,6 +24,7 @@ type RefinementInput struct {
 	Candidates []Candidate          `json:"candidates,omitempty"`
 	Scheduled  []ScheduledCandidate `json:"scheduled,omitempty"`
 	Plan       RefinementPlan       `json:"plan,omitempty"`
+	Windows    []RefinementWindow   `json:"windows,omitempty"`
 	SampleRate int                  `json:"sample_rate"`
 	FFTSize    int                  `json:"fft_size"`
 	CenterHz   float64              `json:"center_hz"`

internal/pipeline/refiner.go

@@ -7,7 +7,7 @@ import (
 
 // RefineCandidates upgrades coarse detector candidates into refined signals
 // by attaching local IQ-derived classification and PLL metadata.
-func RefineCandidates(candidates []Candidate, spectrum []float64, sampleRate int, fftSize int, snippets [][]complex64, snippetRates []int, mode classifier.ClassifierMode) []Refinement {
+func RefineCandidates(candidates []Candidate, windows []RefinementWindow, spectrum []float64, sampleRate int, fftSize int, snippets [][]complex64, snippetRates []int, mode classifier.ClassifierMode) []Refinement {
 	out := make([]Refinement, 0, len(candidates))
 	for i, c := range candidates {
 		sig := detector.Signal{
@@ -44,8 +44,13 @@ func RefineCandidates(candidates []Candidate, spectrum []float64, sampleRate int
 				cls.ModType = classifier.ClassWFMStereo
 			}
 		}
+		var window RefinementWindow
+		if i < len(windows) {
+			window = windows[i]
+		}
 		out = append(out, Refinement{
 			Candidate:   c,
+			Window:      window,
 			Signal:      sig,
 			SnippetRate: snipRate,
 			Class:       cls,

internal/pipeline/types.go

@@ -8,25 +8,33 @@ import (
 // Candidate is the coarse output of the surveillance detector.
 // It intentionally stays lightweight and cheap to produce.
 type Candidate struct {
-	ID         int64     `json:"id"`
-	CenterHz   float64   `json:"center_hz"`
-	BandwidthHz float64  `json:"bandwidth_hz"`
-	PeakDb     float64   `json:"peak_db"`
-	SNRDb      float64   `json:"snr_db"`
-	FirstBin   int       `json:"first_bin"`
-	LastBin    int       `json:"last_bin"`
-	NoiseDb    float64   `json:"noise_db,omitempty"`
-	Source     string    `json:"source,omitempty"`
-	Hint       string    `json:"hint,omitempty"`
+	ID          int64   `json:"id"`
+	CenterHz    float64 `json:"center_hz"`
+	BandwidthHz float64 `json:"bandwidth_hz"`
+	PeakDb      float64 `json:"peak_db"`
+	SNRDb       float64 `json:"snr_db"`
+	FirstBin    int     `json:"first_bin"`
+	LastBin     int     `json:"last_bin"`
+	NoiseDb     float64 `json:"noise_db,omitempty"`
+	Source      string  `json:"source,omitempty"`
+	Hint        string  `json:"hint,omitempty"`
+}
+
+// RefinementWindow describes the local analysis span that refinement should use.
+type RefinementWindow struct {
+	CenterHz float64 `json:"center_hz"`
+	SpanHz   float64 `json:"span_hz"`
+	Source   string  `json:"source,omitempty"`
 }
 
 // Refinement contains higher-cost local analysis derived from a candidate.
 type Refinement struct {
-	Candidate   Candidate                    `json:"candidate"`
-	Signal      detector.Signal              `json:"signal"`
-	SnippetRate int                          `json:"snippet_rate"`
-	Class       *classifier.Classification   `json:"class,omitempty"`
-	Stage       string                       `json:"stage,omitempty"`
+	Candidate   Candidate                  `json:"candidate"`
+	Window      RefinementWindow           `json:"window"`
+	Signal      detector.Signal            `json:"signal"`
+	SnippetRate int                        `json:"snippet_rate"`
+	Class       *classifier.Classification `json:"class,omitempty"`
+	Stage       string                     `json:"stage,omitempty"`
 }
 
 func CandidatesFromSignals(signals []detector.Signal, source string) []Candidate {