diff --git a/cmd/sdrd/main.go b/cmd/sdrd/main.go
index 95c70d5..fbf6fac 100644
--- a/cmd/sdrd/main.go
+++ b/cmd/sdrd/main.go
@@ -854,8 +854,10 @@ func runDSP(ctx context.Context, srcMgr *sourceManager, cfg config.Config, det *
 				_ = enc.Encode(ev)
 			}
 			eventMu.Unlock()
-			if rec != nil {
-				go rec.OnEvents(finished)
+			if rec != nil && len(finished) > 0 {
+				evCopy := make([]detector.Event, len(finished))
+				copy(evCopy, finished)
+				go rec.OnEvents(evCopy)
 			}
 			h.broadcast(SpectrumFrame{
 				Timestamp: now.UnixMilli(),
diff --git a/internal/cfar/ca.go b/internal/cfar/ca.go
index b13f673..feae208 100644
--- a/internal/cfar/ca.go
+++ b/internal/cfar/ca.go
@@ -1,5 +1,7 @@
 package cfar
 
+import "math"
+
 // cellAvg implements CA-CFAR with a sliding sum window.
 type cellAvg struct {
 	guard      int
@@ -40,23 +42,27 @@ func (c *cellAvg) Thresholds(spectrum []float64) []float64 {
 		return spectrum[i]
 	}
 
+	toLinear := func(db float64) float64 {
+		return math.Pow(10, db/10.0)
+	}
+
 	var leftSum, rightSum float64
 	for k := 1; k <= train; k++ {
-		leftSum += at(0 - guard - k)
-		rightSum += at(0 + guard + k)
+		leftSum += toLinear(at(0 - guard - k))
+		rightSum += toLinear(at(0 + guard + k))
 	}
 
 	invN := 1.0 / float64(total)
-	out[0] = (leftSum+rightSum)*invN + c.scaleDb
+	out[0] = 10*math.Log10((leftSum+rightSum)*invN) + c.scaleDb
 
 	for i := 1; i < n; i++ {
-		leftSum -= at(i - 1 - guard - train)
-		leftSum += at(i - guard - 1)
+		leftSum -= toLinear(at(i - 1 - guard - train))
+		leftSum += toLinear(at(i - guard - 1))
 
-		rightSum -= at(i - 1 + guard + 1)
-		rightSum += at(i + guard + train)
+		rightSum -= toLinear(at(i - 1 + guard + 1))
+		rightSum += toLinear(at(i + guard + train))
 
-		out[i] = (leftSum+rightSum)*invN + c.scaleDb
+		out[i] = 10*math.Log10((leftSum+rightSum)*invN) + c.scaleDb
 	}
 	return out
 }
diff --git a/internal/cfar/caso.go b/internal/cfar/caso.go
index 6a75015..08706ac 100644
--- a/internal/cfar/caso.go
+++ b/internal/cfar/caso.go
@@ -1,5 +1,7 @@
 package cfar
 
+import "math"
+
 type caso struct {
 	guard      int
 	train      int
@@ -34,10 +36,14 @@ func (c *caso) Thresholds(spectrum []float64) []float64 {
 		return spectrum[i]
 	}
 
+	toLinear := func(db float64) float64 {
+		return math.Pow(10, db/10.0)
+	}
+
 	var leftSum, rightSum float64
 	for k := 1; k <= train; k++ {
-		leftSum += at(0 - guard - k)
-		rightSum += at(0 + guard + k)
+		leftSum += toLinear(at(0 - guard - k))
+		rightSum += toLinear(at(0 + guard + k))
 	}
 	lm := leftSum * inv
 	rm := rightSum * inv
@@ -45,20 +51,20 @@ func (c *caso) Thresholds(spectrum []float64) []float64 {
 	if rm < noise {
 		noise = rm
 	}
-	out[0] = noise + c.scaleDb
+	out[0] = 10*math.Log10(noise) + c.scaleDb
 
 	for i := 1; i < n; i++ {
-		leftSum -= at(i - 1 - guard - train)
-		leftSum += at(i - guard - 1)
-		rightSum -= at(i - 1 + guard + 1)
-		rightSum += at(i + guard + train)
+		leftSum -= toLinear(at(i - 1 - guard - train))
+		leftSum += toLinear(at(i - guard - 1))
+		rightSum -= toLinear(at(i - 1 + guard + 1))
+		rightSum += toLinear(at(i + guard + train))
 		lm = leftSum * inv
 		rm = rightSum * inv
 		noise = lm
 		if rm < noise {
 			noise = rm
 		}
-		out[i] = noise + c.scaleDb
+		out[i] = 10*math.Log10(noise) + c.scaleDb
 	}
 	return out
 }
diff --git a/internal/cfar/cfar_test.go b/internal/cfar/cfar_test.go
index 40efb55..8e6db39 100644
--- a/internal/cfar/cfar_test.go
+++ b/internal/cfar/cfar_test.go
@@ -1,6 +1,9 @@
 package cfar
 
-import "testing"
+import (
+	"math"
+	"testing"
+)
 
 func makeSpectrum(n int, noiseDb float64, signals [][2]int, sigDb float64) []float64 {
 	s := make([]float64, n)
@@ -59,6 +62,37 @@ func TestGOSCAMaskingProtection(t *testing.T) {
 	}
 }
 
+func TestCellAveragingUsesLinearPower(t *testing.T) {
+	spec := []float64{-100, -100, -100, -80, -100, -90, -100, -100, -100}
+	cfg := Config{GuardCells: 0, TrainCells: 2, ScaleDb: 0, WrapAround: false}
+
+	ca := New(Config{Mode: ModeCA, GuardCells: cfg.GuardCells, TrainCells: cfg.TrainCells, ScaleDb: cfg.ScaleDb, WrapAround: cfg.WrapAround})
+	gosca := New(Config{Mode: ModeGOSCA, GuardCells: cfg.GuardCells, TrainCells: cfg.TrainCells, ScaleDb: cfg.ScaleDb, WrapAround: cfg.WrapAround})
+	caso := New(Config{Mode: ModeCASO, GuardCells: cfg.GuardCells, TrainCells: cfg.TrainCells, ScaleDb: cfg.ScaleDb, WrapAround: cfg.WrapAround})
+
+	mid := 5
+	caTh := ca.Thresholds(spec)[mid]
+	goscaTh := gosca.Thresholds(spec)[mid]
+	casoTh := caso.Thresholds(spec)[mid]
+
+	dbApproxCA := (-80.0 + -90.0 + -100.0 + -100.0) / 4.0
+	dbApproxGOSCA := -85.0
+	dbApproxCASO := -100.0
+
+	if math.Abs(caTh-dbApproxCA) < 1.0 {
+		t.Fatalf("CA threshold still looks like dB averaging: got %v approx %v", caTh, dbApproxCA)
+	}
+	if math.Abs(goscaTh-dbApproxGOSCA) < 1.0 {
+		t.Fatalf("GOSCA threshold still looks like dB averaging: got %v approx %v", goscaTh, dbApproxGOSCA)
+	}
+	if math.Abs(casoTh-dbApproxCASO) < 1.0 {
+		t.Fatalf("CASO threshold still looks like dB averaging: got %v approx %v", casoTh, dbApproxCASO)
+	}
+	if !(goscaTh > caTh && caTh > casoTh) {
+		t.Fatalf("unexpected ordering: GOSCA=%v CA=%v CASO=%v", goscaTh, caTh, casoTh)
+	}
+}
+
 func BenchmarkCFAR(b *testing.B) {
 	spec := makeSpectrum(2048, -100, [][2]int{{500, 510}, {1000, 1020}}, -20)
 	for _, mode := range []Mode{ModeCA, ModeOS, ModeGOSCA, ModeCASO} {
diff --git a/internal/cfar/gosca.go b/internal/cfar/gosca.go
index 27f2a28..d3f063c 100644
--- a/internal/cfar/gosca.go
+++ b/internal/cfar/gosca.go
@@ -1,5 +1,7 @@
 package cfar
 
+import "math"
+
 // gosca implements Greatest-Of Selection with Cell Averaging.
 type gosca struct {
 	guard      int
@@ -40,10 +42,14 @@ func (g *gosca) Thresholds(spectrum []float64) []float64 {
 		return spectrum[i]
 	}
 
+	toLinear := func(db float64) float64 {
+		return math.Pow(10, db/10.0)
+	}
+
 	var leftSum, rightSum float64
 	for k := 1; k <= train; k++ {
-		leftSum += at(0 - guard - k)
-		rightSum += at(0 + guard + k)
+		leftSum += toLinear(at(0 - guard - k))
+		rightSum += toLinear(at(0 + guard + k))
 	}
 
 	leftMean := leftSum * inv
@@ -52,13 +58,13 @@ func (g *gosca) Thresholds(spectrum []float64) []float64 {
 	if rightMean > noise {
 		noise = rightMean
 	}
-	out[0] = noise + g.scaleDb
+	out[0] = 10*math.Log10(noise) + g.scaleDb
 
 	for i := 1; i < n; i++ {
-		leftSum -= at(i - 1 - guard - train)
-		leftSum += at(i - guard - 1)
-		rightSum -= at(i - 1 + guard + 1)
-		rightSum += at(i + guard + train)
+		leftSum -= toLinear(at(i - 1 - guard - train))
+		leftSum += toLinear(at(i - guard - 1))
+		rightSum -= toLinear(at(i - 1 + guard + 1))
+		rightSum += toLinear(at(i + guard + train))
 
 		leftMean = leftSum * inv
 		rightMean = rightSum * inv
@@ -66,7 +72,7 @@ func (g *gosca) Thresholds(spectrum []float64) []float64 {
 		if rightMean > noise {
 			noise = rightMean
 		}
-		out[i] = noise + g.scaleDb
+		out[i] = 10*math.Log10(noise) + g.scaleDb
 	}
 	return out
 }
diff --git a/internal/cfar/os.go b/internal/cfar/os.go
index a318d0e..58d002a 100644
--- a/internal/cfar/os.go
+++ b/internal/cfar/os.go
@@ -55,6 +55,15 @@ func (o *orderedStat) Thresholds(spectrum []float64) []float64 {
 	sort.Float64s(win)
 	out[0] = win[o.rank] + o.scaleDb
 
+	rebuildWindow := func(bin int) {
+		win = win[:0]
+		for k := 1; k <= train; k++ {
+			win = append(win, at(bin-guard-k))
+			win = append(win, at(bin+guard+k))
+		}
+		sort.Float64s(win)
+	}
+
 	for i := 1; i < n; i++ {
 		removeFromSorted(&win, at(i-1-guard-train))
 		removeFromSorted(&win, at(i-1+guard+1))
@@ -62,6 +71,9 @@ func (o *orderedStat) Thresholds(spectrum []float64) []float64 {
 		insertSorted(&win, at(i-guard-1))
 		insertSorted(&win, at(i+guard+train))
 
+		if len(win) != 2*train {
+			rebuildWindow(i)
+		}
 		out[i] = win[o.rank] + o.scaleDb
 	}
 	return out