rf sensor parameter and performance plotting improvements

@rfSensor/computePointToPoints.m

@@ -16,7 +16,7 @@ function [d, t, a] = computePointToPoints(obj, agentPos, targetPos)
     % distance from sensor nadir to target nadir (i.e. distance ignoring altitude)
     x = vecnorm(agentPos(1:2) - targetPos(:, 1:2), 2, 2);
     
-    % tilt angle (degrees) (-90, 0 (down), 90)
+    % tilt angle (degrees) (0 (nadir), 180 (zenith))
     t = (180 - atan2d(x, targetPos(:, 3) - agentPos(3)));
 
     % azimuth angle (degrees) (0 (+y) clockwise to 360)

@rfSensor/plotParameters.m

@@ -7,9 +7,9 @@ function f = plotParameters(obj)
     end
     
     % Distance and tilt sample points
-    d_values = [0.1, 0.5, 1:1:9, 10:2:19, 20:5:49, 50:10:100];
+    d_values = 10.^[1, 2, 3, 4, 5, 6];
-    t_values = -90:0.5:90;
+    t_values = 0:2.5:180;   % 0=nadir (center), 180=zenith (edge)
-    a_values = 0:0.5:360;
+    a_values = 0:2.5:360;
 
     % Make grid of values of distances and tilts
     [d_mg, t_mg, a_mg] = meshgrid(d_values, t_values, a_values);
@@ -20,17 +20,18 @@ function f = plotParameters(obj)
     s_x = reshape(s_x, size(d_mg));
 
     [T, A] = meshgrid(t_values, a_values);   % Naz x Nel
-    Tr = deg2rad(T);
     Ar = deg2rad(A);
 
-    figure;
+    f = figure;
     hold("on");
 
     for ii = 1:numel(d_values)
-        % geometry (your "tilt from nadir, stack by distance")
+        % Linear radial mapping: t=0 (nadir) -> center, t=180 (zenith) -> edge
-        X = d_values(ii) * cos(Ar) .* sin(Tr);
+        % Radius in log10 units to match Z axis scale
-        Y = d_values(ii) * sin(Ar) .* sin(Tr);
+        r = log10(d_values(ii)) .* T ./ 180;
-        Z = d_values(ii) * ones(size(X));
+        X = r .* cos(Ar);
+        Y = r .* sin(Ar);
+        Z = log10(d_values(ii)) * ones(size(X));
     
         % evaluate or extract this slice
         Fslice = squeeze(s_x(:, ii, :))';
@@ -45,7 +46,7 @@ function f = plotParameters(obj)
     colormap(turbo);
     colorbar;
     daspect([1 1 0.2])   % Separate Z further for more distinct layers
-    xlabel('X'); ylabel('Y'); zlabel('Distance (m)');
+    xlabel('X (log_{10} units)'); ylabel('Y (log_{10} units)'); zlabel('log_{10} Distance (m)');
     set(gca,'ZDir','reverse');
     view(3); 
     axis("vis3d");

@rfSensor/plotPerformance.m

@@ -13,7 +13,10 @@ function f = plotPerformance(obj, altitude, otherSensorsPos, otherSensors)
 
     % Create grid on which to evalute SINR, SNR
     agentPos = [0, 0, altitude];
-    d = max(10, max(vecnorm(otherSensorsPos(1:2), 2, 2)) * 1.25);
+    d = 10;
+    if ~isempty(otherSensorsPos)
+        d = max(d, max(vecnorm(otherSensorsPos(:, 1:2), 2, 2)) * 1.25);
+    end
     c = 0.1;
     d = ceil(d / c) * c;
     distances = -d:c:d;
@@ -37,6 +40,7 @@ function f = plotPerformance(obj, altitude, otherSensorsPos, otherSensors)
     colorbar;
     xlabel("X (m)"); ylabel("Y (m)");
     title("Linearly Normalized SNR (dB)");
+    subtitle("No interfering sources");
 
     nexttile;
     imagesc(distances, distances, SINR);
@@ -44,5 +48,5 @@ function f = plotPerformance(obj, altitude, otherSensorsPos, otherSensors)
     colorbar;
     xlabel("X (m)"); ylabel("Y (m)");
     title("Linearly Normalized SINR (dB)");
-    
+    subtitle(sprintf("%d interfering source(s)", size(otherSensorsPos, 1)));
 end

@rfSensor/transmitterGain.m

@@ -11,6 +11,11 @@ function value = transmitterGain(obj, t, a)
         error("t and a must be the same size");
     end
 
-    % Temporary logic to make nadir-pointing most effective
+    n_t = 4;   % tilt beamwidth (higher = narrower beam)
-    value = 10 .* log10(cosd(t) .^ 2) + 10 .* log10((0.5 + 0.5 .* cosd(a)) .^ 4);
+    n_a = 4;   % azimuth rolloff sharpness (higher = more directional)
+
+    % Elevation: cardioid family, null at zenith (t=180°), peak at nadir (t=0°)
+    % Azimuth: cardioid family, peak at a=0° (+y), null at a=180° (-y)
+    value = 10 .* n_t .* log10((1 + cosd(t)) ./ 2) + ...
+            10 .* n_a .* log10((0.5 + 0.5 .* cosd(a)));
 end

test/test_rfSensor.m

@@ -51,7 +51,6 @@ classdef test_rfSensor < matlab.unittest.TestCase
             otherSensors{1} = tc.testClass; % One interfering sensor, identical to the main sensor
 
             tc.testClass.plotPerformance(altitude, otherSensorsPos, otherSensors);
-            
         end
 
         function plot_SINR_heterogenous_interferers(tc)
@@ -74,7 +73,6 @@ classdef test_rfSensor < matlab.unittest.TestCase
             otherSensors{2} = otherSensors{2}.initialize(100 * P_TX, BW, f_c, G_RX_dBi);
 
             tc.testClass.plotPerformance(altitude, otherSensorsPos, otherSensors);
-            
         end
     end
 end