User Guide

These guides provide in-depth coverage of the phased array modeling library’s capabilities. Each section includes explanations, practical examples, and best practices.

• Array Geometries
  • Planar Arrays
  • Ring and Circular Arrays
  • Conformal Arrays
  • Sparse/Thinned Arrays
  • Subarray Architectures
  • ArrayGeometry Class
  • Visualizing Array Geometries
• Beamforming
  • Beam Steering
  • Amplitude Tapering
  • Null Steering
  • Multi-Beam Synthesis
  • Monopulse Patterns
  • Applying Tapers to Arbitrary Geometries
  • Best Practices
• Impairments
  • Mutual Coupling
  • Phase Quantization
  • Element Failures
  • Scan Blindness
  • Combined Impairments
  • Best Practices
• Wideband Beamforming
  • Beam Squint Problem
  • Instantaneous Bandwidth
  • True Time Delay (TTD) Steering
  • Hybrid Phase/TTD Architecture
  • Pattern vs Frequency
  • Visualization
  • Design Guidelines
• Visualization
  • 2D Matplotlib Plots
  • UV-Space Visualization
  • 3D Plotly (Interactive)
  • Wideband Visualization
  • Saving Figures
  • Customization
  • Best Practices

Overview

The library is organized into modules that follow the typical phased array design workflow:

1. Geometry: Define array element positions and orientations

2. Beamforming: Compute steering weights, apply tapers, place nulls

3. Impairments: Model real-world effects (coupling, quantization, failures)

4. Wideband: Handle frequency-dependent effects and TTD compensation

5. Visualization: Plot patterns in 2D, 3D, and UV-space

Typical Workflow

import phased_array as pa
import numpy as np

# 1. Create array geometry
geom = pa.create_rectangular_array(16, 16, dx=0.5, dy=0.5)

# 2. Compute steering weights with sidelobe control
k = pa.wavelength_to_k(1.0)
weights = pa.steering_vector(k, geom.x, geom.y, theta0_deg=30, phi0_deg=0)
weights *= pa.taylor_taper_2d(16, 16, sidelobe_dB=-30)

# 3. (Optional) Model impairments
weights_q = pa.quantize_phase(weights, n_bits=5)

# 4. Compute pattern
theta, phi, pattern_dB = pa.compute_full_pattern(geom.x, geom.y, weights_q, k)

# 5. Visualize
pa.plot_pattern_contour(np.rad2deg(theta), np.rad2deg(phi), pattern_dB)