Radio Wave Reradiation/Reflection TRIZ Cases

Closed-loop radar PA calibration counters detector saturation and bias-code errors.

Attention-based radar patches improve object tracking across frames while limiting computation.

Chirp data is arranged in offset memory rows to reduce local storage needs and accelerate range and velocity Fourier processing.

A machine-learning pipeline creates virtual antenna signals and high-resolution maps to improve radar detection without extra hardware.

A policy network tunes digital filter parameters while multi-head rewards flag out-of-distribution radar scenes.

This radar method correlates range-based long beam and reference vectors to reduce angle ambiguity without added hardware or computation.

This radar case uses indexed vector histograms to retain low-amplitude returns and make processing loads more predictable.

The case combines transmit data before adaptive beamforming to produce high-quality ultrasound images with less computation.

Randomized chirp slopes reduce interference between multiple FMCW sensing nodes while preserving accurate RF sensing.

Receive antennas at varying elevations and neural virtual arrays jointly process radar data for more accurate target tracking.

DDMA phase steps separate MIMO radar signals, improving SNR and angular resolution.

Distinct PRI chirp groups are extrapolated to reduce processing delay and improve unambiguous velocity determination.

A neural network emulates λ/2-spaced antennas, improving phase uniformity and angular calculations without adding physical array elements.

A shared rotating platform combines laser and radar scans to resolve phase ambiguity and map structural displacement in 3D.

Multiple radar transmitters and receivers detect vehicle edges and gaps to measure drive-through wait and transit times in varied weather.

FastBLRC uses FFT-equivalent operations to preserve high-resolution DOA detection while reducing radar memory and compute demands.

Non-λ/2 spacing creates virtual-array gaps; neural networks add supplemental antennas to restore phase uniformity and angular resolution.

This automotive radar case replaces large matrix operations with FFT processing for high-resolution DOA estimation in larger arrays.

This radar case uses uniform phase steps in DDMA to improve SNR, angular resolution, and target angle and velocity estimation.

Each radar chip preprocesses echo data before combination, easing CPU throughput and cache bottlenecks in large antenna arrays.

This radar approach uses grouped chirps and extrapolation to reduce effective PRI and improve velocity disambiguation.

This case uses interpolated time-frequency analysis to convert Doppler shifts and time delays into vehicle object speed and range.

A propagation-channel likelihood ratio test uses amplitude and phase data to improve buried-target localization in heterogeneous ground.

ARMA reconstruction restores radar samples degraded by interference.

A coupled peak detector, comparator, and threshold monitor flags low transmit power for radar fault recovery.

Partial transmission configurations help separate passive radio measurements without exposing embedded data.

Frequency-bin thresholds and adaptive scaling reduce interfered cells, limiting ghost artifacts and false positives in radar detection.

Noise-gated CFAR targets high-amplitude radar bins to cut processing power.

This case combines Doppler imaging masts with light curtains to reduce false detections during body scanner entry.

Independent transmission clocks reduce radar interference while reception timing adapts detection from minimum to maximum distance.

Expected range-Doppler filtering separates ground backscatter from object clutter, improving vehicle motion estimation reliability.

This case models radar ground clutter from terrain patches and NUFFT resampling for faster, realistic bi-static radar testing.

Radar behavior recognition combines time-frequency and LPC features to resist light interference.

This case shifts chirp start frequency and sampling timing to break spur coherence and improve FMCW radar range and velocity accuracy.

Adaptive spectrogram scaling suppresses radar interference while limiting ghost artifacts.

Airport surface geometry and operating rules refine aircraft and obstacle paths to improve collision alerts during taxiing.

This case combines a plastic radar window, conductive fillers, and frame-integrated heating for defrosting without added air hoses.

This case uses segmented subsignals and phase differences to resolve high-speed Doppler ambiguity without extra chirps.

Optical modulation and transmitter frequency conversion combine low- and high-frequency radar signals for range and resolution.

This radar processing case interleaves data-cube writes and reads, extending Doppler work across look periods for better utilization.

MIMO sparse arrays and iterative refinement improve angle-of-arrival resolution while reducing grating lobes and false detections.

Iterative AESA quadrant calibration uses the radar receiver/excitor for relative phase and amplitude checks, supporting health monitoring.

Overlapping polarization values hinder particle classification; scatter-plot correlation of radar factors improves discrimination accuracy.

Stationary calibration averages received signals to build channel-specific correction spectra for radar sensors behind vehicle body parts.

A processor subtracts pre-calculated transmission line error distances from UWB ToF measurements to improve detection accuracy.

Multiple antennas use phase rotation, code sequences, and transmission delays to extend Doppler range without aliasing.

This radar chip uses preconfigured sequencing registers to coordinate transmit, receive, ADC, and amplifier timing autonomously.

This radar case uses direction-of-arrival estimates and phase compensation to improve velocity accuracy while preserving angular resolution.

Angle, polarization, time, and frequency filtering separate interference from radar returns for continuous environmental mapping.

A first radar processor omits data segments, while a second reconstructs them with machine learning for lower bandwidth use.