Radio Wave Reradiation/Reflection TRIZ Cases

Radar estimates are screened with average–median comparison, accepting the average only when the allowable condition supports accuracy.

This radar circuit assigns Doppler shifts and code sequences to antennas, extracting more path responses for improved angular sensing.

A signal processor periodically updates phase correction coefficients to offset antenna changes from shocks or environmental conditions.

Symmetric antenna arrays use complex-conjugate beat-signal products to correct phase noise and improve range and velocity accuracy.

Multiple frequency bands and filtered return signals expand map resolution and depth range for non-invasive drilling hazard detection.

This case combines millimeter-wave radar with AI to classify human targets from micro-Doppler features for privacy-preserving monitoring.

Measure propagation time repeatedly with offset responses to improve wireless ranging accuracy for moving targets.

A trained determination model separates terrain, land use, vegetation, and soil inputs to attribute ground height displacement.

Combining 24 GHz and 77 GHz components balances spatial resolution, penetration, and clutter resilience in dense environments.

This radar control case compares FFT-derived relative speeds from two radars to identify blockage and preserve detection reliability.

This radar case uses alternating central frequencies and unequal Doppler intervals to extend range and improve velocity accuracy.

This case aligns oscillator, clock, and RF phases across radar chips to improve distance, speed, and angle measurements.

The RF system matches response resonances to a material database for sensitive identification and multi-point localization.

A controller tunes radar parameters for flight phases, environments, and missions, reducing aircraft size, weight, complexity, and cost.

Descriptor matching of radar landmarks reduces computation for accurate, real-time sensor localization on low-power vehicle platforms.

This radar simulation approach uses path loss models and point spread functions to support low-latency SIL and HIL validation.

Software alignment and centralized processing combine radar signals for improved angular resolution without shared synchronization hardware.

CFAR-selected noise power drives frequency-dependent compensation, preserving target detection as filter characteristics change.

Attention selects relevant radar range bins to reduce memory and power use.

This case matches simulated SAR images to imaging conditions, improving change detection in urban areas with layover.

Comparison signals estimate interference variables and correct phase noise, improving distance measurement in multichannel radar.

The case uses angular spectra and signal-based noise levels to suppress precipitation echoes while preserving target detection.

A server combines delay profiles from mobile terminals to identify reflective objects and estimate water vapor across wider areas.

Phase derivative analysis identifies ambiguous SAR pixels, while Doppler-spectrum thresholding suppresses azimuth ambiguities across SAR modes.

Fuse sensor data across devices to improve angular resolution and spatial awareness.

Compute bistatic control vectors to improve radar angle estimates without direct path calibration.

Multiple radar modules and groupwise deep learning localize subtle cabin movements despite multipath effects and clutter.

This radar case combines I/Q complex data and FFT processing to cancel electromagnetic noise while maintaining continuous target detection.

This radar approach uses shared transmitter phase coding and receiver compensation to reduce ghost targets from fleet interference.

Switch transmitter and receiver nodes to preserve RF detection accuracy amid interference.

Automatic signal-variation estimation tunes delay, frequency shift, and attenuation to correct RF simulation setup deviations.

The radar applies one phase code across transmitters and compensates receivers, spreading interference in Doppler processing.

Sparsity-driven signal processing estimates antenna positions and restores sharp radar images despite motion-induced perturbations.

A modular radar detects reflections from predetermined targets to select vehicle-side calibration tables and align a narrower FOV.

A limiting amplifier and modular delay lines improve radar return replication across multiple targets and antenna arrays.

This case encodes key Doppler peaks before neural processing, reducing radar tensor size for real-time detection on modest controllers.

Coupled antenna modules with distinct radiating directions extend radar coverage beyond 180° for adaptable detection ranges.

Vertical radar sensor offsets reduce ground-bounce interference and power fading.

This radar case uses alternating defined delays between radar sections to preserve Doppler detection range and reduce measurement time.

This case detects corrupted range-Doppler subarrays and modifies them to improve DOA estimates for vehicle radar.

A frequency-offset chirp and beat-frequency comparison verify cascaded radar IC timing without extra loopback wiring.

A receiver estimates RF variation over time, then adapts signal parameters to simulate reflective objects and fading for DUT testing.

This radar case uses stepped-frequency chirps and FFT processing to improve range and velocity estimates for moving targets.

This case uses non-adjacent measurement vectors and correlated subspaces to reduce autocorrelation computation and rounding errors.

This radar case extrapolates snapshots to build full-rank covariance matrices, improving DoA robustness despite calibration errors.

A frequency-offset chirp test measures synchronization between leader and follower radar ICs without added loopback wiring.

Different sensor heights create phase shifts so at least one radar unit receives constructive signals during ground-bounce fading.

Receiver subarray spectra identify overlapping reflections and correct range-Doppler data for more reliable MIMO radar DOA estimates.

Predict radar resource needs from traffic conditions to reduce interference.

A four-sector radar method tracks selected beam amplitudes over time to detect congestion despite stationary surrounding vehicles.