Radar vs. other technologies for mobile & industrial uses

Radar sensors, 3D cameras, LiDAR, and ultrasonic sensors measure distance or area in various mobile and industrial applications. Choosing the right technology for a specific application is critical for consistent, accurate monitoring, and reliable operation.

The landscape for these technologies is a mix of application demands and technology enablers:

Industrial automation often requires object detection, level or height measurement, area monitoring, and more.
Innovations such as mobile robotics and autonomous vehicles have introduced new, complex use cases, particularly for obstacle detection and collision avoidance.
Sensor fusion, or combining radar with cameras, LiDAR, and ultrasonics, is increasingly popular for complex, safety-critical tasks in robotics and autonomous vehicles.
New breakthroughs in radar technology make industrial and mobile radar sensors more suitable in cases where other technologies previously excelled or struggled.

This guide helps determine if radar fits an application by exploring:

How each technology works
Their features and different types
The advantages and disadvantages of each
Applications where each one is most popular
Innovations and sensor fusion

Technology overviews: Radar, LiDAR and ultrasonic

Radar is capable of all-weather, long-range perception. It originated in the 1930s as a military detection technology. It rapidly evolved through microwave miniaturization and FMCW modulation into today’s compact 24–122 GHz solid-state industrial and automotive sensors.
LiDAR is cornerstone of modern 3D sensing for robotics and autonomous systems through solid-state and photonic integration. It emerged in the 1960s following the invention of the laser and first served aerospace and topographic mapping.
Ultrasonic sensing is the most economical short-range distance-measurement technology for parking assistance, level sensing, and low-cost automation. Its origins trace back to early 20th-century sonar. The technology evolved thanks to advances in piezoelectric materials and digital electronics.

Radar, LiDAR and ultrasonic sensing: Milestones and innovations
1910s	Ultrasonic: First sonar transducer
1930s	Radar: Early RF detection system, military application
1950s-1960s	Radar: Civil aviation, marine navigation Ultrasonic: Industrial NDT and early medical imaging LiDAR: Early rangefinding after laser was invented
1970s-1980s	Radar: Miniaturization, Doppler weather radar Ultrasonic: Factory automation and distance sensing expands LiDAR: Airborne scanning for topography and mapping
1990s	Radar: First automotive ACC systems Ultrasonic: Parking assist becomes mainstream LiDAR: Robotic mapping, environmental and forestry applications
2000s	Radar: FMCW becomes widespread Ultrasonic: Low-cost digital modules, tank level and robotics applications expand LiDAR: Mechanical 360° LIDAR for autonomous vehicles
2010s-2020s	Radar: High-res 4D imaging radar, AI-enhanced object clarification Ultrasonic: MEMS ultrasonic arrays

Key factors for sensor selection

Choosing a radar sensor vs. 3D cameras, LiDAR, or ultrasonic for an application generally comes down to four key considerations:

Range and resolution: Is precision or range more important?
Best for environment: Will the sensor face harsh outdoor or indoor conditions?
Media/target type: What kinds of media will the sensor detect or measure?
Budget: Do the technology benefits outweigh the costs?

Industrial applications

Technology	Excels at	Weakest at	Range & resolution	Environment	Media/target	Budget
Radar	Maximum range, environmental robustness, universal surface compatibility	Cost competitiveness, media compatibility	Excellent range with good precision; best when both are needed	Superior for harsh industrial environments—dust, temperature extremes, moisture, contaminants	Works with any surface type—metallic, transparent, irregular, or dark materials	Highest cost; justified when environmental challenges make other technologies impractical
LiDAR	Precision and resolution, 3D mapping, object recognition	Cost competitiveness, environmental robustness in harsh conditions	Excellent combination of long range with highest precision; ideal for detailed 3D measurements	Poor for harsh industrial conditions—struggles with dust, steam, fog, and contaminants that scatter laser light	Excellent for mapping complex geometries; limited by highly reflective or transparent surfaces	Most expensive option (comparable to 3D cameras); justified for safety-critical applications requiring maximum precision
Ultrasonic	Cost competitiveness, good resolution within range	Range, environmental robustness, process speed	Good precision but extremely limited range	Poor for harsh conditions—dust, humidity, temperature variations degrade performance	Moderate compatibility; works with most solid surfaces but struggles with foam, fabrics	Most affordable option; best when budget is primary constraint and conditions are controlled

Mobile applications

Technology	Excels at	Weakest at	Range & resolution	Environment	Media/target	Budget
Radar	Long range, velocity measurement, environmental robustness, all lighting conditions	Long range, velocity measurement, environmental robustness, all lighting conditions	Best for long-range detection where velocity matters more than fine detail	Ideal for harsh outdoor conditions—rain, fog, snow, day/night operation	Detects all objects regardless of color or material; cannot distinguish visual details	Mid-range cost; excellent value for all-weather reliability
LiDAR	Long range, highest resolution/accuracy, object recognition, dark light performance	Cost competitiveness, bright light performance	Superior combination of both—ideal when precision at distance is critical	Good for controlled outdoor conditions; struggles in bright sunlight	Excellent 3D mapping of all objects; limited by highly reflective surfaces	Most expensive option; best for safety-critical applications requiring precision
Ultrasonic	Resolution/accuracy at close range, cost competitiveness, all lighting conditions	Range, velocity measurement	High precision at very short distances only	Consistent performance regardless of lighting; moderate weather tolerance	Detects all solid objects at close proximity; limited material discrimination	Most affordable; ideal for simple proximity detection
2D camera	Resolution/accuracy, object recognition, color/contrast detection	Environmental robustness, lighting sensitivity	Excellent resolution at moderate range; best for detailed visual analysis	Poor for harsh conditions; struggles in extreme bright/dark and adverse weather	Only technology reading signs, lane markings, traffic lights; requires visual information	Moderate cost; good value when color/visual recognition is essential

Radar vs. other sensor technologies

Radar vs. ultrasonic

Radar is better for long-range object detection and distance/level measurement in challenging environments. Ultrasonic is better forshort-range proximity detection and liquid level measurement in still environements. Radar costs slightly more but is more versatile.

Radar vs. LiDAR

Radar is better for all-weather robustness and cost-effective mid-range sensing, or when detecting shiny, dull, dusty, or dirty objects. LiDAR is better for high-precision 3D mapping in clearer environments where cost is less critical.

Radar vs. 3D Cameras

Radar is better for general-purpose, rugged sensing and mid-range measurements; 3D cameras are better for applications demanding rich spatial scene understanding and object classification in controlled lighting. 3D cameras are significantly more expensive.

Innovations and sensor fusion

Expanded radar capabilities

Cutting-edge radar sensors use frequency-modulated controlled wave (FMCW) radar technology and 4GHz bandwidth for significantly better detail and range resolution. They approach the abilities of LiDAR and 3D in those regards at a lower price point. Most notably, they provide these enhanced abilities in harsh environments where other technologies struggle.

Sensor fusion: Combining technologies

Given each technology's varied features and limitations, combining two or more technologies can fulfill all application requirements. This approach is most common in safety-critical applications requiring human recognition. Robust spatial data awareness, precise navigation, and real-time processing in challenging conditions are essential in mobile robotics and automated guided vehicles. In these cases, machines or robots with little to no human oversight may interact with people, making safety especially important.

How sensor fusion works

Low-level fusion (Raw data fusion) combines raw measurements before interpretation. Example: Merging LiDar point clouds and radar Doppler points.
Mid-level fusion (Feature fusion) first extracts features (e.g., edges, objects, velocity), then merges features from multiple sensors for enhanced detection.
High-level fusion (Decision fusion) allows each sensor to make its own decision. They system either chooses which signal is correct, or blends decisions.

Benefits of Sensor Fusion

Increased accuracy: Combining sensors reduces noise and uncertainty
Better reliability: Other sensors compensate when one sensor degrades
Richer understanding: Different sensors capture geometry, color, speed, depth, and classification
Greater safety: Fused data reduces false positives/negatives.
Expanded use cases: Greater autonomy for smart factories, mobile equipment, and complex motion control