When Targets Merge with the Shoreline: How Radar Distinguishes What Is Moving

Berthing and unberthing are among the most frequent operations in ports, but they are also challenging scenarios for conventional navigational radar.

As a target vessel approaches the shore, its radar return may merge with returns from the shoreline, berthed vessels and port infrastructure. The resulting display can become cluttered, making moving targets difficult to distinguish from the static background.

In complex waters such as ports and inland waterways, a reliable perception system must do more than detect a return. It must determine what that return represents. The BOTIX 4D Imaging Small-Target Radar provides point-cloud-level moving and static separation, enabling it to distinguish moving targets from complex background returns and support more reliable perception in ports, inland waterways and other confined operating environments.

Maintaining Target Detection During Berthing and Unberthing

During a full-scale vessel trial, BOTIX used an approximately 40-metre tug as the target and recorded the entire berthing operation. The perception output from the BOTIX 4D Imaging Small-Target Radar was compared simultaneously with that of a conventional navigational radar.

As the target vessel approached the shoreline, the BOTIX radar continued to produce clear, distinct target returns, allowing the tug to remain detectable and separate from the complex near-shore background.

The conventional navigational radar was more susceptible to strong returns from the shoreline and terminal infrastructure. Target and background returns became intermingled, resulting in indistinct boundaries and making separation more difficult.

As the tug entered the final berthing stage, the moving vessel and static shoreline became even closer in space, further increasing the perception challenge.

The test display showed that the BOTIX radar continued to separate the target vessel from the complex background using point-cloud-level moving and static classification. On the conventional radar display, the target was more easily obscured by shoreline returns and became difficult to distinguish consistently.

The BOTIX 4D Imaging Small-Target Radar can also output additional target-level information, including course over ground (COG), speed over ground (SOG), heading and estimated target dimensions. This provides a more complete data foundation for berthing assistance, situational assessment and subsequent navigational decision-making.

Distinguishing Motion from Static Backgrounds in Complex Waters

The test display shows a target vessel being continuously detected and separated from the background. Although this may appear to be a simple improvement in visual clarity, determining whether an object is moving or stationary is considerably more difficult on the water than on land.

Target vessels typically operate at very low speeds during berthing. As a vessel moves slowly toward the shoreline, the difference in velocity between the moving target and the static background may be extremely small.

If the radar lacks sufficient velocity resolution, it may classify a slowly moving vessel as part of the shoreline. Conversely, static infrastructure or shoreline returns may be incorrectly interpreted as moving targets.

Wind- and wave-induced clutter, multipath reflections, strong close-range returns and changes in target attitude can further complicate moving and static classification.

Reliable separation therefore cannot depend on a simple velocity threshold alone. The radar architecture, point-cloud quality, clutter suppression and multi-target tracking algorithms must work together to determine the actual motion state of each target continuously.

How BOTIX Separates Moving Targets from Static Backgrounds

The BOTIX 4D Imaging Small-Target Radar does not wait until a stable target track has been established before determining whether an object is moving. Instead, it begins analyzing motion characteristics at the underlying 4D point-cloud level.

During point-cloud preprocessing, the system performs spatial-temporal alignment and stitching of point clouds collected by its multi-panel radar array. It then filters noise and classifies moving and static points.

This allows the system to assess whether individual points within the raw 4D point cloud represent moving or stationary objects before higher-level target tracking is performed.

The radar also uses multisource positioning-data fusion to improve the accuracy and stability of moving and static separation. Radar point clouds are registered and fused with data from the inertial measurement unit (IMU), Global Positioning System (GPS) and gyro.

By estimating the host vessel’s position and velocity in real time, the system can compensate for vessel motion and continuously calibrate the radar data. This helps prevent the host vessel’s own movement from being incorrectly interpreted as target motion.

Through these capabilities, the BOTIX 4D Imaging Small-Target Radar achieves:

• Velocity resolution of 0.0565 m/s

• Point-cloud-level moving and static classification across 360° coverage and the full detection range

• Classification accuracy exceeding 97% under the applicable test conditions

These capabilities provide a reliable perception foundation for target tracking, situational assessment and navigational decision-making during berthing, unberthing and other close-range operations.