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As autonomous platforms become more advanced, it is useful to obtain more information about the environment. Classic vision sensors such as RGB-D cameras, LiDAR, or acoustical imaging cameras are capable of accurately displaying the location of an object in 3D space and are widely used for this purpose. However, a problem arises when the measurement environment becomes more complex and measurement data might begin to suffer in terms of accuracy. Imaging sonar sensors are developed specifically for these environments but are limited in terms of frame rate due to the inherent slow nature of sound. This paper will propose a method to increase the amount of useful information obtained from the available data from a single measurement in post-processing. Utilizing a signal in combination with a Doppler velocity-tuned matched-filter bank it is possible to estimate the radial velocity of an object with respect to the sensor&#x2019;s location. This allows the robot to make better decisions when it comes to path planning and collision avoidance, as a rapidly approaching object requires a different action than a stationary object or one moving away from the sensor. Another advantage of this system is that a single seemingly-large object might be identified as two close lying objects with different speeds. This paper serves as a proof-of-concept with results from a realistic simulation environment using an imaging sonar sensor and shows that the proposed method is perfectly suitable for making accurate estimations of an object&#x2019;s radial velocity.

Radial Velocity Estimation for Multiple Objects Using In-Air Sonar With MIMO Virtual Arrays