Determining human target facing orientation using bistatic radar micro-Doppler signals

Abstract

Micro-Doppler radar signals can be used to separate moving human targets from stationary clutter and also to identify and classify human movements. Traditional micro-Doppler radar systems which use a single sensor, monostatic system, suffer from the drawback that only the radial component of the micro-Doppler signal will be observed by the radar operator. This reduces the sensitivity of human activity recognition if the movements are not directly towards or away with respect to the line-of-sight to the radar antenna. In this paper, we propose the use of two bistatic micro-Doppler sensors to overcome this limitation. By using multiple sensors, the orientation of oscillating targets with respect to the radar line-of-sight can be inferred, thereby providing additional information to the radar operator. This approach can be used to infer the facing direction of the human with respect to the radar beam.

Performance Analysis of Centroid and SVD Features for Personnel Recognition Using Multistatic Micro-Doppler

“…If this angle has limited values up to approximately 30°, the classification performance appears not to present excessive degradation [2], but for higher angles up to the case limit of 90° the classification is severely hindered [7]. Bistatic and multistatic radar has been suggested as a possible mitigation to this problem [8][9][10], as the deployment of multiple nodes can provide multiperspective views on targets, where at least one node can illuminate the target at a favorable aspect angle.…”

Multistatic human micro‐Doppler classification of armed/unarmed personnel

“…In our work we investigate and try to quantify how the information collected using a multistatic radar with 3 receivers can improve classification performance. As pointed out in [14], it is expected that a bistatic/multistatic system allows the problem of micro-Doppler signature not visible to a monostatic sensor at particular aspect angles to be overcome. Other approaches to analyse micro-Doppler signatures use alternative time-frequency techniques such as Pseudo Wigner-Ville Distribution and B-Distribution [15], principal component and independent component analysis [16], empirical mode decomposition [17] and the Hilbert-Huang transform [18], which may be more suitable to process non stationary signals.…”

“…The importance of the micro-Doppler contribution of the arms for classification purposes suggested by these papers has been taken into consideration in this work when extracting suitable features, but further analysis has been performed to exploit Radar Cross Section (RCS) related features and above all the use of multistatic operation to improve classification performance. The use of two bistatic sensors with reference to human activity classification has been reported in [14], with one receiver co-located with the transmitter and another physically separated. The successful identification of the motion direction for a series of human movements such as swinging arms or picking up an object is achieved with this system.…”

Aspect angle dependence and multistatic data fusion for micro‐Doppler classification of armed/unarmed personnel

“…Other approaches of combining multistatic data are only briefly mentioned, such as classification and decision based only on data from the most accurate node, or a weighted combination of data from different nodes ranked on the base of the mutual information parameter. A bistatic radar system with two receivers, one co-located with the transmitter and one physically separated, has been reported in [16]. This system is capable of inferring the orientation of oscillating mechanical targets such as a pendulum, as well as the facing direction of human beings performing different activities such as swinging arms and picking up objects.…”