Saurav R Tuladhar

Saurav R Tuladhar, John R Buck

The sample matrix inversion (SMI) beamformer implements Capon's minimum variance distortionless (MVDR) beamforming using the sample covariance matrix (SCM). In a snapshot limited environment, the SCM is poorly conditioned resulting in a suboptimal performance from the SMI beamformer. Imposing structural constraints on the SCM estimate to satisfy known theoretical properties of the ensemble MVDR beamformer mitigates the impact of limited snapshots on the SMI beamformer performance. Toeplitz rectification and bounding the norm of weight vector are common approaches for such constrains. This paper proposes the unit circle rectification technique which constraints the SMI beamformer to satisfy a property of the ensemble MVDR beamformer: for narrowband planewave beamforming on a uniform linear array, the zeros of the MVDR weight array polynomial must fall on the unit circle. Numerical simulations show that the resulting unit circle MVDR (UC MVDR) beamformer frequently improves the suppression of both discrete interferers and white background noise compared to the classic SMI beamformer. Moreover, the UC MVDR beamformer is shown to suppress discrete interferers better than the MVDR beamformer diagonally loaded to maximize the SINR.

Saurav R Tuladhar, Peter Khomchuk, Siva Sivananthan

Crowding on train cars is a common problem plaguing the major public transit agencies around the world. On one hand a crowded train car presents a negative experience for the passengers, while on the other hand it indicated inefficiencies in the train system. The Federal Transit Agency is interested in reducing the crowding level on public transit train cars. Automatic passenger counters (APC) are commonly used to count the passengers boarding and alighting the train cars. Advanced APC solutions are available based on EO/IR sensors and visual object detection technology, but are considerably expensive for large scale deployment. This report discusses a low-cost approach to APC by using accelerometer measurements from train car to estimate approximate passenger loading. Accelerometer sensor can measure train car vibration as the train moves along the rail tracks. The train car vibration changes with the passenger loading on the car. Detecting this change in vibration pattern with changing passenger loading level is key to the accelerometer based APC solution. Moreover, accelerometer sensors present a low-cost APC solution compared to existing EO/IR based APCs. This work presents a (i) theoretical model analysis (ii) experimental data driven approach to demonstrate the feasibility of using accelerometer for passenger loading estimation.

Peter Khomchuk, Saurav R Tuladhar, Siva Sivananthan

Crowding in train cars is increasingly a major concern for transit agencies. From the perspective of the passengers and the transit agencies, overcrowding of the train cars has several negative consequences such as: (i) extended duration of passengers boarding and alighting which leads to longer dwell times, (ii) subsequent disruption of the headway and the schedule, and (iii) passenger dissatisfaction (e.g. increased stress and lack of privacy). Moreover, overcrowding during peak service hours also indicates inadequate infrastructure to meet the passenger demands. Realizing the importance of the crowding issue, transit agencies have developed measures to assess the crowding levels. The Transit Capacity and Quality of Service Manual provides guidelines on thresholds for crowding in transit systems in the United States.