Robust Direction-of-Arrival Estimation using Array Feedback Beamforming in Low SNR Scenarios

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance parameters of a beamformer, viz. half-power beamwidth (HPBW), side-lobe suppression, and directivity. Through quantitative comparison, we show that our approach outperforms the previous feedback beamforming approach with a single transmit antenna, and the conventional beamformer. We then incorporate a retransmission based minimum variance distortionless response (MVDR) beamformer with the feedback beamforming setup. We propose two approaches, show that one approach is superior in terms of lower estimation error, and use that as the DoA estimation method. We then compare this approach with Multiple Signal Classification (MUSIC), Estimation of Parameters using Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods. The results show that at SNR levels of -60 dB to -10 dB, the angle estiation error of the proposed method is 20 degree less compared to that of prior methods

Multi target direction-of-arrival tracking using road priors

In this paper, we present a multi target particle filter DOA tracker that can incorporate road prior information at a single array node. The filter uses a batch of DON's to determine the state vector, based on an image template matching idea. The filter likelihood is derived with the joint probability density association principles so that no DOA measurement is associated to more than one target. The filter state update has the target DOA, the target velocity over range ratio, and the target heading parameters. We present two approaches for incorporating the road information. In the first approach, the road prior is injected at the weighting stage of the tracker, where a raised mixture Gaussian distribution, derived from the road headings at the target DOA, constraints the particles. The second approach is based on modifying the state update function with a compound model, where a mixture of the constant velocity model and the road information is used. In this case, the filter uses an online EM algorithm to update the state vector along with the mixture components. Computer simulations demonstrate the performance of the approaches

Acoustic multi target tracking using direction-of-arrival batches

In this paper, we propose a particle filter acoustic direction-of-arrival (DOA) tracker to track multiple maneuvering targets using a state space approach. The particle filter determines its state vector using a batch of DOA estimates. The filter likelihood treats the observations as an image, using template models derived from the state update equation, and also incorporates the possibility of missing data as well as spurious DOA observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The particle filter solution is compared with three other methods: the extended Kalman filter, Laplacian filter, and another particle filter that uses the acoustic microphone outputs directly. We discuss the advantages and disadvantages of these methods for our problem. In addition, we also demonstrate an autonomous system for multiple target DOA tracking with automatic target initialization and deletion. The initialization system uses a track-before-detect approach and employs the matching pursuit idea to initialize multiple targets. Computer simulations are presented to show the performances of the algorithms

A range-only multiple target particle filter tracker

We propose a particle filter tracker to track multiple maneuvering targets using a batch of range measurements. The state update is formulated through a locally linear motion model and the observability of the state vector is proved using geometrical arguments. The data likelihood treats the range observations as an image using template models derived from the state update equation, and incorporates the possibility of missing data as well as spurious range observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The filter proposal function uses a Gaussian approximation of the full-posterior to cope with target maneuvers for improved efficiency. By treating the range measurements as images and using smoothness constraints, the particle filter is able to avoid the data association problems. Computer simulations demonstrate the performance of the tracking algorithm

A multi target bearing tracking system using random sampling consensus

In this paper, we present an acoustic direction-of-arrival (DOA) tracking system to track multiple maneuvering targets using a state space approach. The system consists of three blocks: beamformer, random sampling, and particle filter. The beamformer block processes the received acoustic data to output bearing batches as point statistics. The random sampling block determines temporal clustering of the bearings in a batch to determine region-of-interests (ROIs). Based on the track-before-detect approach, each ROI indicates the presence of a possible target. We describe three random sampling algorithms called RANSAC, MSAC, and NAPSAC to use in the random sampling block. The particle filter then tracks the targets via its interactions with the beamformer and the random sampling blocks. We present a computational analysis of the random sampling blocks and show tracking results with field data

Implementation Strategies for Particle Filter based Target Tracking

This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter and is shown to perform better for maneuvering targets. Using an approach similar to the acoustic tracker, a radar range-only tracker is also developed. This includes developing the state update and observation models, and proving observability for a batch of range measurements. From an implementation perspective, this thesis provides new low-power and real-time implementations for particle filters. First, to achieve a very low-power implementation, two mixed-mode implementation strategies that use analog and digital components are developed. The mixed-mode implementations use analog, multiple-input translinear element (MITE) networks to realize nonlinear functions. The power dissipated in the mixed-mode implementation of a particle filter-based, bearings-only tracker is compared to a digital implementation that uses the CORDIC algorithm to realize the nonlinear functions. The mixed-mode method that uses predominantly analog components is shown to provide a factor of twenty improvement in power savings compared to a digital implementation. Next, real-time implementation strategies for the batch-based acoustic DOA tracker are developed. The characteristics of the digital implementation of the tracker are quantified using digital signal processor (DSP) and field-programmable gate array (FPGA) implementations. The FPGA implementation uses a soft-core or hard-core processor to implement the Newton search in the particle proposal stage. A MITE implementation of the nonlinear DOA update function in the tracker is also presented.Ph.D.Committee Chair: McClellan, James; Committee Member: Anderson, David; Committee Member: Davis, Jeffrey; Committee Member: Lanterman, Aaron; Committee Member: Vidakovic, Bran