This paper focuses on the multi-target tracking using the Stone Soup framework. In particular, we aim at evaluation of two multi-target tracking scenarios based on the simulated class-B dataset and ADS-B class-A dataset provided by OpenSky Network. The scenarios are evaluated w.r.t. selection of a local state estimator using a range of the Stone Soup metrics. Source code with scenario definitions and Stone Soup set-up are provided along with the paper.

This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.

In this paper, to reduce the time and manpower spent on fine-tuning an extended Kalman filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the best process and measurement noise covariance pair from the real measurement data. The EKFNet is trained by using backpropagation through time (BPTT). The proposed method can choose among several optimization criteria, such as maximizing the likelihood, minimizing the measurement residual error, or minimizing the posterior state estimation error. We illustrate the proposed method's performance using real GPS data, which outperforms existing methods and a manually tuned EKF.

Most visual object tracking (VOT) algorithms treat the object as a single point in the output score map, and the bounding box is estimated by a multi-scale search. Further, most of them are based on the concept of tracking-by-detection, which is focused on the detection step and ignores the tracking step and object’s dynamics. In this paper, we address these limitations by developing a new VOT framework. Instead of a point object, we mathematically model the shape of the extended visual object as an ellipse. We allow multiple detections in the score map, and derive an elliptical gating method to discard possible clutters. We apply a sophisticated extended target tracking algorithm to track the object’s kinematic state and shape simultaneously. Experiment results are provided to show that the proposed algorithm outperforms several state-of-the-art methods.

The International Society of Information Fusion (ISIF) Stone Soup project seeks to bring together advances in target tracking through an open-source repository of software libraries. Additionally, the ISIF uncertainty reasoning working group provides an open-source ontology. This paper seeks to demonstrate the correspondence between the open source tracking repository and the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. For example, many target tracking challenge problems propose a scenario for data fusion techniques to solve, from which various performance metrics are considered for evaluation. The Stone Soup framework has the MetricGenerator class and the URREF has the accuracy class. The example presented in the paper utilizes the cubature Kalman filter to determine the impact of corrupted measurements on the track accuracy as an instance of the Stone Soup and URREF metrics.

In this paper, we investigate the problem of source location estimation in wireless sensor networks (WSNs) based on quantized data in the presence of false information attacks. Using a Gaussian mixture to model the possible attacks, we develop a maximum likelihood estimator (MLE) to locate the source with sensor data corrupted by injected false information, and call the approach quantized received signal strength with a Gaussian mixture model (Q-RSS-GM). The Cramer-Rae lower bound (CRLB) for this estimation problem is also derived to evaluate the estimator's performance. Simulation results show that the proposed estimator is robust in various cases with different attack probabilities and parameter mismatch, and it significantly outperforms the approach that ignores the possible false information attacks.

In this poster, an accurate distance ranging system for off-the-shelf smartphones is introduced. Two ranging methods, namely improved Microsoft Beep-Beep and our Single-Beep, are developed and evaluated on 6 different Android phones.

In this paper, an overview of recent developments in received-signal-strength (RSS)-based localization in wireless sensor networks is presented. Several important practical issues and their solutions are discussed. To save communication bandwidth and sensor energy, a maximum-likelihood estimator based on quantized data is presented along with its corresponding Cramér-Rao lower bound (CRLB) and optimal quantizer design schemes. For further system resource savings, an iterative sensor selection approach is presented to activate only the most informative sensors, by maximizing the mutual information or minimizing the posterior CRLB at each iteration. For a resource constrained WSN with imperfect wireless channels, channel-aware target localization is described, where the channel model is incorporated into the localization scheme itself, thereby improving performance without increasing communication overhead. Another practical issue involving the presence of malicious sensors called Byzantines is discussed and mitigation schemes are provided. A recent coding-theorybased approach which is both computationally inexpensive and robust to such malicious attacks is also discussed.

In this paper, sparsity-promoting sensor selection algorithms for target tracking with quantized data are developed. We formulate sensor selection as an optimization problem that aims to strike a balance between estimation accuracy and the number of selected sensors. To cope with sensor selection problems in large-scale wireless sensor networks (WSNs), we propose a fast centralized optimization algorithm based on the alternating direction method of multipliers (ADMM). We further develop a low-complexity distributed version of the ADMM where each sensor makes a local sensor selection decision. The simulation results show that the proposed centralized and distributed algorithms activate the most informative sensors and yield very good tradeoff between the estimation performance and the cost of sensing and communication. For large scale sensor networks, the distributed ADMM algorithm is more efficient and has lower computational load per sensor node.

In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. In the proposed framework, a new state evolution formulation takes the quantization error into account, and analytically determines the coding rate required in each iteration. Two approaches for allocating the coding rate, an online back-tracking heuristic and an optimal allocation scheme based on dynamic programming, provide significant reductions in communication costs.

In this paper, from the adversary's point of view, the optimal strategy to attack a multi-sensor dynamic system is investigated. It is assumed that the system can perfectly detect and remove sensors once they are corrupted by false information injected by an adversary. The adversary is trying to maximize the covariance matrix of the system state estimate by the end of attack period under the constraint that the adversary can only attack the system a few times over time and over sensors, which leads to an integer programming problem. The exhaustive search algorithm has a prohibitive complexity and greedy algorithms are proposed to find the attack strategies. Examples and numerical results are provided in order to illustrate the effectiveness of the proposed attack strategies.

In this paper, we suggest a modified distributed compressed sensing (CS) approach based on the iterative hard thresholding (IHT) algorithm, namely, distributed IHT (DIHT). Our technique improves upon a recently proposed DIHT algorithm in two ways. First, for sensing matrices with i.i.d. Gaussian entries, we suggest an efficient and tight method for computing the step size μ in IHT based on random matrix theory. Second, we improve upon the global computation (GC) step of DIHT by adapting this step to allow for complex data, and reducing the communication cost. The new GC operation involves solving a Top-K problem and is therefore referred to as GC.K. The GC.K-based DIHT has exactly the same recovery results as the centralized IHT given the same step size μ. Numerical results show that our approach significantly outperforms the modified thresholding algorithm (MTA), another GC algorithm for DIHT proposed in previous work. Our simulations also verify that the proposed method of computing μ renders the performance of DIHT close to the oracle-aided approach with a given “optimal” μ.

In this paper, we consider a Bayesian estimation problem in a sensor network where the local sensor observations are quantized before their transmission to the fusion center (FC). Inspired by Widrow's statistical theory on quantization, at the FC, instead of fusing the quantized data directly, we propose to fuse the post-processed data obtained by adding independent controlled noise to the received quantized data. The injected noise acts like a low-pass filter in the characteristic function (CF) domain such that the output is an approximation of the original raw observation. The optimal minimum mean squared error (MMSE) estimator and the posterior Cramér-Rao lower bound for this estimation problem are derived. Based on the Fisher information, the optimal controlled Gaussian noise and the optimal bit allocation are obtained. In addition, a near-optimal linear MMSE estimator is derived to reduce the computational complexity significantly.

We propose a modified Bayesian Cramér-Rao lower bound (BCRLB) for nonlinear tracking applications where the prediction distribution conditioned on past measurements is used as the prior. The novelty of the proposed modified BCRLB comes from the fact that it utilizes past measurements, therefore it is specific to the current realization of the track which makes it a useful online tool that can be used for real-time sensor management. The computation of our proposed modified BCRLB is not analytically tractable except under very restricted conditions. Therefore, we also develop a particle based numerical computation method for our modified BCRLB so that this new bound can be easily calculated in real-time using the particles already available from the underlying particle filter which is used to track the target. We show by simulations that our developed numerical computation method approaches to its true analytical value as the number of particles in the particle filter increases.

The problem of dynamic bit allocation for target tracking is investigated in this paper under a total sum rate constraint in sensor networks. Bits are dynamically allocated to sensors in such a way that a cost function, which is based on the Cramér-Rao lower bound evaluated at the predicted target state, is minimized. The optimal solution to this problem, namely joint bit allocation and local quantizer design, is computationally prohibitive and not realistic for real-time online implementation. Instead, a two-step optimization procedure is proposed. First, the best time independent quantizers are obtained offline by maximizing the average Fisher information about the signal amplitude, for different number of bits. With the time independent quantizers, the generalized Breiman, Friedman, Olshen, and Stone (BFOS) algorithm is employed to dynamically assign bits to sensors. Simulation results show that with the same or even less sum bit rate, the proposed dynamic bit allocation approach leads to significantly improved tracking performance, compared with the static bit allocation approach where each sensor is allocated with equal number of bits.

We study the parametric distributed estimation problem using a wireless sensor network (WSN) where each sensor observes an unknown scalar parameter, quantizes its observation and sends its quantized observation to a fusion center via fading and noisy communication channels. We propose to incorporate channel statistics rather than the instantaneous channel state information (CSI) into the maximum likelihood (ML) formulation and show that the resulting likelihood function is strictly log-concave almost surely with a change of variable provided that at least one of the communication channels between the sensors and the fusion center has nonzero capacity. We also investigate the effects of channel layer on the sensor threshold design and show that the threshold design problem is coupled with the channel layer and the sensor signal-to-noise ratio (SNR) only for nonsymmetric channels. Our formulation is very general in the sense that no assumptions are made about the physical layer in terms of the modulation schemes and the reception techniques.

This paper extends our earlier work on sensor selection [1]. We are now focusing on a more challenging problem of how to effectively utilize quantized sensor data for target tracking in sensor networks by considering sensor selection problems with quantized data. A subset of sensors are dynamically selected to optimize the tracking performance. The one-step- look-ahead posterior Cramer-Rao Lower Bound (CRLB) on the state estimation error is proposed as the sensor selection criterion. Particle filtering method is employed to compute the posterior CRLB, as well as to estimate the target state. Simulation results show that the proposed posterior CRLB based method outperforms the one based on information theoretic measures.

In this paper, we propose a new maximumlikelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach “channel-aware target localization”. Furthermore, we derive the Cramer-Rao lower bound as a performance bound for our channel-aware ML estimator. Simulation results are presented to show that the performance of the channel-aware ML location estimator is quite close to its theoretical performance bound even with relatively small number of sensors and it has superior performance compared to that of the channel-unaware ML estimator.

This paper presents a new framework for distributed target detection in wireless sensor networks (WSNs). In our previous work, for multiple networked sensors collaboratively detecting the presence or absence of a target in the sensor field, every sensor uses an identical threshold for local decision-making. In this paper, we propose a framework where the sensors in the network collaboratively decide and select non-identical thresholds to improve network-wide detection performance in a dynamic manner. This threshold selection scheme is based on a new statistical metric called False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like system level probability of detection and probability of false alarm are studied. Analytical and simulation results are provided for system level probability of false alarm and probability of detection. Performance comparison between the proposed approach and the classical identical local sensor threshold approach is provided to demonstrate the effectiveness of this scheme.

Source localization is investigated for a sensor network with passive sensors. The signal emitted by the source endures Rayleigh fading during its propagation, and its average intensity is a function of the distance from the source. Maximum likelihood (ML) source location estimators that use the output, or its quantized version, of the non-coherent receiver is proposed. The ML estimators' Cramer-Rao lower bounds (CRLBs) are derived. Due to the fading effect, the proposed estimator's performance is degraded, compared to the ideal case without fading. However, it can still accurately estimate the source's position and intensity, and achieve its CRLB with relatively small amount of resources, namely small number of observations, sensors and quantization bits.

The objective in sensor collaboration for target tracking is to dynamically select a subset of sensors over time to optimize tracking performance in terms of mean square error (MSE). In this paper, we apply the Monte Carlo method to compute the expected posterior Cramer-Rao lower bound (CRLB) in a nonlinear, possibly non-Gaussian, dynamic system. The joint recursive one-step-ahead CRLB on the state vector is introduced as the criterion for sensor selection. The proposed approach is validated by simulation results. In the experiments, a particle filter is used to track a single target moving according to a white noise acceleration model through a two-dimensional field where bearing-only sensors are randomly distributed. Simulation results demonstrate the improved tracking performance of the proposed method compared to other existing methods in terms of tracking accuracy.

Efficient localization of a ballistic missile is an important task in missile defense problems. This paper formulates and solves the sensor placement problem for efficient estimation of the missile location. The first part of this paper develops a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the Fisher information matrix to facilitate the evaluation of estimation accuracy. The second part of the paper presents an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.

In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

For a wireless sensor network (WSN) with a random number of sensors, a decision fusion rule that uses the total number of detections reported by local sensors for hypothesis testing, is proposed. It is assumed that the number of sensors follows a Poisson distribution and the locations of sensors follow a uniform distribution within the region of interest (ROI). Both analytical and simulation results for the system level detection performance are provided. This fusion rule can achieve a very good system level detection performance even at very low signal to noise ratio (SNR), if the average number of sensors is sufficiently large. In addition, the problem of choosing an optimum local sensor level threshold is investigated for various system parameters.

In this paper, four sampling schemes for sequential detection in colored noise are introduced. Two of them use uniform sampling procedures with high and low sampling rates, respectively. The other two employ groups of samples, which are separated by long intergroup gaps such that the intergroup correlations are negligible. Their performance, in terms of average termination time, is derived analytically. Under the assumption that all the schemes have the same power and sampling interval (x), their efficiencies are compared through analytical and numerical methods. Our results show that the scheme using group sampling with an optimal signal is the most efficient.

Change detection is known to be a significant and difficult research problem in automated surveillance systems. In this paper, we propose a new change detection approach based on the least squares method, which is robust to changes in illumination and shadow conditions. This new approach is employed to design our detection and tracking system that is shown to successfully detect a moving object in a complex outdoor environment.

An outdoor multi-camera video surveillance system operating under changing weather conditions is presented. A new confidence measure, appearance ratio (AR), is defined to evaluate automatically the sensors' performance for each time instant. By comparing their ARs, the system can select the most appropriate cameras to perform specific tasks. When redundant measurements are available for a target, the AR measures are used to perform a weighted fusion of them. Experimental results are presented on outdoor scenes under different weather conditions.