Probability Of Target Existence Research Articles

ITS Efficiency Analysis for Multi-Target Tracking in a Clutter Environment

The Integrated Track Splitting (ITS) is a multi-scan algorithm for target tracking in a cluttered environment. The ITS filter models each track as a set of mutually exclusive components, usually in the form of a Gaussian Mixture. The purpose of this research is to determine the limits of the ‘endurance’ of target tracking of the known ITS algorithm by analyzing the impact of target detection probability. The state estimate and the a-posteriori probability of component existence are computed recursively from the target existence probability, which may be used as a track quality measure for false track discrimination (FTD). The target existence probability is also calculated and used for track maintenance and track output. This article investigates the limits of the effectiveness of ITS multi-target tracking using the method of theoretical determination of the dependence of the measurements likelihood ratio on reliable detection and then practical experimental testing. Numerical simulations of the practical application of the proposed model were performed in various probabilities of target detection and dense clutter environments. Additionally, the effectiveness of the proposed algorithm in combination with filters for various types of maneuvers using Interacting Multiple Model ITS (IMMITS) algorithms was comparatively analyzed. The extensive numerical simulation (which assumes both straight and maneuvering targets) has shown which target tracking limits can be performed within different target detection probabilities and clutter densities. The simulations confirmed the derived theoretical limits of the tracking efficiency of the ITS algorithm up to a detection probability of 0.6, and compared to the IMMITS algorithm up to 0.4 in the case of target maneuvers and dense clutter environments.

Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs.

Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.

Modified you-only-look-once model for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment

The you-only-look-once (YOLO) model identifies objects in complex images by framing detection as a regression problem with spatially separated boundaries and class probabilities. Object detection from complex images is somewhat similar to underwater source detection from acoustic data, e.g., time-frequency distributions. Herein, YOLO is modified for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment. The modified you-only-look-once (M-YOLO) input is a frequency-beam domain (FBD) sample containing the target and multi-interfering spectra at different azimuths, generated from the received data of a towed horizontal line array. M-YOLO processes the whole FBD sample using a single-regression neural network and directly outputs the target-existence probability and spectrum azimuth. Model performance is assessed on both simulated and at-sea data. Simulation results reveal the strong robustness of M-YOLO toward different signal-to-noise ratios and mismatched ocean environments. As tested on the data collected in an actual multi-interfering environment, M-YOLO achieved near-100% target detection and a root mean square error of 0.54° in azimuth estimation.

Multiagent coverage search based on Voronoi and sparse heteroscedastic Gaussian process

AbstractThis work is concerned with multiagent cooperative and autonomous coverage search in an unknown environment. A novel Voronoi and sparse heteroscedastic Gaussian process (SHGP)‐based search method is proposed to improve the target search efficiency. By means of heteroscedastic Gaussian process (HGP), each agent can estimate the probability of target existence in given locations online according to the local probability map. Then, these local probability maps are fused together to obtain a posterior probability distribution for target prediction. Based on that, the agents' search paths within Voronoi cells will be adjusted adaptively online to make sure all the agents can search for targets more efficiently. A challenging issue then falls in the computational complexity of the HGP. To solve this problem, we propose an adaptive ranked‐set sampling algorithm based on the weighted sampling process and K‐means clustering algorithm. Theoretical analysis on the discrepancy of the kernel matrix between the original probability points and the sampling points is also given. Simulation results demonstrate that our proposed coverage search method can improve the performance of the area search compared with the existing Voronoi‐based one.

클러터 환경에서 레이더 시스템의 표적 포착 시간 예측 기법

A radar system generally operates a signal and data processing chain, which consists of detection, confirmation, lock-on, and tracking, to seek a target within a region of interest. The performance of target lock-on depends on a confirmation method, and the lock-on time for a target is a crucial design parameter in terms of efficient resource management of a radar system in cluttered environments. This paper presents a target lock-on time prediction method based on the probability of target existence with Markov chain-2 model and its applicability in cluttered environments via computer simulation.

Adaptive Weight Adjustment and Searching Perception Strategy for Multivariate Complex Environments

Affected by the complex environment and the destruction of communication infrastructure in the disaster-stricken area, it has brought great challenges to the search and rescue team. The use of small unmanned aerial vehicles (UAVs) for search tasks can minimize casualties. Therefore, in order to avoid any possible collision and search for unknown targets in the shortest time, it is necessary to design a multi-UAV cooperative target search strategy. In this paper, we analyze the unknown target search problem of multi-UAVs under random dynamic topology and propose an adaptive target search strategy based on the whale algorithm. First of all, each UAV detects the environmental information of its current area and uses the probability map search algorithm to gain the target existence probability map in the task area. Then, the whale optimization search method of shrinking circle or spiral is selected to update the position of the UAV to continuously approach the target. Finally, the obstacle avoidance strategy based on artificial potential field is designed to solve any collision problems that may be encountered during the flight of UAVs. Simulations on multi-UAVs target search in different scenarios show that compared with the whale optimization algorithm, the proposed algorithm can reduce the search time by 43.1% and the total path cost by 18.1%, and it is also superior to the advanced metaheuristic optimization algorithms such as PSO and GWO.

MRPT: Millimeter-Wave Radar-Based Pedestrian Trajectory Tracking for Autonomous Urban Driving

Pedestrians represent agile and low-observable targets, especially under adverse weather conditions, whose trajectory tracking plays a crucial role in determining pedestrian behavior in autonomous urban driving. Thus, this article presents a millimeter-wave radar-based pedestrian trajectory-tracking (MRPT) system that enables all-weather trajectory perception with high precision. More specifically, to improve the tracking performance in the presence of strong background clutters, a track-before-detection-based algorithm is proposed to address pedestrian detection and tracking jointly, instead of regarding them as two separate phases in conventional methods. After that, a continuous detection method based on the integration of target existence probability and the Markov transition matrix is proposed to achieve superior pedestrian detection performance by directly using unthresholded radar data. In addition, based on the binary-phase-multiplex (BPM) strategy, the proposed system is validated using a low-cost automotive frequency-modulated-continuous-waveform (FMCW) multiple-input–multiple-output (MIMO) radar sensor. Consequently, extensive simulation and experimental results demonstrate that MRPT exhibits better pedestrian detection and tracking performance under low signal-noise-ratio (SNR) conditions compared with traditional methods.

Target Search Algorithm for AUV Based on Real-Time Perception Maps in Unknown Environment

For the problem of AUV target searches in unknown underwater environments, a target search algorithm for AUVs based on a real-time perception map is proposed. Real-time perception maps, including target existence probability maps, uncertainty maps, and pheromone maps and their updating rules, are established. Attraction source maps and search status maps based on the environmental information detected by the AUV are established. The maps are used for the AUV to search for corner areas that are unknown to a high degree and areas with low coverage around the current location. At the same time, a release mechanism for attraction and revisiting pheromones is established by combining a neural excitation network algorithm to make the gradient spread in the pheromone grid map. By setting up a search revenue function based on real-time perception maps, an AUV search decision-making method is established. When the AUV finds a suspected target, the AUV approaches the suspected target. The path planning of the AUV is carried out through an improved artificial potential field method. The short-distance confirmation of the target and obstacle avoidance in the search process are realized. The simulation results show that the algorithm has high search efficiency. Additionally, when the target exists in a corner area, the probability of the AUV to quickly search for the target is fast and feasible.

Multisensor distributed out-of-sequence-tracks fusion with track origin uncertainty

The increased trend toward multisensor target tracking system is driving an interest for distributed tracks fusion algorithm. In such a system, local tracks formed by each sensor may arrive in the fusion center with temporal out-of-sequence because of different sensor data processing time and random communication delay, moreover, the priori information on the origins of tracks to be fused is usually unknown due to clutter disturbance and presence of multitargets. This paper considers the problem of fusing out-of-sequence tracks (OOSTs) with track origin uncertainty in a distributed fusion setup, and proposes a novel all neighbor fusion-integrated forward prediction fusion and decorrelation (ANF-IFPFD) method. The proposed ANF-IFPFD enumerates and probabilistically evaluates all feasible track-to-track association events, and fuses the central tracks with extracted information purely contributed by the local OOSTs through an information decorrelation process. Furthermore, it enables to operate the false track discrimination (FTD) in the fusion center by using the fused probability of target existence as a track quality measure. Additionally, two implementation configurations of the proposed ANF-IFPFD method are also designed to provide a trade-off between the tracking performance and the system communication bandwidth consumption. Monte Carlo simulations are carried out to validate the superiority of the proposed ANF-IFPFD over the enhanced state-of-art methods, in terms of both tracking performance and also computation-storage requirement. Compared to the configuration without any information feedback, the partial information feedback configuration of the ANF-IFPFD method is verified to deliver intensive improvements for both local tracking and tracks fusing.

Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments.

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

This paper addresses the problem of distributed multitarget detection and tracking based on the linear arithmetic average (AA) fusion. We first analyze the conservativeness and Frechet mean properties of the AA fusion, presenting new analyses based on a literature review. Second, we propose a target-wise fusion rule for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed MB fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Third, two communicatively and computationally efficient cardinality consensus approaches are presented which merely disseminate and fuse target existence probabilities among local MB filters. Finally, the performance of these four approaches in terms of accuracy, computing efficiency, and communication cost is tested in two simulation scenarios.

Distributed Bernoulli Filtering for Target Detection and Tracking Based on Arithmetic Average Fusion

We present a distributed Bernoulli filter for tracking a target that may be present or absent in the cluttered surveillance area in unknown time intervals by using a decentralized sensor network. As a key feature of the Bernoulli filter, a parameter referring to the target existence probability is online updated jointly with the target state probability density function. We propose to fuse them in parallel, both in an arithmetic average fusion manner via the standard consensus or flooding scheme. Alternatively, one may communicate and fuse merely target existence probabilities, leading to a communication-inexpensive protocol. We experimentally compare the proposed approaches, based on the Gaussian mixture implementation of the Bernoulli filter, with the cutting-edge geometric average fusion approach based on a Doppler shift sensor network. Advantages are observed in computing efficiency and in dealing with local missed detection.

Research on cooperative area search of multiple underwater robots based on the prediction of initial target information

A novel distributed cooperative search strategy for multiple underwater robots is proposed based on the initial target information, so as to reduce the average time of searching target and improve the search target probability in statistical sense. The cooperative search strategy divides the target search process into two phases. In the first search stage, the underwater robot predicts the possible existence range of the target based on the speed and the elapsed time of the target. When the underwater robot is not in the target prediction range, it directly moves towards the initial position of the target to reduce the time of the blind search. In the second search stage, the underwater robot enters the target prediction range, then the target existence probability is updated in real time according to the sensor detection results, and the predictive control thought is used to make optimization decisions according to the target existence probability. Finally, the Monte Carlo simulation experiments are carried out for 1000 times. The proposed search strategy in this paper is compared with the cooperative search strategy with no target initial information. The comparison results show the effectiveness and feasibility of searching a moving target using the proposed search strategy.1

Joint integrated track splitting for multi-path multi-target tracking using OTHR detections

In target tracking applications where an over-the-horizon radar (OTHR) is used to gather measurements, one target may generate more than one measurement at each scan due to the multi-path propagation effect. However, traditional tracking methods obtain the data association probabilities based on track-to-measurement data association events under the assumption that each target can generate at most one detection at each scan, leading to poor performances if these methods are applied to multiple detection applications such as OTHR applications. In this paper, we develop a multi-path multi-target tracking algorithm entitled multiple detection joint integrated track splitting (MD-JITS). This novel algorithm jointly solves the measurement origin uncertainty and measurement path model uncertainty. The probability of target existence (PTE) is utilized in the OTHR application as a track quality measure for true track confirmation and false track discrimination. The data association algorithm of MD-JITS the proposed algorithm is realized based on measurement cells that each measurement cell consists of one or more of the validated measurements, while considering the measurement path model. The proposed algorithm is compared with the multiple detection joint integrated probabilistic data association (MD-JIPDA) algorithm in a multi-target crossing scenario, implementing the OTHR system in the presence of clutter and failed target detections, to demonstrate the desired effectiveness.

Iterative joint integrated probabilistic data association filter for multiple-detection multiple-target tracking

Most classical target tracking algorithms assume that one target generates one measurement per scan. However, for situations where the target's physical size exceeds one sensor resolution cell or the transmission and received signals have multi-path propagations, one target generates multiple detections which terms the multiple-detection problem. The multiple-detection multiple-target tracking algorithms usually suffered from an intractable computational load and could not operate in real time when multiple targets are closely spaced or too many detections are generated. In addition, automatic track initialization technique in cluttered environments brings in the presences of both true and false tracks that false track discrimination is required. These two terms are critical for real tracking systems but largely neglected by the published papers for the multiple-detection problem. In this paper, the authors propose an algorithm, called the Multiple-detection Iterative Joint Integrated Probabilistic Data Association (MD-iJIPDA) filter, that iteratively adjusts the computational load as well the tracking performances so that desirable performances can be achieved within manageable time. By integrating the target existence probability as a track quality measure, the MD-iJIPDA becomes capable of false track discrimination. The MD-iJIPDA is obtained by utilizing the arithmetic structure of MD-JIPDA algorithm. Therefore, we first incorporate the target existence probability into the calculation of joint event probability in MD-JPDA to derive the MD-JIPDA algorithm, then followed by the derivation of MD-iJIPDA algorithm. Simulations for the scenarios of large number targets with multiple detections are investigated to validate the effectivenesses of the proposed algorithm in tracking performance and computational efficiency.

Joint DTC based on FISST and generalised Bayesian risk

This study proposes a recursive solution to target joint detection, tracking, and classification (DTC) based on finite set statistics (FISST) and generalised Bayesian risk. A new Bayesian risk is defined involving the costs of target existence probability estimation (detection), state estimation (tracking), and classification. The estimates and costs are calculated within the FISST framework for different hypotheses and decisions of the target class, and the optimal solution is then derived to minimise the new Bayesian risk. As different costs are unified, the inter-dependence of DTC is considered, and these three sub-problems are solved jointly. The effects of the parameters of the new Bayesian risk are also analysed. Simulations show that the authors’ method has a better overall performance compared with traditional methods.

Multi‐sensor track‐to‐track fusion with target existence in cluttered environments

Multi-sensor fusion for multiple target tracking in cluttered environments is needed for improving tracking accuracy and track maintenance over single sensor target tracking in real target tracking applications. A track quality measure, such as the probability of target existence, can be used when fusing local sensor tracks for false track discrimination at the fusion centre and to improve the performance of the track-to-track association. The track quality measure is a highly effective tool for track maintenance and lowers the computational load by sending tracks with a high probability of target existence to the track centre. In this study, the authors utilise the iterative joint-integrated probabilistic data association technique for multi-sensor distributed fusion systems. Track-to-track fusion performance also depends on distributed fusion architecture. The track-to-track fusion algorithm is evaluated in distributed fusion systems without memory, with memory, and with feedback. The track confirmation rates and position errors are simulated and compared with low-level centralised fusion, which is theoretically optimal.

Integrated particle smoothing for target tracking in clutter

The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (IPS) algorithm and the IP-Rauch–Tung–Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The IPS algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality.In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via “supertargets” using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is performed to verify the superiority of the proposed SP-JIPDA algorithm over the MJIPDA in this multistatic passive radar system.