An improved multiple model particle filtering approach for manoeuvring target tracking using Airborne GMTI with geographic information

This paper proposes a novel ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle’s movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle’s behaviour within different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm aided by particle swarm optimisation is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique for the particle filter which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking

An enhanced particle filtering method for GMTI radar tracking

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e. falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise related doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem

Multi-camera real-time three-dimensional tracking of multiple flying animals

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time—with minimal latency—opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behaviour of freely flying animals. If combined with other techniques, such as ‘virtual reality’-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Tracking sperm whales using passive acoustics and particle filters

Passive acoustics provides a powerful tool for marine mammal research and mitigation of the risk posed by high energy anthropogenic acoustic activities through monitoring animal positions. Animal vocalisations can be detected and utilised in poor visibility conditions and while animals are dived. Marine mammal research is often conducted on restricted financial budgets by non-government organisations and academic institutions from boats or ships towing hydrophone arrays often comprising only two elements. The arrival time-delay of the acoustic wavefront from the vocalising animals across the array aperture is computed, often using freely available software, and typically regarded as the bearing of the animal to the array. This methodology is limited as it provides no ranging information and, until a boat manoeuvre is performed, whether the animal is to the left or right of the array remains ambiguous. Methods of determining range that have been suggested either negate the fact the animal is moving, rely on robust detection of acoustic reflections, rely on accurate equipment calibration and knowledge of the animal’s orientation or require modification of hydrophone equipment. There is a clear need to develop an improved method of estimating animal position as relative bearing, range and elevation to a hydrophone array or boat based on time-delay measurements. To avoid the costs of upgrading hydrophone arrays, and potentially the size of the vessels required to tow them, a software solution is desirable. This thesis proposes that the source location be modelled as a probability density function and that the source location is estimated as the mean. This is developed into a practical method using particle filters to track sperm whales. Sperm whales are the ideal subject species for this kind of development because the high sound pressure levels of their impulsive vocalisations (up to 236 dB re 1 ?Pa) makes them relatively simple to detect. Simulation tracking results demonstrate particle filters are capable of tracking a manoeuvring target using time-delay measurements. Tracking results for real data are presented and compared to the pseudotrack reconstructed from a tag equipped with accelerometers, magnetometers, a depth sensor and an acoustic recorder placed on the subject animal. For the majority of datasets the animal is tracked to a position relatively close to the surface sighting position. Sperm whales are typically encountered in groups, therefore a viable tracking solution needs to be capable of tracking multiple animals. A multiple hypothesis tracking method is proposed and tested for associating received vocalisations with animals, whereby vocalisations are correctly associated for periods exceeding 15 minute