Effectiveness of track monitoring with multiple 2D passive sensors

An analytical technique is presented which can be used to evaluate the effectiveness of the track monitoring scheme presented by J.A. Roecker (see ibid., vol.27, no.6, p.872-6, Nov. 1991) in detecting false track-to-track (ghost track) assignments without the use of simulations. It is shown that when two tracks are traveling in parallel and in the direction of the inclination angle, it is impossible to determine which tracks to associate. However, track monitoring is effective under all other circumstances. A tracking algorithm designer can use this technique to determine the length of time before track-to-track initiation for good ghost track rejection. This technique also determines the probability of accepting ghost tracks during mature track monitoring.<>     

Tracking a 3D maneuvering target with passive sensors

A novel application of the interacting multiple models (IMM) algorithm in which passive infrared sensors are fused for tracking a target maneuvering in three dimensions is discussed. More accurate models of target motion are proposed to improve performance. When the general models are used to describe the maneuvering periods, it is shown that the IMM behaviour is not satisfactory, in that the innovations associated with the different models do not discriminate between the corresponding target maneuvering regimes. The turning of the Markov chain transition matrix, i.e., a priori information, is then crucial to obtaining the correct ordering of the a posteriori regime probabilities. On the contrary, a more satisfactory behavior of the IMM algorithm is obtained by carefully selecting the target motion models in the different regimes     

Fusion of sensors with dissimilar measurement/tracking accuracies

The case of data fusion of sensors dissimilar in their measurement/tracking errors is considered. It is shown that the fused track performance is similar whether the sensor data are fused at the track level or at the measurement level. The case of a cluster of targets, resolved by one sensor but not the other, is also considered. Under certain conditions the fused track may perform worse than the worst of the sensors. A remedy to this problem through modifications of the association algorithm is presented     

Optimal multiple level decision fusion with distributed sensors

A data fusion model consisting of several levels of parallel decision fusions is considered. Global optimization of such a model is discussed to obtain the fusion rules for overall optimal performance. The reliability analysis of the proposed model is carried out to establish its superiority over the existing parallel and serial fusion models     

Interacting multiple model tracking with target amplitude feature

A recursive tracking algorithm is presented which uses the strength of target returns to improve track formation performance and track maintenance through target maneuvers in a cluttered environment. This technique combines the interacting multiple model (IMM) approach with a generalized probabilistic data association (PDA), which uses the measured return amplitude in conjunction with probabilistic models for the target and clutter returns. Key tracking decisions can be made automatically by assessing the probabilities of target models to provide rapid and accurate decisions for both true track acceptance and false track dismissal in track formation. It also provides the ability to accurately continue tracking through coordinated turn target maneuvers     

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density (PHD) filter has been recognized as a promising tech-nique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking mul-tiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.     

冷却结构塞式喷管红外特性抑制试验技术研究

利用涡扇发动机喷管红外辐射特征模拟试验台,对二元塞式喷管的红外辐射特征进行试验研究,得到塞锥冷却对二元塞式喷管红外辐射特性的影响规律,并与轴对称收扩喷管(基准喷管)进行了对比。试验结果表明:在探测角0°方向上,相比基准喷管,二元塞式喷管在不采取冷却措施时红外特征降低了12.1%,冷气流量为1.0%总流量时红外辐射强度降低了40.8%,冷气流量为2.8%总流量时红外辐射强度降低了51.2%。     

分辨率优于0.1arcsec的二维微小角度传感器的系统设计

设计一种新型二维微小角度传感器,可实现实时二维角度的测量。并采用基于DSP的检测电路进行高速数据采集处理,使系统的测量精度和响应速度满足了当前在线检测的需求,角度测量>±600arcsec,测量分辨率优于0.1arcsec。