分布式雷达网络：目标航迹协同启动算法提升隐形雷达性能

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本文主要探讨了"基于目标航迹信息的分布式雷达网络协同航迹启动"这一关键主题，针对分布式雷达网络在提高目标定位精度方面的重要潜力。通常情况下，当多个雷达站点能够对同一目标进行有效跟踪时，分布式雷达网络可以显著提升定位性能。然而，由于不同的雷达站点可能对同一目标的信号噪声比（SNR）存在差异，这导致它们难以同时跟踪同一目标，尤其是在那些尚未成功启动目标跟踪的"隐形"雷达站。为解决这个问题，作者提出了一种合作式跟踪启动算法。该算法的核心思想是利用已成功跟踪目标的雷达站点提供的目标航迹信息，来优化那些未能独立启动跟踪的雷达站的性能。通过这种方式，即使在SNR不均衡的条件下，也能协同工作，确保整体系统性能的提升。该研究论文关注于算法设计和实施，可能包括以下几个关键知识点： 1. **目标航迹信息共享**：算法强调了不同雷达站点之间的数据共享，特别是关于目标位置、速度和其他相关参数的信息，这是实现协同跟踪的基础。 2. **SNR适应性**：算法会考虑到雷达站点间的SNR差异，通过智能处理来调整跟踪策略，确保即使在低信噪比环境下也能进行有效的启动。 3. **目标初始化策略**：隐形雷达站如何利用接收到的目标信息进行初始化，可能是算法中的关键步骤，需要保证准确性和鲁棒性。 4. **算法性能评估**：文中可能包含对算法性能的定量评估，如跟踪精度、响应时间、误报率等指标，以及与传统方法的比较分析。 5. **复杂度分析**：考虑到实际应用的可行性，文章可能会讨论算法的计算复杂性和资源消耗，以及如何在分布式网络中高效实施。 6. **实际应用前景**：最后，作者可能对未来如何将这种协同跟踪启动技术应用于实时的空域监控、军事侦察或其他需要高精度跟踪的场景进行了展望。这篇研究论文针对分布式雷达网络的挑战，提出了一种创新的解决方案，旨在通过目标航迹信息共享和策略优化，增强整个系统的跟踪能力，特别是在那些初始跟踪性能受限的雷达站上。通过深入理解和应用这些研究成果，雷达网络的性能优化和可靠性将得到显著提升。

Cooperative track initiation for distributed

radar network based on target track

information

ISSN 1751-8784

Received on 23rd January 2015

Revised on 30th September 2015

Accepted on 12th October 2015

doi: 10.1049/iet-rsn.2015.0312

www.ietdl.org

Hongwei Liu, Hongliang Liu, Xiaodong Dan, Shenghua Zhou

✉

, Jun Liu

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, Shannxi, People’s Republic of China

✉ E-mail: shzhou@mail.xidian.edu.cn

Abstract: Distributed radar network can significantly improve target localisation accuracy on condition that targets can be

successfully tracked by all radar sites. However, since different radar sites often have different signal-to-noise ratios with

respect to even the same target, it is difficult for all radar sites to track a target simultaneously. In this case, the authors

propose a cooperative track initiation algorithm for distributed radar network. It aims to improve track initiation

performance of invisible radar sites that have not initiated the target track, by using target track information provided

by the radar sites that have tracke d the target. Based on the target track information, the invisible radar sites would

predict a region where the target will be present, and decrease detection thresholds in the predicted region according

to a given probability of false track initiation. Numerical results indicate that the proposed algorithm ca n significan tly

improve the probability of target track initiation.

1 Introduction

Distributed radar network [1–3] is a hot topic in the radar ﬁeld since

it owns many advantages over a single radar, such as improved

detection, tracking and localisation performance [4, 5]. It is known

that a higher localisation accuracy relies on the precondition that

all the relevant radar sites have successfully tracked the target. In

real applications, since different radar sites often have different

signal-to-noise ratios (SNRs), they often do not initiate the target

track simultaneously. Hence an interesting problem is, how to

improve track initiation performance of the invisible radar sites

with the aid of the radar sites that have tracked the target.

For traditional monostatic radar systems, track initiation is

generally performed without any assistant information, and there

are mainly two strategies [6]: the sequential processing strategy

and the batch processing strategy. For the former, such as the

rule-based method and the logic-based method [7], the received

data of each scan (frame) are processed one by one until a target

track is established. The sequential processing strategy needs a low

computation cost, but requires some rough information (maximum

velocity) about target motion. For the latter, such as the Hough

transform method [8] and the modiﬁed Hough transform method

[9, 10], the received data of some scans are processed together to

establish a target track. The batch processing strategy works well

in the environment of dense clutter, but suffers from a heavy

computation burden. All these track initiation strategies are

referred to as non-cooperative mode, since they are designed

without a prior knowledge of target motion status. In distributed

radar network, however, one radar site can receive target track

information from other radar sites that have tracked the target. The

motivation of this work is, how to use the fed-in target track

information to improve detection performance and then track

initiation performance of the invisible radar sites.

There are lots of works concerning how to use target track

information for target detection. A typical joint detection and

tracking scheme is the track-before-detection (TBD) strategy [11],

which can improve SNR through accumulation along successive

scans, but the high computation cost hinders its real application.

Another scheme is to adjust detection thresholds in target’s

predicted region. A line of research [12–15] concentrates on

optimising spatially identical detection thresholds in the predicted

region to maximise tracking performance. The essential of the

threshold optimisation strategy is to decrease detection thresholds

for a higher detection probability and hence improve tracking

performance. Once applied to target track initiation, they do not

have a mechanism to control the probability of false track

initiation, and may trigger many false tracks messing up with real

targets. Distinct from this strategy, in [16], spatially varying

detection thresholds in the predicted region are presented under the

Bayesian criterion [17], called a Bayesian detector. The Bayesian

detector works well for non-manoeuvring targets, but for

manoeuvring targets, the real target location may deviate from the

predicted centre, which may degrade detection performance.

Moreover, the spatially varying detection thresholds are given for

all continuous locations in the predicted region, but in radar

applications, one resolution cell requires one detection threshold,

rather than the location-continuous detection thresholds. Besides,

the detection thresholds of the Bayesian detector are dependent on

a so-called tunable parameter, but no strategy is given to set the

tunable parameter. Hence the Bayesian detector cannot control the

probability of false track initiation, either.

To improve track initiation performance at an acceptable false

track level, in [18], we study how to set detection thresholds in the

predicted region under a constant probability of false track

initiation, and mainly discussed spatially identical detection

thresholds (a uniform method) for non-manoeuvring targets. In

this paper, we expand the previous work to manoeuvring

situations, and consider spatially varying detection thresholds (a

non-uniform method) in the predicted region. Explicitly, given a

probability of false track initiation, we ﬁrst calculate a so-called

frame false alarm rate, that is, the probability that there exists at

least one false alarm in the predicted region. Under the frame false

alarm rate, detection thresholds of resolution cells in the predicted

region are calculated according to the probability of target being

present in each resolution cell. Furthermore, we evaluate track

initiation performance of the uniform method and non-uniform

method, for both non-manoeuvring targets and manoeuvring

targets. Numerical results indicate that both methods can improve

the track initiation performance compared with traditional track

initiation algorithm. Moreover, the uniform method has a better

performance for manoeuvring targets, while the non-uniform

method performs better for non-manoeuvring targets.

The rest of the paper is organised as follows. Section 2 introduces

the mathematical model for cooperative track initiation problem. In

IET Radar, Sonar & Navigation

Research Article

IET Radar Sonar Navig., 2016, Vol. 10, Iss. 4, pp. 735–741

735

The Institution of Engineering and Technology 2016

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